I recently attended a meeting of great minds at Harvard’s Kennedy School of Government titled, ‘Governing AI?-?How Do We Do It?’. The meeting was full of high-profile individuals ranging from distinguished Harvard faculty, to cyber-security guru, to members of a delegation working with the Danish Minister of Higher Education and Science. As an extremely low-profile software engineer and prospective graduate student, I felt quite intimidated to say the least.

1. Demand That AI Systems Are Transparent
2. Equip AI Systems With an ‘Ethical Black Box’
3. Make AI Serve People and Planet
4. Adopt a Human-In-Command Approach
5. Ensure a Genderless, Unbiased AI
6. Share the Benefits of AI Systems
7. Secure a Just Transition and Ensuring Support for Fundamental Freedoms and Rights
8. Establish Global Governance Mechanisms
9. Ban the Attribution of Responsibility to Robots
10. Ban AI Arms Race

1. Introduce a regulatory approach governing the deployment of AI which mirrors that used for the pharmaceutical sector.
2. Establish an AI regulatory function working alongside the Information Commissioner’s Office and Centre for Data Ethics – to audit algorithms, investigate complaints by individuals, issue notices and fines for breaches of GDPR and equality and human rights law, give wider guidance, spread best practice and ensure algorithms must be fully explained to users and open to public scrutiny.
3. Introduce a new ‘Certificate of Fairness for AI systems’ alongside a ‘kite mark’ type scheme to display it. Criteria to be defined at industry level, similarly to food labelling regulations.
4. Introduce mandatory AIAs (Algorithm Impact Assessments) for organisations employing AI systems that have a significant effect on individuals.
5. Introduce a mandatory requirement for public sector organisations using AI for particular purposes to inform citizens that decisions are made by machines, explain how the decision is reached and what would need to change for individuals to get a different outcome.
6. Introduce a ‘reduced liability’ incentive for companies that have obtained a Certificate of Fairness to foster innovation and competitiveness.
7. To compel companies and other organisations to bring their workforce with them – by publishing the impact of AI on their workforce and offering retraining programmes for employees whose jobs are being automated.
8. Where no redeployment is possible, to compel companies to make a contribution towards a digital skills fund for those employees.
9. To carry out a skills audit to identify the wide range of skills required to embrace the AI revolution.
10. To establish an education and training programme to meet the needs identified by the skills audit, including content on data ethics and social responsibility. As part of that, we recommend the set up of a solid, courageous and rigorous programme to encourage young women and other underrepresented groups into technology.

Ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our ecosystems as well as their ecosystem functions. The relationships between drivers, stress and ecosystem functions in ecosystems are complex, multi- faceted and often non-linear and yet environmental managers, decision makers and politicians need to be able to make rapid decisions that are data-driven and based on short- and long-term monitoring information, complex modeling and analysis approaches. A huge number of long-standing and standardized ecosystem health approaches like the essential variables already exist and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. This presentation therefore discusses the requirements for using Data Science as a bridge between complex and multidimensional Big Data for environmental health. It became apparent that no existing monitoring approach, technique, model or platform is sufficient on its own to monitor, model, forecast or assess vegetation health and its resilience. In order to advance the development of a multi-source ecosystem health monitoring network, we argue that in order to gain a better understanding of ecosystem health in our complex world it would be conducive to implement the concepts of Data Science with the components: (i) digitalization, (ii) standardization with metadata management adhering to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, (iii) Semantic Web, (iv) proof, trust and uncertainties, (v) complex tools for Data Science analysis and (vi) easy tools for scientists, data managers and stakeholders for decision-making support .

Artificial intelligence (AI) has now become a topic of controversy bigger than ever before. Many people are worried about robots taking over the world. The concept of AI scares people because they are afraid of the fact that we are creating bots in which we have no idea how they work. But what if I were to tell you that the majority of statements you have heard about AI are inaccurate? And when I say inaccurate, I don’t just mean a little bit off; the media is VERY wrong when it comes to AI. How can you speak about AI when you don’t even know how it works and have no experience with it? Even worse, most content on AI produced by the media hardly includes evidence from experts in the field of AI. At this point, it’s still understandable if you don’t trust AI; I don’t expect you to agree with me right off the bat. But hopefully, by the end of this article, a new light on AI will be shed for you.

Two leading data scientists offer an up-close and user-friendly look at artificial intelligence: what it is, how it works, where it came from and how to harness its power for a better world. ‘There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.’ Steven D. Levitt, co-author of Freakonomics Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the twenty first century in the same way that the Industrial Revolution remade the world in the nineteenth. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability and better thinking. Along the way, you’ll see how these same ideas are playing out in the modern age of big data and intelligent machines, and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.

Das eigentlich wertneutrale Medium Internet bringt als exponiertestes Instrument des menschlichen Geistes die menschliche Zivilisation schneller, direkter und effektiver voran als alle Erfindungen zuvor. Die Digitalisierung hat Grenzen eingerissen, Gesellschaften transformiert und eine virtuelle Parallelwelt geschaffen, in der alles möglich scheint. Diese neue Form mit Inhalt zu erfüllen, ist Aufgabe menschlicher Kreativität. Das Potential, auch das Zerstörerische zu erkennen (Überwachung, Manipulation, digitalkulturelle Fremdsteuerung), ist Aufgabe eines wachsamen Bewusstseins. Diese neue Dimension der Macht zu kontrollieren, ist Aufgabe der Ethik. Dabei die richtige Balance zwischen Sicherheit und Datenschutz zu finden, ist Aufgabe der Öffentlichkeit und des Individuums.

To improve the experience of consumers, all social media, commerce and entertainment sites deploy Recommendation Systems (RSs) that aim to help users locate interesting content. These RSs are black-boxes – the way a chunk of information is filtered out and served to a user from a large information base is mostly opaque. No one except the parent company generally has access to the entire information required for auditing these systems – neither the details of the algorithm nor the user-item interactions are ever made publicly available for third-party auditors. Hence auditing RSs remains an important challenge, especially with the recent concerns about how RSs are affecting the views of the society at large with new technical jargons like ‘echo chambers’, ‘confirmation biases’, ‘filter bubbles’ etc. in place. Many prior works have evaluated different properties of RSs such as diversity, novelty, etc. However, most of these have focused on evaluating static snapshots of RSs. Today, auditors are not only interested in these static evaluations on a snapshot of the system, but also interested in how these systems are affecting the society in course of time. In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users. We apply the framework to several popular movie RSs to demonstrate its utility.

