We focus on the problem of designing an artificial agent capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible. Our insight is that in order to do so, we should develop an understanding of human decision for the task domain. In this work, we consider the domain of collaborative packing, and as a first step, we explore the mechanisms underlying human packing strategies. We conduct a user study in which human participants complete a series of packing tasks in a virtual environment. We analyze their packing strategies and discover that they exhibit specific spatial and temporal patterns (e.g., humans tend to place larger items into corners first). Our insight is that imbuing an artificial agent with an understanding of this spatiotemporal structure will enable improved assistance, which will be reflected in the task performance and human perception of the AI agent. Ongoing work involves the development of a framework that incorporates the extracted insights to predict and manipulate human decision making towards an efficient route of low cognitive load. A follow-up study will evaluate our framework against a set of baselines featuring distinct strategies of assistance. Our eventual goal is the deployment and evaluation of our framework on an autonomous robotic manipulator, actively assisting users on a packing task.

Gary Marcus is not impressed by the hype around deep learning. While the NYU professor believes that the technique has played an important role in advancing AI, he also thinks the field’s current overemphasis on it may well lead to its demise. Marcus, a neuroscientist by training who has spent his career at the forefront of AI research, cites both technical and ethical concerns. From a technical perspective, deep learning may be good at mimicking the perceptual tasks of the human brain, like image or speech recognition. But it falls short on other tasks, like understanding conversations or causal relationships. To create more capable and broadly intelligent machines, often referred to colloquially as artificial general intelligence, deep learning must be combined with other methods.

As so many more organizations now rely on AI to deliver services and consumer experiences, establishing a public trust in the AI is crucial as these systems begin to make harder decisions that impact customers.

Bias in AI programming, both conscious and unconscious, is an issue of concern raised by scholars, the public, and the media alike. Given the implications of usage in hiring, credit, social benefits, policing, and legal decisions, they have good reason to be. AI bias occurs when a computer algorithm makes prejudiced decisions based on data and/or programming rules. The problem of bias is not only with coding (or programming), but also with the datasets that are used to train AI algorithms, in what some call the ‘discrimination feedback loop.’

Today was another rainy Friday afternoon in Berlin. At 4 pm sharp, a dear colleague of mine came to my working place and took me for a quick coffee break. While walking, avoiding the small water puddles in the floor, she said: ‘Yesterday I saw a movie about this couple. After 9 years being together and still loving each other, they broke up. The girl had an amazing working opportunity in another place and the guy did not want a long distance relationship. Seriously mate, heartbreaking.’ One thing led to another, and suddenly I said: ‘look, no matter what everybody says. I am convinced that when two people love, truly love, each other, everything can be solved. They can overcome everything. I understand and respect other people opinions, but love is at the core of everything that I am and do’. ‘But, dude, really, why you just can’t nurture yourself from other human experiences around you. Can’t you see that life is harsh and realise that love is not enough?’, she replied.

Benefits of data science, you might guess that data science has played a role in your daily life. After all, it not only affects what you do online, but what you do offline. Companies are using massive amounts of data to create better ads, produce tailored recommendations, and stock shelves, in the case of retail stores. It’s also shaping how, and who, we love. Here’s how data impacts us daily.

The modern business leader’s new responsibility in a brave new world ruled by data. As Data Science moves along the hype cycle and matures as a business function, so do the challenges that face the discipline. The problem statement for data science went from ‘we waste 80% of our time preparing data’ via ‘production deployment is the most difficult part of data science’ to ‘lack of measurable business impact’ in the last few years.

Services mediated by ICT platforms have shaped the landscape of the digital markets and produced immense economic opportunities. Unfortunately, the users of platforms not only surrender the value of their digital traces but also subject themselves to the power and control that data brokers exert for prediction and manipulation. As the platform revolution takes hold in public services, it is critically important to protect the public interest against the risks of mass surveillance and human rights abuses. We propose a set of design constraints that should underlie data systems in public services and which can serve as a guideline or benchmark in the assessment and deployment of platform-mediated services. The principles include, among others, minimizing control points and non-consensual trust relationships, empowering individuals to manage the linkages between their activities and empowering local communities to create their own trust relations. We further propose a set of generic and generative design primitives that fulfil the proposed constraints and exemplify best practices in the deployment of platforms that deliver services in the public interest. For example, blind tokens and attribute-based authorization may prevent the undue linking of data records on individuals. We suggest that policymakers could adopt these design primitives and best practices as standards by which the appropriateness of candidate technology platforms can be measured in the context of their suitability for delivering public services.

Tools and approaches to help CX Designers and Product Owners approach emergent tech projects.

