Optimizing how we live, work, and play is embedded deep in the psyche of coders and American culture. But how much efficiency is too much?

Killer robots – they’re coming to get us! At least that’s what the majority of otherwise credible news sources would have us believe when tackling the very serious concerns around AI safety, as well as the increasingly clichéd use of photos of Terminator robots in such articles. It’s a pet peeve expressed by renowned AI researcher Eliezer Yudkowsky in a recent appearance on Sam Harris’ podcast, and a reference that betrays a deep misunderstanding of the ways in which our civilization’s future survival likely depends on ensuring the safe development of future artificial intelligence. For one thing, you’d think we’d be over ‘robots’. As was graphically illustrated by the apparent Russian meddling in the 2016 US presidential election, nefarious artificial intelligence is not some far-off thing – it’s already here and running wild throughout the World Wide Web in the form of bots. And yet somehow we still fail to recognize it for what it is, because our image of malicious artificial intelligence still looks like a gleaming metal endoskeleton with glowing red eyes and an evil grin.

Data Science can do good things for us: it improves life, it makes things more efficient, more effective and leads to a better experience. There are however some miss-steps that data-driven analysis has already exhibited. Here are few examples where data science tools were intentionally or unintentionally misused …

At a recent industry event, I was asked to be on a panel to discuss location-based marketing solutions. The group was in front of 100 local marketing professionals who specifically have the task of helping local businesses or national brands with a local retail presence. They drive more foot traffic to their bricks-and-mortar locations through various digital marketing efforts. This is the battleground for some of the most important data privacy questions of our time. There were three of us on stage, and honestly, this wasn’t one of your friendlier discussions. Because consumer location data is so helpful in targeting would-be visitors to a particular store location, this real-time data is intoxicating for most marketers. Imagine, every consumer walking down the street being bombarded with ads telling them where they can get lunch, a deal on coffee, or where to find the cheapest gas. Who doesn’t want that convenience and benefit? As it turns out, I don’t. And all the marketers on stage do.

Bias is a fundamental human characteristic. We are all biased, by our very nature, and every day we make countless decisions based on our gut feelings. We all have preconceived ideas, prejudices, and opinions. And that is fine, as long as we recognize it and take responsibility for it. The fundamental promise of AI, besides the dramatic increase of data processing power and business efficiency, is to help reduce the conscious or unconscious bias of human decisions. At the end of the day, this is what we expect from algorithms, isn’t it? Objectivity, mathematical detachment rather than fuzzy emotions, fact-based rather than instinctive decisions. Algorithms are supposed to alert people to their cognitive blind spots, so they can make more accurate, unbiased decisions. At least that’s the theory…

The Mission of The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems To ensure every stakeholder involved in the design and development of autonomous and intelligent systems is educated, trained, and empowered to prioritize ethical considerations so that these technologies are advanced for the benefit of humanity. By ‘stakeholder’ we mean anyone involved in the research, design, manufacture, or messaging around intelligent and autonomous systems, including universities, organizations, governments, and corporations making these technologies a reality for society. Our goal is that Ethically Aligned Design will provide insights and recommendations that provide a key reference for the work of technologists in the related fields of science and technology in the coming years. To achieve this goal, in the current version of Ethically Aligned Design (EAD2v2), we identify pertinent ‘Issues’ and ‘Candidate Recommendations’ we hope will facilitate the emergence of national and global policies that align with these principles. The IEEE Global Initiative brings together several hundred participants from six continents, who are thought leaders from academia, industry, civil society, policy and government in the related technical and humanistic disciplines to identify and find consensus on timely issues. A second goal of The IEEE Global Initiative is to provide recommendations for IEEE Standards based on Ethically Aligned Design. Ethically Aligned Design (v1 and v2) and members of The IEEE Global Initiative are the inspiration behind the suite of IEEE P7000 Standards Working Groups that are free and open for anyone to join.
More information on other Working Groups:
IEEE P7000 – Model Process for Addressing Ethical Concerns During System Design
IEEE P7001 – Transparency of Autonomous Systems
IEEE P7002 – Data Privacy Process
IEEE P7003 – Algorithmic Bias Considerations
IEEE P7004 – Standard on Child and Student Data Governance
IEEE P7005 – Standard for Transparent Employer Data Governance
IEEE P7006 – Standard for Personal Data Artificial Intelligence (AI) Agent
IEEE P7007 – Ontological Standard for Ethically Driven Robotics and Automation Systems
IEEE P7008 – Standard for Ethically Driven Nudging for Robotic, Intelligent, and Automation Systems
IEEE P7009 – Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
IEEE P7010 – Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems

Although facial recognition technology dates way back to the 1960s with the innovations of its founding father Woodrow Wilson Bledsoe, it was not until the last 10 years that it has truly come into its own. The newest solutions, including those created at Iflexion, are able to detect faces in a crowd with amazing accuracy. Due to this, they are effectively used in criminal identifications and can help in establishing the identity of missing people. However, such solutions also invoke a lot of criticism regarding the legality and ethics of their application. In this article, we’ll explore the issues that surround facial recognition in depth and look at how these technologies can be made safer for everyone.

Few days ago I attended a sharing session by an eminent professor and one thing captured my attention (which has already been mentioned as the topic’s name of this article). One interesting thing was that this topic during the sharing session turned out to be an endless (almost) question-answer-question kind of conversation between attendees and the professor. This made me think deeper and subsequently consolidated my thoughts to answer this question here from the perspective of business and technical standpoint.