The Center for Data Innovation is pleased to submit feedback to the High-Level Expert Group (HLEG) on AI on its draft AI Ethics Guidelines for Trustworthy AI. The Center is a nonprofit research institute focused on the intersection of data, technology, and public policy. With staff in Washington, DC and Brussels, the Center formulates and promotes pragmatic public policies designed to maximize the benefits of data-driven innovation in the public and private sectors. It educates policymakers and the public about the opportunities and challenges associated with data, as well as technology trends such as artificial intelligence, open data, and the Internet of Things. The Center is affiliated with the Information Technology and Innovation Foundation (ITIF), the top-ranked science and technology policy think tank in the world.

In this episode of The Dr. Data Show, Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

Excited about using AI to improve your organization’s operations? Curious about the promise of insights and predictions from computer models? I want to warn you about bias and how it can appear in those types of projects, share some illustrative examples, and translate the latest academic research on ‘algorithmic bias.’

AI has been the most intriguing topic of 2018 according to McKinsey. Many people make referrals to AI without actually knowing what it really means. There is public debate on whether it is an evil or savior for humanity. Thus this is yet another attempt to compile & explain the introductory AI/ML concepts to go beyond this buzz for non-practitioners and curious people. Artificial intelligence as an academic discipline was founded in 50s. Actually the ‘AI’ term was coined by John McCarthy, an American computer scientist, back in 1956 at The Dartmouth Conference. According to John McCarthy, AI is ‘The science and engineering of making intelligent machines, especially intelligent computer programs’.

The evil AIs from 2001: A Space Odyssey, Terminator, and beloved children’s movie Wall-E are, without doubt, some of the most terrifying depictions of artificial intelligence in our era. In reality, artificial intelligence is driving some massively positive change in the world; diagnosing illnesses based on conversation, forecasting disease outbreaks, even writing books. However, the thought of an all-powerful sentient AI wiping out all humans is actually a concern for a lot of smart people in the world?-?billionaire innovators like Elon Musk have devoted money and resources to make sure we have friendly artificial intelligence in the future through projects like OpenAI.

On January 28, the Center for Data Innovation and DiploFoundation hosted an event which brought together over 150 people – diplomats, policy makers from EU institutions and member states, researchers, journalists and others – with an interest in the relationship between artificial intelligence (AI), diplomacy, and foreign policy. The event served as a platform to launch and discuss DiploFoundation’s report, Mapping the challenges and opportunities of artificial intelligence for the conduct of diplomacy, which was commissioned by the Finnish Ministry for Foreign Affairs. The launch was followed by three panels of experts, which deepened the discussion on some of the key themes in the report and allowed for further exchange of ideas.

(CIT): Computer science pioneer Alan Curtis Kay, Ph.D., will deliver this year’s Lindberg-King Lecture in the Lister Hill Auditorium. His talk is titled, ‘The Best Way to Predict the Future is to Create It. But Is It Already Too Late?’ A child prodigy, Dr. Kay was an original member of the seminal Xerox-PARC group, and for his myriad innovations in computer science was awarded computer science’s highest honor: the Turing Prize. He has been elected a Fellow of the American Academy of Arts and Sciences, the National Academy of Engineering, and the Royal Society of Arts. He is the president of the Viewpoints Research Institute and an adjunct professor of computer science at the University of California, Los Angeles. The Lindberg-King Lecture honors former NLM Director Donald A.B. Lindberg, M.D., and former NLM Deputy Director of Research, and Education Donald West King, M.D. The event is co-sponsored by the NLM, Friends of the National Library of Medicine, and the American Medical Informatics Association.

Advances in artifcial intelligence (AI) frame opportunities and challenges for user interface design. Principles for human- AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of guidelines for human-AI interaction design.
1. Make clear what the system can do. Help the user understand what the AI system is capable of doing.
2. Make clear how well the system can do what it can do. Help the user understand how often the AI system may make mistakes.
3. Time services based on context. Time when to act or interrupt based on the user’s current task and environment.
4. Show contextually relevant information. Display information relevant to the user’s current task and environment.
5. Match relevant social norms. Ensure the experience is delivered in a way that users would expect, given their social and cultural context.
6. Mitigate social biases. Ensure the AI system’s language and behaviors do not reinforce undesirable and unfair stereotypes and biases.
7. Support effcient invocation. Make it easy to invoke or request the AI system’s services when needed.
8. Support effcient dismissal. Make it easy to dismiss or ignore undesired AI system services.
9. Support effcient correction. Make it easy to edit, refne, or recover when the AI system is wrong.
10. Scope services when in doubt. Engage in disambiguation or gracefully degrade the AI system’s services when uncertain about a user’s goals.
11. Make clear why the system did what it did. Enable the user to access an explanation of why the AI system behaved as it did.
12. Remember recent interactions. Maintain short term memory and allow the user to make effcient references to that memory.
13. Learn from user behavior. Personalize the user’s experience by learning from their actions over time.
14. Update and adapt cautiously. Limit disruptive changes when updating and adapting the AI system’s behaviors.
15. Encourage granular feedback. Enable the user to provide feedback indicating their preferences during regular interaction with the AI system.
16. Convey the consequences of user actions. Immediately update or convey how user actions will impact future behaviors of the AI system.
17. Provide global controls. Allow the user to globally customize what the AI system monitors and how it behaves.
18. Notify users about changes. Inform the user when the AI system adds or updates its capabilities.