If you think this is a no-brainer and reliable option (A) is the obvious answer, think again. It really depends on the skills of whoever’s giving the workers their instructions. Reliable workers will efficiently scale up the intelligent decision-making of a good leader, but they will unfortunately also amplify a foolish decision-maker. Remember those classic café posters? ‘Coffee: Do stupid things faster with more energy!’ When a leader is incompetent (or depraved), unreliable workers are a blessing. Can’t drag single-minded determination out of them? How wonderful! Things can get scary when zealots wholeheartedly pursue objectives set by a bad decision-maker.

In 2019, we live in a world where we are augmenting our soldiers and military units with AR. We are increasing living in a world of deep fakes, and the weaponization of AI appears to have no limit. A.I. technology has for years led military leaders to ponder a future of warfare that needs little human involvement yet in 2019 impressively, it’s how consumers are under a dark threat of a repressive authoritarian internet. In China, its internet can bar millions from travel for ‘social credit’ offences. Meanwhile new apps weaponize idol submissiveness to the state. We already know Facebook and other American tech companies practice privacy invasion and 3rd party data harvesting at a nightmarish scenario of exploiting user data.

As I am moving into the area of AI Safety within the field of artificial intelligence (AI) I find myself both confused and perplexed. Where do you even start? I covered the financial developments in OpenAI yesterday, and they are one of the foremost authorities on AI Safety. As such I thought it would be interesting to look at one of their papers. The paper that I will be looking at is called AI safety via debate published October 2018. You can of course read the article yourself in arXiv, and critique my article in turn; that would be the ideal situation. This debate about AI debates is of course ongoing.

With the advent of Alexa, Google Assistant, Siri, and Alibaba and Baidu killing it in smart speaker adoption in China, consumer voice AI is eating the world, but to what end? Case in point, Alexa echo devices don’t make much of a profit for Amazon on hardware sales. It’s always been about the software ecosystem and the add-on value it could create. The longer-term goal could be to make money off an app marketplace through skills. ‘Skills’ are in a sense what Alexa calls its app store.

Artificial intelligence is on a par with human experts when it comes to making medical diagnoses based on images, a review has found. The potential for artificial intelligence in healthcare has caused excitement, with advocates saying it will ease the strain on resources, free up time for doctor-patient interactions and even aid the development of tailored treatment. Last month the government announced £250m of funding for a new NHS artificial intelligence laboratory.

In many practical scenarios, a population is divided into disjoint groups for better administration, e.g., electorates into political districts, employees into departments, students into school districts, and so on. However, grouping people arbitrarily may lead to biased partitions, raising concerns of gerrymandering in political districting, racial segregation in schools, etc. To counter such issues, in this paper, we conceptualize such problems in a voting scenario, and propose FAIR DISTRICTING problem to divide a given set of people having preference over candidates into k groups such that the maximum margin of victory of any group is minimized. We also propose the FAIR CONNECTED DISTRICTING problem which additionally requires each group to be connected. We show that the FAIR DISTRICTING problem is NP-complete for plurality voting even if we have only 3 candidates but admits polynomial time algorithms if we assume k to be some constant or everyone can be moved to any group. In contrast, we show that the FAIR CONNECTED DISTRICTING problem is NP-complete for plurality voting even if we have only 2 candidates and k = 2. Finally, we propose heuristic algorithms for both the problems and show their effectiveness in UK political districting and in lowering racial segregation in public schools in the US.

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system’s approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality – while duly respecting all ethical and legal concerns – to reap full benefits of ML.

A recent poll found that most Americans think algorithms are unfair. Unfortunately, the poll was itself biased and an example of the very phenomenon it decries. All around us, algorithms are invisibly at work. They’re recommending music and surfacing news, finding cancerous tumors, and making self-driving cars a reality. But do people trust them? Not really, according to a Pew Research Center survey taken last year. When asked whether computer programs will always reflect the biases of their designers, 58 percent of respondents thought they would. This finding illustrates a serious tension between computing technology, whose influence on people’s lives is only expected to grow, and the people affected by it.

Better information on workers’ skills attainment, employers’ skills needs, and educational institutions’ programs to increase skills is an essential element across all of these focus areas. The AWPAB’s Data Transparency working group has identified interoperable learning records (ILRs) as a novel and technically feasible, achievable way to communicate skills between workers, employers, and education and training institutions.

Five billion dollars. That’s the apparent size of Facebook’s latest fine for violating data privacy. While many believe the sum is simply a slap on the wrist for a behemoth like Facebook, it’s still the largest amount the Federal Trade Commission has ever levied on a technology company. Facebook is clearly still reeling from Cambridge Analytica, after which trust in the company dropped 51%, searches for ‘delete Facebook’ reached 5-year highs, and Facebook’s stock dropped 20%. While incumbents like Facebook are struggling with their data, startups in highly-regulated, ‘Third Wave’ industries can take advantage by using a data strategy one would least expect: ethics. Beyond complying with regulations, startups that embrace ethics look out for their customers’ best interests, cultivate long-term trust – and avoid billion dollar fines. To weave ethics into the very fabric of their business strategies and tech systems, startups should adopt ‘agile’ data governance systems. Often combining law and technology, these systems will become a key weapon of data-centric Third Wave startups to beat incumbents in their field.