Systeme der Künstlichen Intelligenz (KI) stellen eine substanzielle Herausforderung für Freiheit und Demokratie in unserer Rechtsordnung dar. Entwicklungen und Anwendungen von KI müssen in demokratisch-rechtsstaatlicher Weise den Grundrechten entsprechen. Nicht alles, was technisch möglich und ökonomisch erwünscht ist, darf in der Realität umgesetzt werden. Das gilt in besonderem Maße für den Einsatz von selbstlernenden Systemen, die massenhaft Daten verarbeiten und durch automatisierte Einzelentscheidungen in Rechte und Freiheiten Betroffener eingreifen. Die Wahrung der Grundrechte ist Aufgabe aller staatlichen Instanzen. Wesentliche Rahmenbedingungen für den Einsatz von KI sind vom Gesetzgeber vorzugeben und durch die Aufsichtsbehörden zu vollziehen. Nur wenn der Grundrechtsschutz und der Datenschutz mit dem Prozess der Digitalisierung Schritt halten, ist eine Zukunft möglich, in der am Ende Menschen und nicht Maschinen über Menschen entscheiden.
1. KI darf Menschen nicht zum Objekt machen
2. KI darf nur für verfassungsrechtlich legitimierte Zwecke eingesetzt werden und das Zweckbindungsgebot nicht aufheben
3. KI muss transparent, nachvollziehbar und erklärbar sein
4. KI muss Diskriminierungen vermeiden
5. Für KI gilt der Grundsatz der Datenminimierung
6. KI braucht Verantwortlichkeit
7. KI benötigt technische und organisatorische Standards

Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing (HPC) facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart (HLRS). We also propose a new position tailor-made for coping with dark data and general data management. We call it the scientific data officer (SDO) and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns – and proposes solutions – to the control and authority that the SDO would acquire.

The Food and Drug Administration announced Tuesday that it is developing a framework for regulating artificial intelligence products used in medicine that continually adapt based on new data. The agency’s outgoing commissioner, Scott Gottlieb, released a white paper ( https://www.regulations.gov/document?D=FDA-2019-N-1185-0001 ) that sets forth the broad outlines of the FDA’s proposed approach to establishing greater oversight over this rapidly evolving segment of AI products. It is the most forceful step the FDA has taken to assert the need to regulate a category of artificial intelligence systems whose performance constantly changes based on exposure to new patients and data in clinical settings. These machine-learning systems present a particularly thorny problem for the FDA, because the agency is essentially trying to hit a moving target in regulating them. The white paper describes criteria the agency proposes to use to determine when medical products that rely on artificial intelligence will require FDA review before being commercialized.

Please join the Center for Data Innovation for a conversation about the state of play in developing standards and oversight of AI systems, the need for proper governance of AI in key industries like health care and transportation, and the role that policymakers can play in advancing these efforts.
Date and Time: Thursday, May 30, 2019, from 10:00 to 11:30 AM
Location: 1101 K Street NW, Suite 610, Washington, DC, 20005
Speakers to be announced. The event will be live-streamed on this page. Please return to this page on the day of the event.

Yesterday, a leaked white paper from the United Kingdom government suggested that social media executives could be held legally responsible for harmful content proliferating on their platform’s algorithms. This proposal aims to address one of the single biggest problems brought about by autonomous decision-making: who should be blamed when an AI causes harm?

Following the publication of the draft ethics guidelines in December 2018 to which more than 500 comments were received, the independent expert group presents today their ethics guidelines for trustworthy artificial intelligence.

The new ethics guidelines are a welcome alternative to the EU’s typical ‘regulate first, ask questions later’ approach to new technology. They also reflect a number of improvements from the draft released in December. For example, the new document acknowledges the trade-off between enhancing a system’s explainability and increasing its accuracy. It rightly acknowledges that its principles remain abstract, does away with the poorly defined ‘principle of beneficence,’ and no longer associates ‘nudging’ with ‘risks to mental integrity.’ In addition, it is particularly important that this document does not include recommendations urging the Commission to regulate on AI.

Well, it’s officially done falling apart – it’s been canceled. Google told Vox on Thursday that it’s pulling the plug on the ethics board. The board survived for barely more than one week. Founded to guide ‘responsible development of AI’ at Google, it would have had eight members and met four times over the course of 2019 to consider concerns about Google’s AI program. Those concerns include how AI can enable authoritarian states, how AI algorithms produce disparate outcomes, whether to work on military applications of AI, and more. But it ran into problems from the start.

As debates about the policy and ethical implications of AI systems grow, it will be increasingly important to accurately locate who is responsible when agency is distributed in a system and control over an action is mediated through time and space. Analyzing several high-profile accidents involving complex and automated socio-technical systems and the media coverage that surrounded them, I introduce the concept of a moral crumple zone to describe how responsibility for an action may be misattributed to a human actor who had limited control over the behavior of an automated or autonomous system. Just as the crumple zone in a car is designed to absorb the force of impact in a crash, the human in a highly complex and automated system may become simply a component – accidentally or intentionally – that bears the brunt of the moral and legal responsibilities when the overall system malfunctions. While the crumple zone in a car is meant to protect the human driver, the moral crumple zone protects the integrity of the technological system, at the expense of the nearest human operator. The concept is both a challenge to and an opportunity for the design and regulation of human-robot systems. At stake in articulating moral crumple zones is not only the misattribution of responsibility but also the ways in which new forms of consumer and worker harm may develop in new complex, automated, or purported autonomous technologies.