The development of AI is creating new opportunities to improve the lives of people around the world, from business to healthcare to education. It is also raising new questions about the best way to build fairness, interpretability, privacy, and security into these systems. These questions are far from solved, and in fact are active areas of research and development. Google is committed to making progress in the responsible development of AI and to sharing knowledge, research, tools, datasets, and other resources with the larger community. Below we share some of our current work and recommended practices. As with all of our research, we will take our latest findings into account, work to incorporate them as appropriate, and adapt as we learn more over time.

The pilots fought continuously until the end of the flight’, said Capt. Nurcahyo Utomo, the head of the investigation of Lion Air Flight 610 that crashed on October 29, 2018, killing the 189 people aboard. The analysis of the black boxes had revealed that the Boeing 737’s nose was repeatedly forced down, apparently by an automatic system receiving incorrect sensor readings. During 10 minutes preceding the tragedy, the pilots tried 24 times to manually pull up the nose of the plane. They struggled against a malfunctioning anti-stall system that they did not know how to disengage for that specific version of the plane. That type of dramatic scene of humans struggling with a stubborn automated system belongs to pop culture. In the famous scene of the 1968 science-fiction film ‘2001: A Space Odyssey’, the astronaut Dave asks HAL (Heuristically programmed ALgorithmic computer) to open a pod bay door on the spacecraft, to which HAL responds repeatedly, ‘I’m sorry, Dave, I’m afraid I can’t do that’.

Last summer the Chinese government released its ambitious New Generation Artificial Intelligence Development Plan (AIDP), which set the eye-catching target of national leadership in a variety of AI fields by 2030. The plan matters not only because of what it says about China’s technological ambitions, but also for its plans to shape AI governance and policy. Part of the plan’s approach is to devote considerable effort to standards-setting processes in AI-driven sectors. This means writing guidelines not only for key technologies and interoperability, but also for the ethical and security issues that arise across an AI-enabled ecosystem, from algorithmic transparency to liability, bias, and privacy. This year Chinese organizations took a major step toward putting these aspirations into action by releasing an in-depth white paper on AI standards in January and hosting a major international AI standards meeting in Beijing in April. These developments mark Beijing’s first stake in the ground as a leader in developing AI policy and in working with international bodies, even as many governments and companies around the world grapple with uncharted territory in writing the rules on AI. China is eager to participate in international standards-setting bodies on the question of whether and how to set standards around controversial aspects of AI, such as algorithmic bias and transparency in algorithmic decision making.

Artificial Intelligence (AI) is poised to disrupt our world. With intelligent machines enabling high-level cognitive processes like thinking, perceiving, learning, problem solving and decision making, coupled with advances in data collection and aggregation, analytics and computer processing power, AI presents opportunities to complement and supplement human intelligence and enrich the way people live and work.
This strategy document is premised on the proposition that India, given its strengths and characteristics, has the potential to position itself among leaders on the global AI map – with a unique brand of #AIforAll. The approach in this paper focuses on how India can leverage the transformative technologies to ensure social and inclusive growth in line with the development philosophy of the government. In addition, India should strive to replicate these solutions in other similarly placed developing countries.

Throughout the world, mobility is becoming increasingly shaped by the digital revolution. The ‘automation’ of private transport operating in the public road environment is taken to mean technological driving aids that relieve the pressure on drivers, assist or even replace them in part or in whole. The partial automation of driving is already standard equipment in new vehicles. Conditionally and highly automated systems which, without human intervention, can autonomously change lanes, brake and steer are available or about to go into mass production. In both Germany and the US, there are test tracks on which conditionally automated vehicles can operate. For local public transport, driverless robot taxis or buses are being developed and trialled. Today, processors are already available or are being developed that are able, by means of appropriate sensors, to detect in real time the traffic situation in the immediate surroundings of a car, determine the car’s own position on appropriate mapping material and dynamically plan and modify the car’s route and adapt it to the traffic conditions. As the ‘perception’ of the vehicle’s surroundings becomes increasingly perfected, there is likely to be an ever better differentiation of road users, obstacles and hazardous situations. This makes it likely that it will be possible to significantly enhance road safety. Indeed, it cannot be ruled out that, at the end of this development, there will be motor vehicles that are inherently safe, in other words will never be involved in an accident under any circumstances. Nevertheless, at the level of what is technologically possible today, and given the realities of heterogeneous and nonconnected road traffic, it will not be possible to prevent accidents completely. This makes it essential that decisions be taken when programming the software of conditionally and highly automated driving systems. The technological developments are forcing government and society to reflect on the emerging changes. The decision that has to be taken is whether the licensing of automated driving systems is ethically justifiable or possibly even imperative. If these systems are licensed – and it is already apparent that this is happening at international level – everything hinges on the conditions in which they are used and the way in which they are designed. At the fundamental level, it all comes down to the following questions. How much dependence on technologically complex systems – which in the future will be based on artificial intelligence, possibly with machine learning capabilities – are we willing to accept in order to achieve, in return, more safety, mobility and convenience? What precautions need to be taken to ensure controllability, transparency and data autonomy? What technological development guidelines are required to ensure that we do not blur the contours of a human society that places individuals, their freedom of development, their physical and intellectual integrity and their entitlement to social respect at the heart of its legal regime?

What in the world is AI policy? First, a definition: AI policy is defined as public policies that maximize the benefits of AI, while minimizing its potential costs and risks. From this perspective, the purpose of AI policy is two-fold. On the one hand, governments should invest in the development and adoption of AI to secure its many benefits for the economy and society. Governments can do this by investing in fundamental and applied research, the development of specialized AI and ‘AI + X’ talent, digital infrastructure and related technologies, and programs to help the private and public sectors adopt and apply new AI technologies. On the other hand, governments need to also respond to the economic and societal challenges brought on by advances in AI. Automation, algorithmic bias, data exploitation, and income inequality are just a few of the many challenges that governments around the world need to develop policy solutions for. These policies include investments into skills development, the creation of new regulations and standards, and targeted efforts to remove bias from AI algorithms and data sets.