AI agents are increasingly deployed and used to make automated decisions that affect our lives on a daily basis. It is imperative to ensure that these systems embed ethical principles and respect human values. We focus on how we can attest to whether AI agents treat users fairly without discriminating against particular individuals or groups through biases in language. In particular, we discuss human unconscious biases, how they are embedded in language, and how AI systems inherit those biases by learning from and processing human language. Then, we outline a roadmap for future research to better understand and attest problematic AI biases derived from language.

Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.

On September 17, Seth Vargo – a former employee of Chef, the software deployment automation company – found out via a tweet that Chef licenses had been sold to the Immigration and Customs Enforcement Agency (ICE) under a $95,500, one-year contract through the approved contractor C&C International Computers & Consultants. In protest, Vargo decided to ‘archive’ the GitHub repository for two open source Chef add-ons he had developed in the Ruby programming language. On his GitHub repository page, Vargo wrote, ‘I have a moral and ethical obligation to prevent my source from being used for evil.’

Seth Vargo wrote code used in a platform called Chef. When he learned ICE was a customer, he wrestled with ICE using code he had personally written. Technologist Seth Vargo had a moral dilemma. He had just found out that Immigration and Customs Enforcement (ICE), which has faced widespread condemnation for separating children from their parents at the U.S. border and other abuses, was using a product that contained code that he had written. ‘I was having trouble sleeping at night knowing that software – code that I personally authored – was being sold to and used by such a vile organization,’ he told Motherboard in an online chat. ‘I could not be complicit in enabling what I consider to be acts of evil and violations of our most basic human rights.’

This is the first part of our special feature series on Deepfakes, exploring the latest developments and implications in this nascent field of AI. We will be covering detailed implementations on generation and countering strategies in future articles, please stay tuned to GradientCrescent to learn more.

I recently came across this panel discussion from AAAI conference, 1984 and weirdly enough it felt both ancient yet relevant all at the same time and I was hoping I can throw my thoughts in this pit too.

We present a brief overview of the CALLector project, and consider ethical questions arising from its overall goal of creating a social network to support creation and use of online CALL resources. We argue that these questions are best addressed in a decentralised, pluralistic open source architecture.

Machine learning is often viewed as an inherently value-neutral process: statistical tendencies in the training inputs are ‘simply’ used to generalize to new examples. However when models impact social systems such as interactions between humans, these patterns learned by models have normative implications. It is important that we ask not only ‘what patterns exist in the data?’, but also ‘how do we want our system to impact people?’ In particular, because minority and marginalized members of society are often statistically underrepresented in data sets, models may have undesirable disparate impact on such groups. As such, objectives of social equity and distributive justice require that we develop tools for both identifying and interpreting harms introduced by models.

The widespread use of machine learning models, especially within the context of decision-making systems impacting individuals, raises many ethical issues with respect to fairness and interpretability of these models. While the research in these domains is booming, very few works have addressed these two issues simultaneously. To solve this shortcoming, we propose FairCORELS, a supervised learning algorithm whose objective is to learn at the same time fair and interpretable models. FairCORELS is a multi-objective variant of CORELS, a branch-and-bound algorithm, designed to compute accurate and interpretable rule lists. By jointly addressing fairness and interpretability, FairCORELS can achieve better fairness/accuracy tradeoffs compared to existing methods, as demonstrated by the empirical evaluation performed on real datasets. Our paper also contains additional contributions regarding the search strategies for optimizing the multi-objective function integrating both fairness, accuracy and interpretability.

The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. We report the initial results of this system in a proof-of-concept study and outline plans for future work.

Technological change has been reshaping human life and work for centuries. The mechanization that began with the Industrial Revolution enabled dramatic improvements in human health, well-being, and quality of life – not only in the developed countries of the West, but increasingly throughout the world. At the same time, economic and social disruptions often accompanied those changes, with painful and lasting results for workers, their families, and communities. Along the way, valuable skills, industries, and ways of life were lost. Ultimately new and unforeseen occupations, industries, and amenities took their place. But the benefits of these upheavals often took decades to arrive. And the eventual beneficiaries were not necessarily those who bore the initial costs. The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras. New and emerging technologies will raise aggregate economic output and boost the wealth of nations. Will these developments enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity? The answers to these questions are not predetermined. They depend upon the institutions, investments, and policies that we deploy to harness the opportunities and confront the challenges posed by this new era. How can we move beyond unhelpful prognostications about the supposed end of work and toward insights that will enable policymakers, businesses, and people to better navigate the disruptions that are coming and underway? What lessons should we take from previous epochs of rapid technological change? How is it different this time? And how can we strengthen institutions, make investments, and forge policies to ensure that the labor market of the 21st century enables workers to contribute and succeed?