In the light of ongoing progresses of research on artificial intelligent systems exhibiting a steadily increasing problem-solving ability, the identification of practicable solutions to the value alignment problem in AGI Safety is becoming a matter of urgency. In this context, one preeminent challenge that has been addressed by multiple researchers is the adequate formulation of utility functions or equivalents reliably capturing human ethical conceptions. However, the specification of suitable utility functions harbors the risk of ‘perverse instantiation’ for which no final consensus on responsible proactive countermeasures has been achieved so far. Amidst this background, we propose a novel socio-technological ethical framework denoted Augmented Utilitarianism which directly alleviates the perverse instantiation problem. We elaborate on how augmented by AI and more generally science and technology, it might allow a society to craft and update ethical utility functions while jointly undergoing a dynamical ethical enhancement. Further, we elucidate the need to consider embodied simulations in the design of utility functions for AGIs aligned with human values. Finally, we discuss future prospects regarding the usage of the presented scientifically grounded ethical framework and mention possible challenges.

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics.

Two articles in the last week show the fallacy of the idea of perfect information. In the Economist this week there is ‘How Price-bots can conspire against consumers – and how trustbusters might thwart them’. This article reports on an illicit cooperation between price bots setting gas prices in the US. In today’s Wall Street Journal there is an article titled, ‘To Set Prices, Stores turn to Algorithms’ that reports on a similar story in Germany. The point of the message is that when algorithms are charged with maximizing certain objectives, they might accidentally ‘collude’ (more precisely, behave as if they were colluding since they cannot) and the result has been, so the observations states, a rise in prices. How can this be? Why do I note this today? In yesterday’s US print edition of the Wall Street Journal there was a fine insert with the title and focus: Artificial Intelligence. It is a quick snapshot on the state of the market of AI. One startling item concerns an innovation being worked on that seems to suggest that those of us that are ill with heart disease, but might not yet know it, might be helped simply by talking to a specific app trained with AI to watch for certain heart disease markets. Yes, markers that can be found in ones’ voice!

Mit dem rechtlichen und moralischen Status von Maschinen mit Chips beschäftigt man sich in der Wissenschaft schon seit den 1950er Jahren. Die Science-Fiction-Literatur war noch früher dran. Lange Zeit ging es vor allem darum, ob Roboter Objekte der Moral sind, sogenannte moral patients, ob man ihnen etwa Rechte zugestehen kann. Ich befasse mich seit den 1990er Jahren mit diesem Thema. Ich habe damals keinen Grund gesehen, Robotern Rechte zu geben, und bis heute nicht meinen Standpunkt geändert. Wenn sie eines Tages etwas empfinden oder wenn sie leiden können, oder wenn sie eine Art von Lebenswillen haben, lasse ich mich bestimmt überzeugen. Aber im Moment bemerke ich keine entsprechenden Tendenzen.

Fortschritte auf dem Gebiet der Künstlichen Intelligenz werfen Fragen auf, wie sie sich für jede technologische Revolution stellen: Was ist von Nutzen und Vorteil für den Menschen jenseits der technischen Machbarkeit? Wie verändern sich Wirtschaft, Arbeit und Alltag? Wo liegen Risiken? Wie lassen sich diese Entwicklungen gesellschaftlich und politisch steuern? Die Debatte um KI berührt zusätzlich Kernbereiche des Menschlichen, wenn die Grenzen zwischen Mensch und Maschine verwischen und die Maschine nicht länger ein bloßes Werkzeug ist, sondern selbst Handlungsentscheidungen treffen kann. Die Diskussion darüber, welche Entscheidungen wir überhaupt an Maschinen delegieren wollen, ist von entscheidender Bedeutung.

The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases like the inBloom disaster even been reversed following concerns by governments, stakeholders and civil rights groups. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success.

This paper presents the METAL project, an ongoing French open Learning Analytics (LA) project for secondary school, that aims at improving the quality of the learning process. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a Learning Analytics implementation. This large-scale project includes many concerns, divided into 4 main actions. (1) data management: multi-source data identification, collection and storage, selection and promotion of standards, and design and development of an open-source Learning Record Store (LRS); (2) data visualization: learner and teacher dashboards, with a design that relies on the co-conception with final users, including trust and usability concerns; (3) data exploitation: study of the link between gaze and memory of learners, design of explainable multi-source data-mining algorithms, including ethics and privacy concerns. An additional key of originality lies in the global dissemination of LA at an institution level or at a broader level such as a territory, at the opposite on many projects that focus on a specific school or a school curriculum. Each of these aspects is a hot topic in the literature. Taking into account all of them in a holistic view of education is an additional added value of the project.

An open source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias, and make informed and equitable decisions around developing and deploying predictive risk-assessment tools.

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction).

The AI Fairness 360 toolkit (AIF360) is an open source software toolkit that can help detect and remove bias in machine learning models. It enables developers to use state-of-the-art algorithms to regularly check for unwanted biases from entering their machine learning pipeline and to mitigate any biases that are discovered. AIF360 enables AI developers and data scientists to easily check for biases at multiple points along their machine learning pipeline, using the appropriate bias metric for their circumstances. It also provides a range of state-of-the-art bias mitigation techniques that enable the developer or data scientist to reduce any discovered bias. These bias detection techniques can be deployed automatically to enable an AI development team to perform systematic checking for biases similar to checks for development bugs or security violations in a continuous integration pipeline.

In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains. The discussion around these developments, however, has implicitly equated the specific task of prediction with the general task of automation. We argue here that automation is broader than just a comparison of human versus algorithmic performance on a task; it also involves the decision of which instances of the task to give to the algorithm in the first place. We develop a general framework that poses this latter decision as an optimization problem, and we show how basic heuristics for this optimization problem can lead to performance gains even on heavily-studied applications of AI in medicine. Our framework also serves to highlight how effective automation depends crucially on estimating both algorithmic and human error on an instance-by-instance basis, and our results show how improvements in these error estimation problems can yield significant gains for automation as well.