We live in a time, where individuals and organizations can store and analyze massive amounts of information; with over 2.5 ­quintillion records of data created every day. People are being defined by what they search for on the internet, what they eat, where they travel to, where they hold membership accounts, etc. Organizations and individuals can leverage this data to uncover powerful findings. Data scientists/analysts often get lost in the techniques and methods of the trade. In doing so, they can forget to ask important questions such as: Who will be affected by the work? How we are ensuring that, by doing ‘good’ for one group, we are not inadvertently harming another? Who actually owns the data that we are working with? What is the protocol for using that data to make business decisions?

Just like the invention of the wheel, of the printing press or of the computer, Artificial Intelligence (AI) will radically reshape the way enterprises work. This is such a revolution, that all industries will be impacted – without any exception: transportation, e-commerce, education, healthcare, energy, insurance, … The standard has seven sections. The full details of each of them are explained on github, but here is a summary:
1. General Information
2. Initial Data
3. Data Preparation
4. Feature Engineering
5. Training Data Audit
6. Model Description
7. Model Audit

This paper discusses the possibility of applying the key principles and tools of current artificial intelligence (AI) to design future human systems in ways that could make them more efficient, fair, responsive, and inclusive.

All revolutions come to an end, whether they succeed or fail. The digital revolution began when stored-program computers broke the distinction between numbers that mean things and numbers that do things. Numbers that do things now rule the world. But who rules over the machines? Once it was simple: programmers wrote the instructions that were supplied to the machines. Since the machines were controlled by these instructions, those who wrote the instructions controlled the machines.

• Organizations will require a professional code of conduct incorporating ethical use of data and AI.
• Legislation will require that 100% of conversational assistant applications, which use speech or text, identify themselves as being nonhuman entities.
• Consumers in mature markets will rely on artificial intelligence (AI) to decide what they eat, what they wear or where they live.
• Organizations will use explainable AI models to build trust with business stakeholders, up from almost no usage today.
• Fortune 1000 antitrust case will hinge on whether tacit cooperation among autonomous AI agents in competitive markets constitutes collusion.
• Large organizations will hire AI behavior forensic, privacy and customer trust specialists to reduce brand and reputation risk.

As organisations and individuals struggle to manage a tidal wave of information and a growing range of devices, Konica Minolta offers a new approach to effective decision-making based on artificial intelligence and the internet of things

The article ‘Cognitive Hub: The Future of Work’ and the supporting infographic (see Figure 1) provides an interesting perspective on some ‘technology combinations’ that could transform the workplace of the future, all enabled by Artificial Intelligence (AI):
• AI + Internet of Things (IoT) yields workplace decision support
• AI + Human-machine Interaction (HMI) yields augmented collaboration
• AI + Cyber physical systems yields digitalization

If you want to tell people the truth, make them laugh, otherwise they’ll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficult to reconstruct or reproduce, results that mis-represent the performance of the system, or contain other biases that limit their validity. In this semi-humorous article, we discuss issues that arise in running and reporting results of machine learning experiments. The purpose of the article is to provide a list of watch out points for researchers to be aware of when developing machine learning models or writing and reviewing machine learning papers.

Today’s cyber physical systems (CPS) are not well protected against cyber attacks. Protected CPS often have holes in their defense, due to the manual nature of today’s cyber security design process. It is necessary to automate or semi-automate the design and implementation of CPS to meet stringent cyber security requirements (CSR), without sacrificing functional performance, timing and cost constraints. Step one is deriving, for each CPS, the CSR that flow from the particular functional design for that CPS. That is the task assumed by our system, Deriving Cyber-security Requirements Yielding Protected Physical Systems – DCRYPPS. DCRYPPS applies Artificial Intelligence (AI) technologies, including planning and model based diagnosis to an important area of cyber security.

Artificial intelligence chatbots are being developed to help diagnose and treat health issues, and could take the place of healthcare providers.

Theories about the ethics of artificial intelligence (AI) are complicated and ever-evolving. As AI expands in 2019, questions will be raised.

Cloudera Chief Architect and Hadoop creator Doug Cutting says that the time has arrived for data privacy and ethics rules.

The US Society of Automotive Engineers (SAE) has defined a scale to describe the autonomous capabilities that self-driving cars have – i.e., their levels of automation.

The following is the opening speech of Arjan van den Born, Academic Director of the Jheronimus Academy of Data Science (JADS), that we worked on for the Den Bosch Data Week of which I am the cofounder and curator.

To unleash automation’s decision-making potential we must examine its limitations

Bias. Discrimination. Inequality. Inequitable. Irresponsible. Unethical. Unfair. How are all these terms connected to Machine Learning (ML) and Artificial Intelligence (AI)? In this post I’ll address three topics that are critical for decision and policy makers- fairness, responsibility and transparency (or explainability) of ML and AI. Our life today is being ruled by algorithms and most of them are a mysterious black box for us. There’s a realization that automated decision making (autonomous systems) will only be acceptable if there is fairness and transparency. A number of media articles, books and research reports by think tanks and books have raised awareness about unfair and unethical AI and algorithmic ethics.

Today, headlines are filled with claims about the power of Artificial Intelligence (AI) to do things only humans could do before. Recognizing objects in images, responding to voice queries, or interpreting complex text instances, to mention a few. But how do AI applications work? What are the AI solutions being used in International Development and Security? In this post, we summarize some of the basic techniques for computers to process and react to human language using Machine Learning, using real-world scenarios from several of our projects at AKTEK.

The genetic algorithm owes its form to biomimicry, not derivation from first principles. So, unlike the workings of conventional optimization algorithms, which are typically apparent from the underlying mathematical derivations, the workings of the genetic algorithm require elucidation. Attempts to explain how genetic algorithms work can be divided in two: those based to a lesser or greater extent on the scientific method, and those that reject the scientific method in favor of logical positivism.

Artificial Intelligence (AI) and Chatbots are changing the world in more ways we can ever imagine. Completing a diversified range of tasks, AI and Chatbots have become a normal element in our everyday life. The technology has played an important role in the development of varied fields including education and online tutoring. Through artificial intelligence, educators and educational institutes have been able to offer a personalized learning environment to students. AI-driven tools not only improve student interaction and collaboration but also act as a game changer in the innovative ed-tech world. Here are 5 ways artificial intelligence and chatbots are influencing and changing education.