The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc is considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science community has been ignoring so far? The answer is yes – in the pursuit of accuracy, most models sacrifice ‘fairness’ and ‘interpretability.’ Rarely, a data scientist tries to dissect a model to find out if the model follows all ethical norms. This is where machine learning fairness and interpretability of models come into being.

A friend shared the article in the New Yorker The Hidden Costs of Automated Thinking Jonathan Zittrain. He refers first to the drug industry or pharmaceutical business and how not all drugs are fully understood – beyond that they are working. He draws this parallel towards the discussion regarding automation and artificial intelligence, machine learning techniques in particular. He mentions that ‘theory-free’ advances can be indispensable to the development of life-saving drugs, however, it comes with a cost. He mentioned altering a few pixels in photograph to fool an algorithm, and that systems can have unknown gaps. In this article, I would like to first attempt to understand slightly better who Jonathan is and secondly a reflection on this concept of intellectual debt. The subtitle of this piece is taken from one of the headings of Jonathan Zittrain’s post on Medium called Intellectual Debt: With Great Power Comes Great Ignorance. As a quick disclaimer, these texts are short reflections as part of my project #500daysofAI and as such will not be comprehensive, it is a process of learning every day about the topic.

When policy makers want to regulate AI, they must first define what AI is. However, legal definitions differ significantly from definitions of other disciplines. They are working definitions. Courts must be able to determine precisely whether or not a concrete system is considered AI by the law. In this paper we examine how policy makers should define the material scope of AI regulations. We argue that they should not use the term ‘artificial intelligence’ for regulatory purposes because there is no definition of AI which meets the requirements for legal definitions. Instead, they should define certain designs, use cases or capabilities following a risk-based approach. The goal of this paper is to help policy makers who work on AI regulations.

The idea of paying people for their data is increasingly seen as a promising direction for resolving privacy debates, improving the quality of online data, and even offering an alternative to labor-based compensation in a future dominated by automation and self-operating machines. In this paper we demonstrate how a Human-Centric Data Economy would compensate the users of an online streaming service. We borrow the notion of the Shapley value from cooperative game theory to define what a fair compensation for each user should be for movie scores offered to the recommender system of the service. Since determining the Shapley value exactly is computationally inefficient in the general case, we derive faster alternatives using clustering, dimensionality reduction, and partial information. We apply our algorithms to a movie recommendation data set and demonstrate that different users may have a vastly different value for the service. We also analyze the reasons that some movie ratings may be more valuable than others and discuss the consequences for compensating users fairly.

Designing solutions for people with disabilities offers a peephole into the future. ‘It’s just the right thing to do.’ Very few people think that those of us who are blind should be exiled from the web altogether, or that people with hearing loss shouldn’t have iPhones. That’s as it should be. But all too often, the importance of accessibility – the catch-all term for designing technology that people with disabilities can use – is framed in terms of charity alone. And that’s a shame because it makes accessibility seem grudging and boring, when the reality is that it’s the most exciting school of design on the planet.

How do new scientific disciplines get started? For Iyad Rahwan, a computational social scientist with self-described ‘maverick’ tendencies, it happened on a sunny afternoon in Cambridge, Massachusetts, in October 2017. Rahwan and Manuel Cebrian, a colleague from the MIT Media Lab, were sitting in Harvard Yard discussing how to best describe their preferred brand of multidisciplinary research. The rapid rise of artificial intelligence technology had generated new questions about the relationship between people and machines, which they had set out to explore. Rahwan, for example, had been exploring the question of ethical behavior for a self-driving car – should it swerve to avoid an oncoming SUV, even if it means hitting a cyclist? – in his Moral Machine experiment.

Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision criteria can produce counter-intuitive results, e.g. that the optimal constrained boundary may reject intuitively better candidates due to demographic imbalance in similar candidates. Both approaches can be seen as introducing individual resentment, where some individuals would have received a better outcome if they either belonged to a different demographic class and had the same qualifications, or if they remained in the same class but had objectively worse qualifications (e.g. lower test scores). We show that both forms of resentment can be avoided by using monotonically constrained machine learning models to create individually fair, demographically balanced classifiers.

Sarah Bird discusses the major challenges of responsible AI development and examines promising new tools and technologies to help enable it in practice.