As companies attempt to introduce self-driving cars, flying taxis, cargo drones, and delivery robots to the public sphere, they are constantly battling to adapt to the inconsistent world we’ve already created. So the question arises, should we modify our own world to better suit the autonomous technology of the future?

In a recent paper, Hila Gonen and Yoav Goldberg argue that methods for de-biasing language models aren’t effective; they make bias less apparent, but don’t actually remove it. De-biasing might even make bias more dangerous by hiding it, rather than leaving it out in the open. The toughest problems are often the ones you only think you’ve solved. Language models are based on ‘word embeddings,’ which are essentially lists of word combinations derived from human language. There are some techniques for removing bias from word embeddings. I won’t go into them in detail, but for the sake of argument, imagine taking the texts of every English language newspaper and replacing ‘he,’ ‘she,’ and other gender-specific words with ‘they’ or ‘them.’ (Real techniques are more sophisticated, of course.) What Gonen and Goldberg show is that words still cluster the same way: stereotypically female professions still turn up as closely related (their example is nurse, caregiver, receptionist, and teacher).

This book will help researchers and engineers in the design of ethical systems for robots, addressing the philosophical questions that arise and exploring modern applications such as assistive robots and self-driving cars. The contributing authors are among the leading academic and industrial researchers on this topic and the book will be of value to researchers, graduate students and practitioners engaged with robot design, artificial intelligence and ethics.

The results of a new study on AI ethics was released. According to research conducted by Vason Bourne on behalf of SnapLogic, studied the views and perspectives of IT decision-makers (ITDMs) across industries, asking key questions such as: who bears primary responsibility to ensure AI is developed ethically and responsibly, will global expert consortiums impact the future development of AI, and should AI be regulated and, if so, by whom?

The conventional wisdom among European policymakers is that the best way to compete against China and the United States in artificial intelligence (AI) is to differentiate European products by emphasizing ethical design. However, a new survey from the Center for Data Innovation finds that consumers are not willing to pay a premium for products labeled ‘ethical by design.’ If the European Union naively continues down this path, it may soon find itself falling further behind in AI competitiveness. Many European policymakers have long been enamored of the notion that increasing consumer trust in the digital economy leads to greater adoption because it allows them to argue that additional regulation is unambiguously good. Rather than coming at the expense of innovation, European policymakers claim that strong regulations will spur greater consumer trust and therefore lead to greater adoption of digital innovations. For example, former EU Commissioner Viviane Reding argued that ‘high standards of data protection will also give Europe’s cloud providers a competitive advantage.’

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.

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.

Government usually isn’t the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia’s chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation’s push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens. ‘We want the government to be as lean as possible,’ says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden’s Umeå University on using the Internet of Things and sensor data in government services. Estonia’s government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.

Today, the U.S. Department of Housing and Urban Development announced it is suing Facebook for violating the Fair Housing Act. It accuses the tech giant of ‘encouraging, enabling, and causing housing discrimination’ by allowing advertisers to block real estate ads from race, religion, country of birth, and other protected characteristics. Advertisers could effectively red-line by excluding inhabitants of certain zip codes from seeing their ads. They could also filter out people who are non-American-born, non-Christian, interested in Hispanic culture, or even interested in ‘deaf culture.’ With all of the ethical scandals plaguing Facebook in the last 12 months, ‘deaf culture’ seems like an apt description of the company itself.

Build Machine Learning Models That Are Fundamentally Sound, Assessable, Inclusive, And Reversible

Most of the time, machine learning does not touch on particularly sensitive social, moral, or ethical issues. Someone gives us a data set and asks us to predict house prices based on given attributes, classifying pictures into different categories, or teaching a computer the best way to play PAC-MAN?-?what do we do when we are asked to base predictions of protected attributes according to anti-discrimination laws? How do we ensure that we do not embed racist, sexist, or other potential biases into our algorithms, be it explicitly or implicitly?

Key Takeaways:
• As we use more natural interfaces with technology, like language, our relationship is shifting to one where we increasingly humanise them.
• Improvements in natural language understanding and our changing relationship means we can use chatbots in ways we couldn’t before – both to augment human conversation and support, or indeed to replace it.
• Advances in AI mean that our experience can increasingly be personalized as analysis of our physical, mental and emotional state through our conversation and voice become possible.
• As technology provides more ambient and customised experiences we risk exposing large amounts of data, perhaps without intending to, that could be used to target us or sold to other companies for their use.
• Those working in the Software Industry must understand and take responsibility for how we use Conversational AI and our user’s data.

Artificial Intelligence is currently one of the hottest topics out there, mostly for bad reasons than good. On one hand, we’ve been able to achieve major breakthroughs in technology, putting us one step closer to creating thinking machines with human like perception. On the other, we gave rise to a whole new danger for our society that is not external like a meteorite or a deadly bacteria, but that comes from within humanity itself. It would be foolish to think that something so powerful and revolutionary can only have a positive impact on our society. Despite the fact that most of the aims within the community are geared towards noble causes, we cannot predict what are the medium to long term effects of inserting AI algorithms in every single part of our lives. Take a look at social media, which is now widely considered as something that can have negative effect on human psyche, all with the purpose of generating more clicks. The truth is that no matter how aware we are of the environment around us, there will always be unwanted side effects from trying to improve peoples’ lives with technology.