This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness. Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume. The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems. The book augments theory with real-world applications including cyber security, defence and space.

Focusing on students’ presentations and discussions in laboratory seminars, this book presents case studies on evidence-based education using artificial intelligence (AI) technologies. It proposes a system to help users complete research activities, and a machine-learning method that makes the system suitable for long-term operation by performing data mining for discussions and automatically extracting essential tasks. By illustrating the complete process – proposal, implementation, and operation – of applying machine learning techniques to real-world situations, the book will inspire researchers and professionals to develop innovative new applications for education.

According to Maurits Kaptein it’s unethical to use randomized controlled trials to personalize healthcare because new data science methods have better outcomes… (yes, there is a trade-off between transparancy and the use of blackbox algorithms. If you want to understand the details continue reading)

Welcome to AI Policy 101: a new series from Politics + AI that will teach you the fundamentals of artificial intelligence (AI) policy. This introductory article provides an overview of the field, an explanation for the sudden flurry of national AI strategies, and a breakdown of what AI policy entails. It concludes with a set of key takeaways and a list of further readings. What in the world is AI policy? AI policy is defined as public policies that maximize the benefits of AI, while minimizing its potential costs and risks.

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent’s internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent’s behavior.

In the last decade or so we have seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. The next step for AI is machine creativity, i.e. tasks where the correct, or even good, solutions are not known, but need to be discovered. Methods for machine creativity have existed for decades. I believe we are now in a similar situation as deep learning was a few years ago: with the million-fold increase in computational power, those methods can now be used to scale up to creativity in real-world tasks. In particular, Evolutionary Computation is in a unique position to take advantage of that power, and become the next deep learning.

The goal of this article is to inspire data scientists to participate in the debate on the impact that their professional work has on society, and to become active in public debates on the digital world as data science professionals. How do ethical principles (e.g., fairness, justice, beneficence, and non-maleficence) relate to our professional lives? What lies in our responsibility as professionals by our expertise in the field? More specifically this article makes an appeal to statisticians to join that debate, and to be part of the community that establishes data science as a proper profession in the sense of Airaksinen, a philosopher working on professional ethics. As we will argue, data science has one of its roots in statistics and extends beyond it. To shape the future of statistics, and to take responsibility for the statistical contributions to data science, statisticians should actively engage in the discussions. First the term data science is defined, and the technical changes that have led to a strong influence of data science on society are outlined. Next the systematic approach from CNIL is introduced. Prominent examples are given for ethical issues arising from the work of data scientists. Further we provide reasons why data scientists should engage in shaping morality around and to formulate codes of conduct and codes of practice for data science. Next we present established ethical guidelines for the related fields of statistics and computing machinery. Thereafter necessary steps in the community to develop professional ethics for data science are described. Finally we give our starting statement for the debate: Data science is in the focal point of current societal development. Without becoming a profession with professional ethics, data science will fail in building trust in its interaction with and its much needed contributions to society!

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road. In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective’ machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases against certain groups. Despite the fact that we only observe the biased labels, we are able to show that the bias may nevertheless be corrected by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine learning classifier. Our procedure is fast and robust and can be used with virtually any learning algorithm. We evaluate on a number of standard machine learning fairness datasets and a variety of fairness notions, finding that our method outperforms standard approaches in achieving fair classification.

One of the biggest challenges that artificial intelligence (AI) research is facing in recent times is to develop algorithms and systems that are not only good at performing a specific intelligent task but also good at learning a very diverse of skills somewhat like humans do. In other words, the goal is to be able to mimic biological evolution which has produced all the living species on this planet and which seems to have no end to its creativity. The process of intellectual discussions is also somewhat similar to biological evolution in this regard and is responsible for many of the innovative discoveries and inventions that scientists and engineers have made in the past. In this paper, we present an information theoretic analogy between the process of discussions and the molecular dynamics within a cell, showing that there is a common process of information exchange at the heart of these two seemingly different processes, which can perhaps help us in building AI systems capable of open-ended innovation. We also discuss the role of consciousness in this process and present a framework for the development of open-ended AI systems.

In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the field of artificial intelligence (AI); as well as its sub-field of computational intelligence (CI). We demarcate these questions using five unique Rs – namely,
(i) Rationalizability,
(ii) Resilience,
(iii) Reproducibility,
(iv) Realism, and
(v) Responsibility.
Just as air serves as the basic element of biological life, the term AIR5 – cumulatively referring to the five aforementioned Rs – is introduced herein to mark some of the basic elements of artificial life (supporting the sustained growth of AI and CI). A brief summary of each of the Rs is presented, highlighting their relevance as pillars of future research in this arena.

This document represents the beginning of a conversation defining Everyday Ethics for AI. Ethics must be embedded into the design and development process from the very beginning of AI creation. This is meant to stimulate ideas and provoke thought. The idea here is to start simple and iterate. Rather than strive for perfection first, we’re releasing this to allow all who read and use this to comment, critique and participate in all future iterations. So please experiment, play, use, and break what you find here and send us your feedback. Designers and developers of AI systems are encouraged to be aware of these concepts and seize opportunities to intentionally put these ideas into practice. As you work with your team and others, please share this guide with them.

Everyday Ethics for Artificial Intelligence is a framework for AI ethics that you and your team can immediately put into practice. We partnered with Francesca Rossi, IBM’s global leader for AI ethics, to distill a variety of information and perspectives into a digestible and actionable guide for designers and developers.

This is a short (and personal) introduction in German to the connections between artificial intelligence, philosophy, and logic, and to the author’s work. Dies ist eine kurze (und persoenliche) Einfuehrung in die Zusammenhaenge zwischen Kuenstlicher Intelligenz, Philosophie, und Logik, und in die Arbeiten des Autors.