Artificial intelligence (AI) is a grand challenge for computer science. Lifetimes of effort and billions of dollars have powered its pursuit. Yet, today its most ambitious vision remains unmet: though progress continues, no human-competitive general digital intelligence is within our reach. However, such an elusive goal is exactly what we expect from a ‘grand challenge’ – it’s something that will take astronomical effort over expansive time to achieve – and is likely worth the wait. There are other grand challenges, like curing cancer, achieving 100% renewable energy, or unifying physics. Some fields have entire sets of grand challenges, such as David Hilbert’s 23 unsolved problems in mathematics, which laid down the gauntlet for the entire 20th century. What’s unusual, though, is for there to be a problem whose solution could radically alter our civilization and our understanding of ourselves while being known only to the smallest sliver of researchers. Despite how strangely implausible that sounds, it is precisely the scenario today with the challenge of open-endedness. Almost no one has even heard of this problem, let alone cares about its solution, even though it is among the most fascinating and profound challenges that might actually someday be solved. With this article, we hope to help fix this surprising disconnect. We’ll explain just what this challenge is, its amazing implications if solved, and how to join the quest if we’ve inspired your interest.

Statistical inference, reinforcement learning, deep neural networks, and other jargon has recently attracted much attention, and indeed, for a fundamental reason. Statistical inference extends the basis of our decisions and changes the deliberative process in making decisions. This change constitutes the essential differentiator from what I name as the pre-data science to the subsequent data science era. In the data science era, decisions are taken based on data and algorithms. Often, decisions are made solely by algorithms and humans constitute an important actor only in the process of gathering, cleaning, structuring the data and setting up the framework for the algorithm selection (often, the algorithm itself is chosen by a metric). Given this fundamental change, it is important to take a closer look at both the extended base of decisions and the changes in thought processes in deliberation of this extended base when taking decisions in the data science era.

This report identifies and names the Alternative Influence Network (AIN): an assortment of scholars, media pundits, and internet celebrities who use YouTube to promote a range of political positions, from mainstream versions of libertarianism and conservatism, all the way to overt white nationalism. Content creators in the AIN claim to provide an alternative media source for news and political commentary. They function as political influencers who adopt the techniques of brand influencers to build audiences and ‘sell’ them on far-right ideology. This report presents data from approximately 65 political influencers across 81 channels. This network is connected through a dense system of guest appearances, mixing content from a variety of ideologies. This cross-promotion of ideas forms a broader ‘reactionary’ position: a general opposition to feminism, social justice, or left-wing politics.

According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered.

Technology is becoming a bigger part of our lives every day. Imagine driving to a new place without your sat-nav, ordering a takeaway without an app, or finding a new place to eat it without a quick search on Google. Now think about the things you don’t see. How Google orders the billions of results it finds when you ask it ‘Why is the sky blue?’ or ‘Where is Dubai?’ (searched 165,000 and 60,500 times a month on average)?

The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development. We demonstrate that features derived from the text of user comments are useful for identifying suspect activity, but lead to increased erroneous identifications when keywords over-represented in past influence campaigns are present. Drawing on research in native language identification (NLI), we use ‘named entity masking’ (NEM) to create sentence features robust to this shortcoming, while maintaining comparable classification accuracy. We demonstrate that while NEM consistently reduces false positives when key named entities are mentioned, both masked and unmasked models exhibit increased false positive rates on English sentences by Russian native speakers, raising ethical considerations that should be addressed in future research.

Could part of AI Safety be ensuring distribution or work towards equality? I have written before about fairness in AI; the importance of data quality; and equality relating to gender. Yet the most challenging article to write was Inequalities and AI. Is artificial intelligence truly safe if it worsens or exacerbates inequality? What is one of the greatest inequalities? It has been important for nonprofits to connect to makers of new technology to see if any part of the revenue can be funnelled towards a humanitarian purpose or programs. As much as we can question these technologies, because they are of course not faultless, it is arguably important that nonprofits are able to raise funds and address issues. The question for these organisations is often a large looming ‘how’? In an ideal world their operations would not be needed, yet in the current situation there is a place for the charity sector, and how they operate is certainly changing. With these services moving to apps or social media with a variety of actors it does seem a challenge to keep up. In many instances the technologies such as AI or ML are integrated into existing products or services. Is it necessary to collaborate? We proceed with the assumption that it can be generated in conjunction with machine learning projects and that part of the money should go to charity. Let us explore a few options, but first a quick look at AI for Good.

We often view AI with suspicion – but AI can be used to solve complex problems currently facing society where innovative approaches are needed. For many of us, the Amazon fires are disturbing and a serious problem because the Amazon cannot be recovered once its gone. It seems that there is nothing we can do to mitigate this man-made (and economically driven) disaster. However, I believe that in the very near future we can. And the solution maybe to create a spirit of activism through transparent algorithms to bring about social change. Two technologies could be key – and they are both currently viewed with some suspicion.

Before you start reading, think of 3 possible scenarios for the future of Artificial Intelligence (AI). If I asked you to think of 3 possible scenarios for the future of AI, I am guessing you’d think of the bad first: Takeover scenario – Terminator-style. Computers and robots dominate human species, take over our planet, and eventually wipe us off the face of Earth. Or, that the power of AI will be held, and used by a handful of tyrants whose sole purpose is to enslave the rest of us. You might’ve also thought of a hybrid scenario, where we lose some of our humanity to gain far superior computational and physical power. And finally, you might’ve even thought of brighter days where robots work for human species who now enjoy their Universal Basic Income (UBI), follow their ‘passions’ or their ‘useless’ creative endeavors, and live without a single worry in the world.