The area of formal ethics is experiencing a shift from a unique or standard approach to normative reasoning, as exemplified by so-called standard deontic logic, to a variety of application-specific theories. However, the adequate handling of normative concepts such as obligation, permission, prohibition, and moral commitment is challenging, as illustrated by the notorious paradoxes of deontic logic. In this article we introduce an approach to design and evaluate theories of normative reasoning. In particular, we present a formal framework based on higher-order logic, a design methodology, and we discuss tool support. Moreover, we illustrate the approach using an example of an implementation, we demonstrate different ways of using it, and we discuss how the design of normative theories is now made accessible to non-specialist users and developers.

Explaining complex or seemingly simple machine learning models is a practical and ethical question, as well as a legal issue. Can I trust the model? Is it biased? Can I explain it to others? We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Of existing work on interpreting complex models, Shapley values is the only method with a solid theoretical foundation. Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions. Like most other existing methods, this approach assumes independent features, which may give very wrong explanations. This is the case even if a simple linear model is used for predictions. We extend the Kernel SHAP method to handle dependent features. We provide several examples of linear and non-linear models with linear and non-linear feature dependence, where our method gives more accurate approximations to the true Shapley values. We also propose a method for aggregating individual Shapley values, such that the prediction can be explained by groups of dependent variables.

Business and organizations do not operate in a vacuum. Their relationship to the society and environment in which they operate is a critical factor in their ability to continue to operate effectively. It is also increasingly being used as a measure of their overall performance. ISO 26000 provides guidance on how businesses and organizations can operate in a socially responsible way. This means acting in an ethical and transparent way that contributes to the health and welfare of society.

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decision-making (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

At my bodega down the block, photos of shoplifters sometimes litter the windows, a warning to would-be thieves that they’re being watched. Those unofficial wanted posters come and go, as incidents fade from the owner’s memory. But with facial recognition, getting caught in one store could mean a digital record of your face is shared across the country. Stores are already using the technology for security purposes and can share that data — meaning that if one store considers you a threat, every business in that network could come to the same conclusion. One mistake could mean never being able to shop again.

Das KI Gütesiegel des KI Bundesverband e.V. verfolgt das Ziel einen menschen-zentrierten und menschen-dienlichen Einsatz von Künstlicher Intelligenz zu sichern. Durch das Definieren und Einhalten von einem übergeordneten Werte- und Prozessverständnis stellt das Gütesiegel eine ethisch verträgliche Service- und Produktentwicklung sicher. Im Zentrum stehen die Gütekriterien Ethik, Unvoreingenommenheit, Transparenz sowie Sicherheit und Datenschutz. Zu jedem Gütekriterium sind notwendige Maßnahmen festgehalten. Zum Zeitpunkt der Einführung umfasst das Gütesiegel eine Selbstverpflichtungserklärung.

Visualization is such a powerful amplifier of human abilities that it should be illegal, unprofessional, and unethical to do data analysis using only statistical and algorithmic processes.’ – Ben Shneiderman

The ethical considerations are also a very important area which should be reported on. It is important to critically think about this area and assess the impact of the system’s capabilities. Ethical considerations need to be a part of the product design and planning process. Data science is an emerging discipline. It will most likely evolve over time. Follow interesting problems, people and technologies into the future of what data science will become. The data science life cycle is generally comprised of the following components:
• data retrieval
• data cleaning
• data exploration and visualization
• statistical or predictive modeling
While these components are helpful for understanding the different phases, they don’t help us think about our programming workflow.
Often, the entire data science life cycle ends up as an arbitrary mess of notebook cells in either a Jupyter Notebook or a single messy script. In addition, most data science problems require us to switch between data retrieval, data cleaning, data exploration, data visualization, and statistical/predictive modeling.

The prevalence of new technologies and social media has amplified the effects of misinformation on our societies. Thus, it is necessary to create computational tools to mitigate their effects effectively. This study aims to provide a critical overview of computational approaches concerned with combating misinformation. To this aim, I offer an overview of scholarly definitions of misinformation. I adopt a framework for studying misinformation that suggests paying attention to the source, content, and consumers as the three main elements involved in the process of misinformation and I provide an overview of literature from disciplines of psychology, media studies, and cognitive sciences that deal with each of these elements. Using the framework, I overview the existing computational methods that deal with 1) misinformation detection and fact-checking using Content 2) Identifying untrustworthy Sources and social bots, and 3) Consumer-facing tools and methods aiming to make humans resilient to misinformation. I find that the vast majority of works in computer science and information technology is concerned with the crucial tasks of detection and verification of content and sources of misinformation. Moreover, I find that computational research focusing on Consumers of Misinformation in Human-Computer Interaction (HCI) and related fields are very sparse and often do not deal with the subtleties of this process. The majority of existing interfaces and systems are less concerned with the usability of the tools rather than the robustness and accuracy of the detection methods. Using this survey, I call for an interdisciplinary approach towards human-misinformation interaction that focuses on building methods and tools that robustly deal with such complex psychological/social phenomena.

In the early part of the 20th century, a group of German experimental psychologists began to question how the brain acquires meaningful perceptions of a world that is otherwise chaotic and unpredictable. To answer this question, they developed the notion of the ‘gestalt effect’ – the idea that when it comes to perception, the whole is something other than the parts. Recommended for You Watch two astronauts take a spacewalk to give the ISS a power upgrade Boeing sold two safety features on its 737 Max planes as ‘extras’ Drones that perch like birds could go on much longer flights A quantum experiment suggests there’s no such thing as objective reality Microsoft just booted up the first ‘DNA drive’ for storing data Sine then, psychologists have discovered that the human brain is remarkably good at perceiving complete pictures on the basis of fragmentary information. A good example is the figure shown here. The brain perceives two-dimensional shapes such as a triangle and a square, and even a three-dimensional sphere. But none of these shapes is explicitly drawn. Instead, the brain fills in the gaps. A natural extension to this work is to ask whether gestalt effects occur in neural networks. These networks are inspired by the human brain. Indeed, researchers studying machine vision say the deep neural networks they have developed turn out to be remarkably similar to the visual system in primate brains and to parts of the human cortex.