Utility functions or their equivalents (value functions, objective functions, loss functions, reward functions, preference orderings) are a central tool in most current machine learning systems. These mechanisms for defining goals and guiding optimization run into practical and conceptual difficulty when there are independent, multi-dimensional objectives that need to be pursued simultaneously and cannot be reduced to each other. Ethicists have proved several impossibility theorems that stem from this origin; those results appear to show that there is no way of formally specifying what it means for an outcome to be good for a population without violating strong human ethical intuitions (in such cases, the objective function is a social welfare function). We argue that this is a practical problem for any machine learning system (such as medical decision support systems or autonomous weapons) or rigidly rule-based bureaucracy that will make high stakes decisions about human lives: such systems should not use objective functions in the strict mathematical sense. We explore the alternative of using uncertain objectives, represented for instance as partially ordered preferences, or as probability distributions over total orders. We show that previously known impossibility theorems can be transformed into uncertainty theorems in both of those settings, and prove lower bounds on how much uncertainty is implied by the impossibility results. We close by proposing two conjectures about the relationship between uncertainty in objectives and severe unintended consequences from AI systems.

In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well. This dual objective can be summarized by the concept of productive laziness. Existing scheduling approaches mostly focus on efficiency but overlook worker wellbeing through proper rest. In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper. It intelligently recommends personalized work-rest schedules based on local data concerning a worker’s capabilities and situational factors to incorporate opportunistic resting and achieve superlinear collective productivity without the need for explicit coordination messages. Extensive experiments based on a real-world dataset of over 5,000 workers demonstrate that CPL enables workers to spend 70% of the effort to complete 90% of the tasks on average, providing more ethically aligned scheduling than existing approaches.

We find ourselves surrounded by a rapidly increasing number of autonomous and semi-autonomous systems. Two grand challenges arise from this development: Machine Ethics and Machine Explainability. Machine Ethics, on the one hand, is concerned with behavioral constraints for systems, so that morally acceptable, restricted behavior results; Machine Explainability, on the other hand, enables systems to explain their actions and argue for their decisions, so that human users can understand and justifiably trust them. In this paper, we try to motivate and work towards a framework combining Machine Ethics and Machine Explainability. Starting from a toy example, we detect various desiderata of such a framework and argue why they should and how they could be incorporated in autonomous systems. Our main idea is to apply a framework of formal argumentation theory both, for decision-making under ethical constraints and for the task of generating useful explanations given only limited knowledge of the world. The result of our deliberations can be described as a first version of an ethically motivated, principle-governed framework combining Machine Ethics and Machine Explainability

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee information used in the process of personalization as well as describing means to collect this information. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations.

In everyday usage, the term ‘data’ is associated with a jumble of notions about information, science, and knowledge. Countless reports marvel at the astonishing volumes of data being produced and manipulated, the efficiencies and new opportunities this has made possible, and the myriad ways in which society is changing as a result. We speak of ‘raw’ data and laud it for its independence from human judgment. On this basis, ‘data-driven’ (or ‘evidence-based’) decision-making is widely endorsed. Yet data’s purported freedom from human subjectivity also seems to allow us to invest it with agency: ‘Let the data speak for itself,’ for ‘The data doesn’t lie.’

As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and society as a whole. Such decisions will need to be justified due to ethical concerns as well as trust, but achieving this has become difficult due to the `black-box’ nature many AI models have adopted. Explainable AI (XAI) can potentially address this problem by explaining its actions, decisions and behaviours of the system to users. However, much research in XAI is done in a vacuum using only the researchers’ intuition of what constitutes a `good’ explanation while ignoring the interaction and the human aspect. This workshop invites researchers in the HCI community and related fields to have a discourse about human-centred approaches to XAI rooted in interaction and to shed light and spark discussion on interaction design challenges in XAI.

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks: transparency; clear reporting to facilitate replicability; exploration for potential ethical concerns; and, clear demonstrations of effectiveness. There are many reasons for why these issues exist, but one of the most important that we provide a preliminary solution for here is the current lack of ML/AI- specific best practice guidance. Although there is no consensus on what best practice looks in this field, we believe that interdisciplinary groups pursuing research and impact projects in the ML/AI for health domain would benefit from answering a series of questions based on the important issues that exist when undertaking work of this nature. Here we present 20 questions that span the entire project life cycle, from inception, data analysis, and model evaluation, to implementation, as a means to facilitate project planning and post-hoc (structured) independent evaluation. By beginning to answer these questions in different settings, we can start to understand what constitutes a good answer, and we expect that the resulting discussion will be central to developing an international consensus framework for transparent, replicable, ethical and effective research in artificial intelligence (AI-TREE) for health.

Multiple-systems or capture-recapture estimation are common techniques for population size estimation, particularly in the quantitative study of human rights violations. These methods rely on multiple samples from the population, along with the information of which individuals appear in which samples. The goal of record linkage techniques is to identify unique individuals across samples based on the information collected on them. Linkage decisions are subject to uncertainty when such information contains errors and missingness, and when different individuals have very similar characteristics. Uncertainty in the linkage should be propagated into the stage of population size estimation. We propose an approach called linkage-averaging to propagate linkage uncertainty, as quantified by some Bayesian record linkage methodologies, into a subsequent stage of population size estimation. Linkage-averaging is a two-stage approach in which the results from the record linkage stage are fed into the population size estimation stage. We show that under some conditions the results of this approach correspond to those of a proper Bayesian joint model for both record linkage and population size estimation. The two-stage nature of linkage-averaging allows us to combine different record linkage models with different capture-recapture models, which facilitates model exploration. We present a case study from the Salvadoran civil war, where we are interested in estimating the total number of civilian killings using lists of witnesses’ reports collected by different organizations. These lists contain duplicates, typographical and spelling errors, missingness, and other inaccuracies that lead to uncertainty in the linkage. We show how linkage-averaging can be used for transferring the uncertainty in the linkage of these lists into different models for population size estimation.