Understanding the behavior of artificial intelligence(AI) agents is one of the pivotal challenges of the next decade of AI. Interpretability or explainability are some of the terms often used to describe methods that provide insights about the behavior of AI programs. Until today, most of the interpretability techniques have focused on exploring the internal structure of deep neural networks. Recently, a group of AI researchers from the Massachusetts Institute of Technology(MIT) are exploring a radical approach that attempts to explain the behavior of AI observing them in the same we study human or animal behavior. They group the ideas in this area under the catchy name of machine behavior which promises to be one of the most exciting fields in the next few years of AI.

President-elect of the European Commission, Ursula von der Leyen made clear in her recently unveiled policy agenda, that not only will artificial intelligence (AI) be a key component of European digital strategy, but the cornerstone of the European AI plan will be to develop ‘AI made in Europe’ that is more ethical than AI made anywhere else in the world. What this means is not always clear, since there is no universal consensus on ethics. However, most European policymakers are less concerned about the ‘what’ and more about the ‘why.’ As explained by former Vice-President for the Digital Single Market, Andrus Ansip, ‘Ethical AI is a win-win proposition that can become a competitive advantage for Europe.’ This idea that Europe can become the global leader in AI simply by creating the most ethical AI systems, rather than by competing to build the best-performing ones, has become the conventional wisdom in Brussels, repeated ad nauseum by those tasked with charting a course for Europe’s AI future. But it is a delusion built on three fallacies: that there is a market for AI that is ethical-by-design, that other countries are not interested in AI ethics, and that Europeans have a competitive advantage in producing AI systems that are more ethical than those produced elsewhere.

Civilizations evolve through strategic forgetting of once-vital life skills. But can machines do all our remembering? When I was a student, in the distant past when most computers were still huge mainframes, I had a friend whose PhD advisor insisted that he carry out a long and difficult atomic theory calculation by hand. This led to page after page of pencil scratches, full of mistakes, so my friend finally gave in to his frustration. He snuck into the computer lab one night and wrote a short code to perform the calculation. Then he laboriously copied the output by hand, and gave it to his professor. Perfect, his advisor said – this shows you are a real physicist. The professor was never any the wiser about what had happened. While I’ve lost touch with my friend, I know many others who’ve gone on to forge successful careers in science without mastering the pencil-and-paper heroics of past generations.

Towards a more responsible development of artificial intelligence with a research paper from OpenAI. The 10th of July team members of OpenAI released a paper on arXiv called The Role of Cooperation in Responsible AI Development by Amanda Askell, Miles Brundage and Gillian Hadfield. One of the main statements in the article goes as follows: ‘Competition between AI companies could decrease the incentives of each company to develop responsibly by increasing their incentives to develop faster. As a result, if AI companies would prefer to develop AI systems with risk levels that are closer to what is socially optimal – as we believe many do – responsible AI development can be seen as a collective action problem’ Therefore how is it proposed we approach this problem?

Big Data powering Big Money, the return of direct democracy, and the tyranny of the minority. Nowadays, artificial intelligence (AI) is one of the most widely discussed phenomena. AI is poised to fundamentally alter almost every dimension of human life – from healthcare and social interactions to military and international relations. However, it is worth considering the effects of the advent of AI in politics – since politics are one of the fundamental pillars of today’s societal system, and understanding the dangers that AI poses for politics is crucial to combat AI’s negative implications, while at the same time maximizing the benefits stemming from the new opportunities in order to strengthen democracy.

Systems that augment sensory abilities are increasingly employing AI and machine learning (ML) approaches, with applications ranging from object recognition and scene description tools for blind users to sound awareness tools for d/Deaf users. However, unlike many other AI-enabled technologies, these systems provide information that is already available to non-disabled people. In this paper, we discuss unique AI fairness challenges that arise in this context, including accessibility issues with data and models, ethical implications in deciding what sensory information to convey to the user, and privacy concerns both for the primary user and for others.

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leveraging historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small-cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials.

Using adversarial machine learning, researchers can trick machines – potentially with fatal consequences. But the legal system hasn’t caught up. Imagine you’re cruising in your new Tesla, autopilot engaged. Suddenly you feel yourself veer into the other lane, and you grab the wheel just in time to avoid an oncoming car. When you pull over, pulse still racing, and look over the scene, it all seems normal. But upon closer inspection, you notice a series of translucent stickers leading away from the dotted lane divider. And to your Tesla, these stickers represent a non-existent bend in the road that could have killed you. In April this year, a research team at the Chinese tech giant Tencent showed that a Tesla Model S in autopilot mode could be tricked into following a bend in the road that didn’t exist simply by adding stickers to the road in a particular pattern. Earlier research in the U.S. had shown that small changes to a stop sign could cause a driverless car to mistakenly perceive it as a speed limit sign. Another study found that by playing tones indecipherable to a person, a malicious attacker could cause an Amazon Echo to order unwanted items.