Artificial Intelligence is gradually wiping out the human touch. Not only labor intensive jobs but creative jobs like journalism are also under threat owing to its outreach. The vicious circle of humans programming robots to act like humans and disillusion them is eventually mechanizing humans. With the magnitude of autonomy that is being granted to robots, robots procreating robots may not be a distant phenomenon. As their numbers increase, so will their impact, paving the way for the Slave to soon overtake its Master.

The growing interest in automation in the enterprise highlights the need to place humans in jobs that are best suited for humans rather than machines, and a need for continuous training.

A vision towards bringing machine learning closer to humans. Is there a way to re-interpret machine learning in a constructivist way? And more importantly, why should we do it? The answers to both questions are quite straightforward. Yes, we can do it, and the motivation for that may address one of the crucial flaws of modern machine learning, i.e., bringing it closer to human interpretation of reality. The key component of cognitive functionality is a model. Humans are able to build very complex models, thanks to the way our mind works. Functionalistic psychology have shown that mental models are able to continuously build hypothetical constructs to predict the environment, and continuously modify them.

I see a society that is crumbling. The rampant technology is simultaneously capsizing industries that were previously the bread and butter of economic growth. The working man and woman have felt its effects as wages stagnate and employment opportunities remain fewer amidst a progressively automated economy. Increasing wage inequality and financial vulnerability have given rise to populism, and the domino effects are spreading. People are angry. They demand fairness and are threatened by policies and outsiders that may endanger their livelihoods. This has caused a greater cultural and racial divide within and between nations. Technology has enabled this anger to spread, influence and manipulate at a much greater speed than ever before resulting in increasing polarization and a sweeping anxiety epidemic. Globally, we are much more connected – this, to our detriment. We’ve witnessed both government and business leverage technology to spread disinformation for their gains. While regulators struggle to keep pace with these harms, the tech giants continue, unabated, to wield their influence and power to establish footprints that make both consumers and business increasingly dependent on their platforms and technology stacks. We cannot escape them, nor do we want to. Therein lays the concern…

The ways in which artificial intelligence (AI) is woven into our everyday lives can hardly be overstated. Powerful deep machine-learning algorithms increasingly predict what movies we want to watch, which ads we’ll respond to, how eligible we are for a loan, and how likely we are to commit a crime or perform well on the job.¹ AI is also revolutionizing the automation of physical systems such as factories, power plants, and self-driving cars, and the pace of deployment is rapidly increasing. However, the recent fatal failures of auto-pilot systems built by Tesla, Uber, and Boeing highlight the risks associated with relying on opaque and highly complex software in dangerous situations.² Mitigating the dangers posed by such systems is an area of active research called resilience engineering, but the rapid adoption of AI, coupled with its notorious lack of algorithmic transparency, makes it difficult for the research to keep pace. One of the pioneers of machine-learning, Professor Thomas Dietterich, has spent the last several years investigating how artificial intelligence can be made more robust, especially when it’s embedded in a complex socio-technical system where the combination of human and software errors can propagate into untested regimes. Paul Scharre’s recent book, ‘An Army of None’, inspired Prof. Dietterich to look deeper into the literature of high-reliability organizations for potential solutions. ‘It drove home to me the sense that it’s not enough to make the technology reliable, you need to make the entire human organization that surrounds it reliable also; understanding what that means has been taking up a lot of my time.’ Prof. Dietterich spared some of that time to discuss with me his thoughts on AI ethics, AI safety, and his recent article Robust artificial intelligence and robust human organizations.

A short read on the psychological impacts of app design, data science, and the humans predisposition for being conditioned. Before I jump in, let me ask you some questions. How often are you on your smartphone? Do you sometimes find yourself opening Facebook, YouTube, or Instagram for a spare second between tasks and end up spending more time than you meant? Have you ever found yourself reloading the feeds to see if something better just got posted? Have you ever closed a Facebook tab only to open it a few minutes later? Be honest about those questions since only you who needs to know, and I didn’t even ask about Tinder or porn. According to announcements by Facebook, users on average spend 50min of their day on the app (that’s out of 2 billion people across the globe). And it’s not just Facebook. Youtube, Snapchat, Instagram, and Twitter all have giant time slices out of their users’ day, and a lot of those users overlap.

As machine learning (ML) systems have advanced, they have acquired more power over humans’ lives, and questions about what values are embedded in them have become more complex and fraught. It is conceivable that in the coming decades, humans may succeed in creating artificial general intelligence (AGI) that thinks and acts with an open-endedness and autonomy comparable to that of humans. The implications would be profound for our species; they are now widely debated not just in science fiction and speculative research agendas but increasingly in serious technical and policy conversations. Much work is underway to try to weave ethics into advancing ML research. We think it useful to add the lens of parenting to these efforts, and specifically radical, queer theories of parenting that consciously set out to nurture agents whose experiences, objectives and understanding of the world will necessarily be very different from their parents’. We propose a spectrum of principles which might underpin such an effort; some are relevant to current ML research, while others will become more important if AGI becomes more likely. These principles may encourage new thinking about the development, design, training, and release into the world of increasingly autonomous agents.