This paper studies whether rationality can be computed. Rationality is defined as the use of complete information, which is processed with a perfect biological or physical brain, in an optimized fashion. To compute rationality one needs to quantify how complete is the information, how perfect is the physical or biological brain and how optimized is the entire decision making system. The rationality of a model (i.e. physical or biological brain) is measured by the expected accuracy of the model. The rationality of the optimization procedure is measured as the ratio of the achieved objective (i.e. utility) to the global objective. The overall rationality of a decision is measured as the product of the rationality of the model and the rationality of the optimization procedure. The conclusion reached is that rationality can be computed for convex optimization problems.

Here at Noodle we spend a great deal of time thinking of ways to center ethics in our machine learning and data science work. This means that we’re constantly reading about (and thinking about) ethics in AI from every possible angle: from healthcare to industry and beyond. First up is a paper in the AMA Journal of Ethics about AI and healthcare titled ‘Is it ethical to use prognostic estimates from machine learning to treat psychosis?’ You might think that’s looking pretty far into the future, but just this week an insurance company stated that they are enacting a plan to use wearable device information to determine pricing in life insurance, so this is not the future we’re reading about, it’s now.

There’s a lot of talk about the potential evils of artificial intelligence, but in the right hands AI can do extraordinary good. The infographic below, developed by out friends over at Noodle.ai, explores both sides to the story.

Welcome to my second medium post about Data Science. I will write here about a project I’ve done using Machine Learning algorithms. I will explain what I did without relying heavily on technical language, but I will show snippets of my code. Code matters 🙂 The project is a hypothetical case study where I had to identify potential donors to a charity that offers funding to people willing to study machine learning in Silicon Valley. This charity, named CharitML found that every donor was making more than $50,000 annually. My task was to use machine learning algorithms to help this charity identify potential donors in the entire region of California.

Are self-learning programs, such as Alpha Zero or its open source brethren Leela Zero, intelligent? Is this intelligence distinguishable in any meaningful way from that of traditional chess engines such as the world’s strongest chess player, Stockfish? The field of AI is burdened by a subjective definition of intelligence: if you can explain it, then it’s not intelligent. To avoid this mysticism we attempt to come up with a definition that is more objective. Using the formalism of a Markov Decision Process, ‘an intelligence is an agent that can sense and manipulate its environment to achieve its goals’. This definition is problematic in the definition of ‘it’; but for the purposes of game playing we can treat a player (the agent, the ‘self’) as distinct from the game (the environment).

Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces. While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face.

Artificial Intelligence principles define social and ethical considerations to develop future AI. They come from research institutes, government organizations and industries. All versions of AI principles are with different considerations covering different perspectives and making different emphasis. None of them can be considered as complete and can cover the rest AI principle proposals. Here we introduce LAIP, an effort and platform for linking and analyzing different Artificial Intelligence Principles. We want to explicitly establish the common topics and links among AI Principles proposed by different organizations and investigate on their uniqueness. Based on these efforts, for the long-term future of AI, instead of directly adopting any of the AI principles, we argue for the necessity of incorporating various AI Principles into a comprehensive framework and focusing on how they can interact and complete each other.

This paper studies the question on whether machines can be rational. It observes the existing reasons why humans are not rational which is due to imperfect and limited information, limited and inconsistent processing power through the brain and the inability to optimize decisions and achieve maximum utility. It studies whether these limitations of humans are transferred to the limitations of machines. The conclusion reached is that even though machines are not rational advances in technological developments make these machines more rational. It also concludes that machines can be more rational than humans.

I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut’s Jobs First welfare experiment. For this application, Bitler, Gelbach, and Hoynes (2017) show that standard CATE estimators fail to provide evidence for theoretical labour supply predictions. Therefore, this is an interesting benchmark to showcase the value added by using CML methods. I report evidence that the CML estimates of CATEs provide support for the theoretical labour supply predictions. Furthermore, I document some reasons why standard CATE estimators fail to provide evidence for the theoretical predictions. However, I show the limitations of CML methods that prevent them from identifying all the effect heterogeneity of Jobs First.

I quit my job to enter an intensive data science bootcamp. I understand the value behind the vast amount of data available that enables us to create predictive machine learning algorithms. In addition to recognizing its value on a professional level, I benefit from these technologies as a consumer. Whenever I find myself in a musical rut, I rely on Spotify’s Discover Weekly. I’m often amazed by how Spotify’s algorithms and other machine learning models so accurately predict my behavior. In fact, when I first sat down to write this post, I took a break to watch one Youtube video. Twenty minutes later, I realized just how well Youtube’s recommendation algorithm works. Although I so clearly see the benefits of machine learning, it is also essential to recognize and mitigate its potential dangers.

Artificial intelligence evokes a mythical, objective omnipotence, but it is backed by real-world forces of money, power, and data. In service of these forces, we are being spun potent stories that drive toward widespread reliance on regressive, surveillance-based classification systems that enlist us all in an unprecedented societal experiment from which it is difficult to return. Now, more than ever, we need a robust, bold, imaginative response.

Ethical reasoning is an essential skill for today’s computer scientists. The Embedded EthiCS distributed pedagogy embeds philosophers directly into computer science courses to teach students how to think throughthe ethical and social implications of their work. Why Embedded EthiCS? The aim of Embedded EthiCS is to teach students to consider not merely what technologies they could create, but whether they should create them.

As the US Senate debates a new bill, a data-governance expert presents a plan to protect liberty and freedom in the digital age.

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons. Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

The first industrial revolution, powered by steam, launched mass production. The second revolution added electricity to everything. The third added computing power. This new revolution, powered by artificial intelligence (AI), is adding cognitive capabilities to everything – and it’s a game changer. Code that learns is both powerful and dangerous. It threatens the basic rules of markets and civic life. AI requires a new technical and civic infrastructure, a new way to conduct business, a new way to be together in community. AI and enabling technologies like robotics and autonomous vehicles will change lives and livelihoods. Great benefits and unprecedented wealth will be created. But with that will come waves of disruption. Compared to prior revolutions, this one is occurring at exponential speed and while impacts are ubiquitous, control is concentrated. AI is a centralizing force. It plows through monster data sets in seconds aggregating benefits and wealth at an unprecedented speed.