The development of artificial general intelligence offers tremendous benefits and terrible risks. There is no easy definition for artificial intelligence, or A.I. Scientists can’t agree on what constitutes ‘true A.I.’ versus what might simply be a very effective and fast computer program. But here’s a shot: intelligence is the ability to perceive one’s environment accurately and take actions that maximize the probability of achieving given objectives. It doesn’t mean being smart, in a sense of having a great store of knowledge, or the ability to do complex mathematics.

No one can escape the fact that trying to legislate on the existing connection between man and machine will lead us to the serious problem of shifting our customs and morality into the field of ethics. In fact, it would be foolish to think that any social pact marked between humans and humanoids would not be reflected through human rights, especially when humanoids themselves were exclusively artificial. The starting point is to analyze which are the inherent human rights acquired by the machines so as to be able to consider which aspect is concerned with morality. So one way to simplify the essay is to postulate that the 1948 Charter of Human Rights could form part of the ethical principles that define not only human beings, but also the very concept of humanity.

Algorithms and big data are entering the often shrouded world of alternative dispute resolution. Robots and artificial intelligence seem worlds away from the sensitive and nuanced area of international mediation. Here, battles are largely settled behind closed doors and skilled mediators pick their way through sticky negotiations. Algorithms and big data, however, are fast entering the often mystery-shrouded world of alternative dispute resolution. This is much the result of the rapidly increasing demand for the kind of data analytics being harnessed in US litigation to predict trial outcomes. The incursion of robots into mediation hit a new milestone in February, when Canadian electronic negotiation specialists iCan Systems reputedly became the first company to resolve a dispute in a public court in England and Wales using a ‘robot mediator’.

We study the fair allocation of indivisible goods under the assumption that the goods form an undirected graph and each agent must receive a connected subgraph. Our focus is on well-studied fairness notions including envy-freeness and maximin share fairness. We establish graph-specific maximin share guarantees, which are tight for large classes of graphs in the case of two agents and for paths and stars in the general case. Unlike in previous work, our guarantees are with respect to the complete-graph maximin share, which allows us to compare possible guarantees for different graphs. For instance, we show that for biconnected graphs it is possible to obtain at least $3/4$ of the maximin share, while for the remaining graphs the guarantee is at most $1/2$. In addition, we determine the optimal relaxation of envy-freeness that can be obtained with each graph for two agents, and characterize the set of trees and complete bipartite graphs that always admit an allocation satisfying envy-freeness up to one good (EF1) for three agents. Our work demonstrates several applications of graph-theoretical tools and concepts to fair division problems.

Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability

Computational Politics is the study of computational methods to analyze and moderate users\textquotesingle behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to a massive number of users joining the online communities and to the digitization of analogous data such as political debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase the users\textquotesingle participation and interaction. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies at one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

Research Findings:
• There is a diversity crisis in the AI sector across gender and race.
• The AI sector needs a profound shift in how it addresses the current diversity crisis.
• The overwhelming focus on ‘women in tech’ is too narrow and likely to privilege white women over others.
• Fixing the ‘pipeline’ won’t fix AI’s diversity problems.
• The use of AI systems for the classification, detection, and prediction of race and gender is in urgent need of re-evaluation.

From massive face-recognition-based surveillance and machine-learning-based decision systems predicting crime recidivism rates, to the move towards automated health diagnostic systems, artificial intelligence (AI) is being used in scenarios that have serious consequences in people’s lives. However, this rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial face recognition systems have much higher error rates for dark skinned women while having minimal errors on light skinned men. A 2016 ProPublica investigation uncovered that machine learning based tools that assess crime recidivism rates in the US are biased against African Americans. Other studies show that natural language processing tools trained on newspapers exhibit societal biases (e.g. finishing the analogy ‘Man is to computer programmer as woman is to X’ by homemaker). At the same time, books such as Weapons of Math Destruction and Automated Inequality detail how people in lower socioeconomic classes in the US are subjected to more automated decision making tools than those who are in the upper class. Thus, these tools are most often used on people towards whom they exhibit the most bias. While many technical solutions have been proposed to alleviate bias in machine learning systems, we have to take a holistic and multifaceted approach. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

The ethical implications of algorithmic systems have been much discussed in both HCI and the broader community of those interested in technology design, development and policy. In this paper, we explore the application of one prominent ethical framework – Fairness, Accountability, and Transparency – to a proposed algorithm that resolves various societal issues around food security and population ageing. Using various standardised forms of algorithmic audit and evaluation, we drastically increase the algorithm’s adherence to the FAT framework, resulting in a more ethical and beneficent system. We discuss how this might serve as a guide to other researchers or practitioners looking to ensure better ethical outcomes from algorithmic systems in their line of work.