As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot’s behavior. These approaches, however, fail to consider the cognitive effort needed to understand the received explanation. In particular, the human teammate is expected to understand any explanation provided before the task execution, no matter how much information is presented in the explanation. In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps to spread out the information to be explained and thus reducing the cognitive load of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented. We base our explanation generation method in a model reconciliation setting introduced in our prior work. Our approach is evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with four different problems.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

Press the pause button! Artificial Intelligence (AI) continues to be a growing focus in the media. An agenda gathering momentum like the cloud did, particularly in the business world. On a global path of technology innovation, AI may seem the next logical step towards progress. Computing power, storage, and processor speed have rapidly improved, and it’s now the turn of the algorithms. But what is progress? What is the cost? And is this what humanity really needs or wants? Who decides? A good place to begin is to define what AI actually is. For the purpose of this post, AI is software that, when executed, can demonstrate an element of decision-making where a programmed result may be unknown, and would typically require human intelligence to perform the decision-making task. AI usually includes an aspect of automated processing that engages one or more of the human senses i.e. sight, speech, sound, taste or smell. Recent discussions in the media, online articles and radio broadcasts sometimes blur the lines between two identifiable AI spaces:
• Near term: machines to perform faster, identify patterns, make unaided decisions, and undertake relatively complex tasks with the goal of reducing any human requirement to perform the same tasks.
• Long term: machines to potentially possess the characteristic of ‘consciousness’ – this is a different space.
Conversations can wander between the impact of these two very different visions. I recently listened to a radio discussion where a caller spoke about a cull on jobs and the knock-on effects within society, but then the caller leapt to a possibility that machines could wipe out humankind. The distinction between the two is important.

The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins’ (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins’ critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.

The ethical decisions behind the acquisition and analysis of audio, video or physiological human data, harnessed for (deep) machine learning algorithms, is an increasing concern for the Artificial Intelligence (AI) community. In this regard, herein we highlight the growing need for responsible, and representative data collection and analysis, through a discussion of modality diversification. Factors such as Auditability, Benchmarking, Confidence, Data-reliance, and Explainability (ABCDE), have been touched upon within the machine learning community, and here we lay out these ABCDE sub-categories in relation to the acquisition and analysis of multimodal data, to weave through the high priority ethical concerns currently under discussion for AI. To this end, we propose how these five subcategories can be included in early planning of such acquisition paradigms.

Cancer analysis and prediction is the utmost important research field for well-being of humankind. The Cancer data are analyzed and predicted using machine learning algorithms. Most of the researcher claims the accuracy of the predicted results within 99%. However, we show that machine learning algorithms can easily predict with an accuracy of 100% on Wisconsin Diagnostic Breast Cancer dataset. We show that the method of gaining accuracy is an unethical approach that we can easily mislead the algorithms. In this paper, we exploit the weakness of Machine Learning algorithms. We perform extensive experiments for the correctness of our results to exploit the weakness of machine learning algorithms. The methods are rigorously evaluated to validate our claim. In addition, this paper focuses on correctness of accuracy. This paper report three key outcomes of the experiments, namely, correctness of accuracies, significance of minimum accuracy, and correctness of machine learning algorithms.

Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We’ve even delegated life-and-death decisions to algorithms-decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators-that is, like you and me.

Humans share a powerful need for emotional connection. It’s what sets us apart from other species: Our ability to connect and relate to one another gives us the power to cooperate and work together toward common goals. In the corporate world, this means that all customers share this same need. As humans, we want to feel heard and understood, and that need carries over to the products and services we purchase. This is a key challenge for businesses. In the age of outsourcing and automation, how can companies truly understand and connect with their customers? Artificial intelligence and machine learning may bring to mind images of emotionless robots and cold algorithms. But in fact, these tools can – and should – help to make businesses more human. This is a core belief at Trulia and one that is woven through our product strategy. I believe in building tech for a purpose, and that includes how we use technologies to build our products. It’s easy to fall into the trap of using machine learning just for the sake of it, like a hammer searching for a nail. At Trulia, we’re not using AI and deep learning because they’re cool buzzwords or because we want to stay ahead of the curve. We’re using them for a specific purpose: to create a more meaningful experience and solve key challenges for our customers.

How fast are computer systems really? Those of us who work in technology can blithely rattle off the clock speeds of processors, but what do those numbers mean? For example, each core of a server based on the Intel Xeon processor E5 v4 family might perform 2.4 billion clock ticks – and might execute many times that number of instructions – in the second or so that it takes to read and register this fact. But these are numbers so gargantuan as to be completely outside of human experience. Let’s rescale things a bit to get a visceral sense of these machines we use every day.

We have a great opportunity to lay a better foundation for the future of AI if we encourage more women and people of diverse backgrounds to dive into data science and other AI fields.

We are surrounded by systems that make ethical decisions: systems approving loans, trading stocks, forwarding news articles, recommending jail sentences, and much more. They act for us or against us, but almost always without our consent or even our knowledge. In recent articles, I’ve suggested the ethics of artificial intelligence itself needs to be automated. But my suggestion ignores the reality that ethics has already been automated: merely claiming to make data-based recommendations without taking anything else into account is an ethical stance. We need to do better, and the only way to do better is to build ethics into those systems. This is a problematic and troubling position, but I don’t see any alternative. The problem with data ethics is scale. Scale brings a fundamental change to ethics, and not one that we’re used to taking into account. That’s important, but it’s not the point I’m making here. The sheer number of decisions that need to be made means that we can’t expect humans to make those decisions. Every time data moves from one site to another, from one context to another, from one intent to another, there is an action that requires some kind of ethical decision.