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the 21st century. Its period of phenomenal growth likely lies ahead. Still, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI’s especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. Hence, the materials and technologies that provide the needed energy represent a critical challenge towards future use-scenarios of AI and should be integrated into their design. Here we make four recommendations for stakeholders and especially decision makers to facilitate a successful trajectory for this technology. First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI. Second, that biomimetic energetic solutions be central to design consideration for future AI. Third, that a pre-competitive space be organized between stakeholder partners and fourth, that a trainee pipeline be established to ensure the human capital required for success in this area.

In this article, I discuss what AI can and cannot yet do, and the implications for humanity.

While many see the prospect of autonomous machines as threatening, autonomy may be exactly what we want in a superintelligent machine. There is a sense of autonomy, deeply rooted in the ethical literature, in which an autonomous machine is necessarily an ethical one. Development of the theory underlying this idea not only reveals the advantages of autonomy, but it sheds light on a number of issues in the ethics of artificial intelligence. It helps us to understand what sort of obligations we owe to machines, and what obligations they owe to us. It clears up the issue of assigning responsibility to machines or their creators. More generally, a concept of autonomy that is adequate to both human and artificial intelligence can lead to a more adequate ethical theory for both.

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups’ qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We then give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn’t, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In more realistic scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. This gives quantifiable insight into both sides of the mismatch hypothesis. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI’s freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.

When building and using autonomous and intelligent systems, it’s important to know they’re behaving reliably, because if things go wrong, they can do so at scale, fast.

From Socrates to Cognitive Science

The field of data science is best-suited for those who love mathematics and working with numbers. While some projects are tedious and monotonous, particularly on the entry level, there are plenty of exciting and rewarding jobs in the sector for qualified, experienced professionals. The dawn of big data and next-gen data analytics makes the field even more innovative and exciting by giving individuals access to more data than ever before. Since we are currently living in the Information Age, it only makes sense to use this data in fun, creative and rewarding ways.

The quiet revolution of artificial intelligence looks nothing like the way movies predicted; AI seeps into our lives not by overtaking our lives as sentient robots, but instead, steadily creeping into areas of decision-making that were previously exclusive to humans. Because it is so hard to spot, you might not have even noticed how much of your life is influenced by algorithms. Picture this?-?this morning, you woke up, reached for your phone, and checked Facebook or Instagram, in which you consumed media from a content feed created by an algorithm. Then you checked your email; only the messages that matter, of course. Everything negligible was automatically dumped into your spam or promotions folder. You may have listened to a new playlist on Spotify that was suggested to you based on the music that you’d previously shown interest in. You then proceeded with your morning routine before getting in your car and using Google Maps to see how long your commute would take today. In the span of half an hour, the content you consumed, the music you listened to, and your ride to work relied on brain power other than your own?-?it relied on predictive modelling from algorithms. Machine learning is here. Artificial intelligence is here. We are right in the midst of the information revolution and while it’s an incredible time and place to be in, one must be wary of the implications that come along with it. Having a machine tell you how long your commute will be, what music you should listen to, and what content you would likely engage with are all relatively harmless examples. But while you’re scrolling through your Facebook newsfeed, an algorithm somewhere is determining someone’s medical diagnoses, their parole eligibility, or their career prospects. At face value, machine learning algorithms look like a promising solution for mitigating the wicked problem that is human bias, and all the ways it can negatively impact the lives of millions of people. The idea is that the algorithms in AI are capable of being more fair and efficient than humans ever could be. Companies, governments, organizations, and individuals worldwide are handing off decision-making for many reasons?-?it’s more reliable, it becomes easier, it is less costly, and it’s time-efficient. However, there are still some concerns to be aware of.

We’re rapidly approaching the point where AI will be so pervasive that it’s inevitable that someone will be injured or killed. If you thought this was covered by simple product defect warranties it’s not at all that clear. Here’s what we need to start thinking about.

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.

There are black box models now being used for high stakes decision-making throughout society. The practice of trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward — it is to design models that are inherently interpretable.

We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods. We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race. One major difference is the extreme diversity of ways disabilities manifest, and people adapt. Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination. Given these differences, we explore definitions of fairness and how well they work in the disability space. Finally, we suggest ways of approaching fairness for people with disabilities in AI applications.

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about.

Every AI system is deployed by a human organization. In high risk applications, the combined human plus AI system must function as a high-reliability organization in order to avoid catastrophic errors. This short note reviews the properties of high-reliability organizations and draws implications for the development of AI technology and the safe application of that technology.

In addition to their benefits, optimization systems can have negative economic, moral, social, and political effects on populations as well as their environments. Frameworks like fairness have been proposed to aid service providers in addressing subsequent bias and discrimination during data collection and algorithm design. However, recent reports of neglect, unresponsiveness, and malevolence cast doubt on whether service providers can effectively implement fairness solutions. These reports invite us to revisit assumptions made about the service providers in fairness solutions. Namely, that service providers have (i) the incentives or (ii) the means to mitigate optimization externalities. Moreover, the environmental impact of these systems suggests that we need (iii) novel frameworks that consider systems other than algorithmic decision-making and recommender systems, and (iv) solutions that go beyond removing related algorithmic biases. Going forward, we propose Protective Optimization Technologies that enable optimization subjects to defend against negative consequences of optimization systems.

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled ‘Black’ it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

Smarter and more adaptive machines are rapidly becoming as much a part of our lives as the internet, and more of our decisions are being handed over to intelligent algorithms that learn from ever-increasing volumes and varieties of data. As these ‘robots’ become a bigger part of our lives, we don’t have any framework for evaluating which decisions we should be comfortable delegating to algorithms and which ones humans should retain. That’s surprising, given the high stakes involved. I propose a risk-oriented framework for deciding when and how to allocate decision problems between humans and machine-based decision makers. I’ve developed this framework based on the experiences that my collaborators and I have had implementing prediction systems over the last 25 years in domains like finance, healthcare, education, and sports. The framework differentiates problems along two independent dimensions: predictability and cost per error.