Computing professionals’ actions change the world. To act responsibly, they should reflect upon the wider impacts of their work, consistently supporting the public good. The ACM Code of Ethics and Professional Conduct (‘the Code’) expresses the conscience of the profession.
1. GENERAL ETHICAL PRINCIPLES.
1.1 Contribute to society and to human well-being, acknowledging that all people are stakeholders in computing.
1.2 Avoid harm.
1.3 Be honest and trustworthy.
1.4 Be fair and take action not to discriminate.
1.5 Respect the work required to produce new ideas, inventions, creative works, and computing artifacts.
1.6 Respect privacy.
1.7 Honor confidentiality.
2. PROFESSIONAL RESPONSIBILITIES.
2.1 Strive to achieve high quality in both the processes and products of professional work.
2.2 Maintain high standards of professional competence, conduct, and ethical practice.
2.3 Know and respect existing rules pertaining to professional work.
2.4 Accept and provide appropriate professional review.
2.5 Give comprehensive and thorough evaluations of computer systems and their impacts, including analysis of possible risks.
2.6 Perform work only in areas of competence.
2.7 Foster public awareness and understanding of computing, related technologies, and their consequences.
2.8 Access computing and communication resources only when authorized or when compelled by the public good.
2.9 Design and implement systems that are robustly and usably secure.
3. PROFESSIONAL LEADERSHIP PRINCIPLES.
3.1 Ensure that the public good is the central concern during all professional computing work.
3.2 Articulate, encourage acceptance of, and evaluate fulfillment of social responsibilities by members of the organization or group.
3.3 Manage personnel and resources to enhance the quality of working life.
3.4 Articulate, apply, and support policies and processes that reflect the principles of the Code.
3.5 Create opportunities for members of the organization or group to grow as professionals.
3.6 Use care when modifying or retiring systems.
3.7 Recognize and take special care of systems that become integrated into the infrastructure of society.
4. COMPLIANCE WITH THE CODE.
4.1 Uphold, promote, and respect the principles of the Code.
4.2 Treat violations of the Code as inconsistent with membership in the ACM.

This repository contains a summary of the current idea of developing a set of ‘Ethically Aligned Licenses’. This would consist of a set of the most common open source software licenses modified to require alignment to an ‘ethical framework’. The current Ethical Framework being considered is the ACM Code of Ethics and Professional Conduct.

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived – the talk also contains an insight on the definitions and principles in this library.

Every day, someone comes up with a new use for old data. Recently, IBM scraped a million photos from Flickr and turned them into a training data set for an AI project intending to reduce bias in facial recognition. That’s a noble goal, promoted to researchers as an opportunity to make more ethical AI.

There are a whole lot of different ways to misunderstand or be duped by data. This is my round up of good links that illustrate some of the most common problems with relying on data. Additions are welcome, but I’m looking for news stories, rather than theoretical examples. Your job, as a reporter, is to put the data in context. Sometimes that means making an honest decision about whether or not maps are even the right way to tell the story, because how you tell a story matters.

Kate Klonick, an assistant professor at St John’s Law School, teaches an Information Privacy course for second- and third-year law students; she devised a wonderful and simply exercise to teach her students about ‘anonymous speech, reasonable expectation of privacy, third party doctrine, and privacy by obscurity’ over the spring break. Klonick’s students were assigned to sit in a public place and eavesdrop on nearby conversations, then, using only Google searches, ‘see if you can de-anonymize someone based on things they say loudly enough for lots of others to hear and/or things that are displayed on their clothing or bags.’

Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the ‘disruptive’ potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems – and how the effectiveness in the demands of AI ethics can be improved.

The data revolution continues to transform every sector of science, industry and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly — in accordance with laws and ethical norms. In this article we discuss three recent regulatory frameworks: the European Union’s General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, that aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. Our goal in this paper is to bring these regulatory frameworks to the attention of the data management community, and to underscore the technical challenges they raise and which we, as a community, are well-equipped to address. The main take-away of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.

We provide a formal definition of blameworthiness in settings where multiple agents can collaborate to avoid a negative outcome. We first provide a method for ascribing blameworthiness to groups relative to an epistemic state (a distribution over causal models that describe how the outcome might arise). We then show how we can go from an ascription of blameworthiness for groups to an ascription of blameworthiness for individuals using a standard notion from cooperative game theory, the Shapley value. We believe that getting a good notion of blameworthiness in a group setting will be critical for designing autonomous agents that behave in a moral manner.

Back in the day, machine experiences were a drag. Hit a button, pull a lever, and get the task done. Decades later, with subsequent computing innovation, machines have transformed into their ultra-smart, self-learning, automated versions that are sweeping the human landscape. The underlying technology that’s reinventing machines to personalize human experiences is Machine Learning (ML), a branch of Artificial Intelligence and a strong buzzword in today’s digital-first world. In essence, it’s about programming machines to infuse the ability of self-learning by leveraging Big Data. Information extracted from various touchpoints is analyzed and used to predict intentions for actionable intelligence. And, the good news is, Latest technology is advancing consistently and revolutionizing every facet of our routines. Humans had their first brush-up with Machine Learning when voice-controlled personal assistants?-?Amazon’s Echo and Alexa?-?were launched. These devices are a new normal with the trend of smart homes picking up. Driverless cars, which were a quintessential sci-fi fantasy, aren’t something of the far-off future now. These new-age vehicles, aimed at cutting down human labor, are tested across the world for their utility benefits. Initially, the idea of intelligent machines was preposterous. Machines that act on behalf of humans weren’t a norm. However, with enablement and evolution of Machine Learning in our daily lives, the human landscape is radically changing and how. Below, we have mentioned 10 ways in which Machine Learning is revolutionizing our lives. Let’s take a dive right in.