You don’t need to come up with an AI ethics framework out of thin air. Here are five of the best resources to get technology and ethics leaders started.
• Future of Life Institute
• IAPP
• IEEE
• The Public Voice
• EU Council of Europe

I was contacted by a friend who is helping to host an event at a large business conference in Norway where industry and politicians meet. The name of the event is ‘Artificial Intelligence – Ethics vs. World Domination?’. In this context I was asked a few questions and I will do my best to answer. I will however first discuss a series of questions I was sent relating to the topic. These questions concern competitiveness, human-centric AI, Norwegian interests and social responsible AI. First let us begin with the description of the event.

Nearly every tech conference right now has at least one, if not many, sessions about ethics: Ethics in artificial intelligence, introductions to data ethics, why letting the internet go to sleep is the ethical thing to do, or just plain integrating the basics of ethics into your design. We as a community are doing a great job raising questions about the implications of technology and spreading awareness to our communities about the potential for harm.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Fluxus Landscape is an art and research project mapping about 500 stakeholders and actors in AI ethics and governance. It casts a broad net and each included stakeholder defines artificial intelligence and ethics in their own terms. Together, they create a snapshot of the organic structure of social change – showing us that development at speed can create vortices of thought and intellectual dead zones open to exploitation.

Fluxus Landscape is an art and research project created in partnership with the Center for the Advanced Study in the Behavioral Sciences (CASBS) at Stanford University with support from the Stanford Institute for Human-Centered Artificial Intelligence.

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory. We introduce a revised BayesAct model that more deeply integrates social-psychological theorising, and we demonstrate a key component of the model as being sufficient to account for cognitive biases about fairness, dissonance and conformity. We close with ethical and philosophical discussion.

The Future of Life Institute (FLI) has appeared across various articles and areas within the field of artificial intelligence, at least where I have looked. They seem to be concerned with the unknown future and how it affects us. Since I have been exploring the topic of AI Safety it does now make sense seeing as FLI has funded a series of different projects throughout the last five years particularly with two rounds, both funded by Elon Musk and different research institutes. The first round seems to have been in 2015 with a focus on AI Safety Researchers and the second round with its focus on artificial general intelligence (AGI) Safety Researchers in 2018. Since the project summaries are all out online I decided to have a think about each in turn.

Recent advancements in artificial intelligence (AI) and its sub-branch machine learning (ML) promise machines that go beyond the boundaries of automation and behave autonomously. Applications of these machines in creative practices such as art and design entail relationships between users and machines that have been described as a form of collaboration or co-creation between computational and human agents. This paper uses examples from art and design to argue that this frame is incomplete as it fails to acknowledge the socio-technical nature of AI systems, and the different human agencies involved in their design, implementation, and operation. Situating applications of AI-enabled tools in creative practices in a spectrum between automation and autonomy, this paper distinguishes different kinds of human engagement elicited by systems deemed automated or autonomous. Reviewing models of artistic collaboration during the late 20th century, it suggests that collaboration is at the core of these artistic practices. We build upon the growing literature of machine learning and art to look for the human agencies inscribed in works of computational creativity, and expand the co-creation frame to incorporate emerging forms of human-human collaboration mediated through technical artifacts such as algorithms and data.

A rich literature has explored the modeling of homophily and other forms of nonuniform mixing associated with individual-level covariates within the exponential family random graph (ERGM) framework. Such differential mixing does not fully explain phenomena such as stigma, however, which involve the active maintenance of social boundaries by ostracism of persons with out-group ties. Here, we introduce a new statistic that allows for such effects to be captured, making it possible to probe for the potential presence of boundary maintenance above and beyond simple differences in nomination rates. We demonstrate this statistic in the context of gender segregation in a school classroom.

We discuss the organisation of internet communities, focusing on what we call the principle of ‘bait and switch’: founders of internet communities often find it advantageous to recruit members by promising inducements which are later not honoured. We look at some of the dilemmas and ways of attempting to resolve them through two paradigmatic examples, Wikispaces and WordPress. Our analysis is to a large extent motivated by the demands of CALLector, a university-centred social network we are in the process of establishing. We consider the question of what ethical standards are imposed on universities engaged in this type of activity.

More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and $\textit{Ethics guidelines for trustworthy AI}$ from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.

Future robots should follow human social norms in order to be useful and accepted in human society. In this paper, we leverage already existing social knowledge in human societies by capturing it in our framework through the notion of social norms. We show how norms can be used to guide a reinforcement learning agent towards achieving normative behavior and apply the same set of norms over different domains. Thus, we are able to: (1) provide a way to intuitively encode social knowledge (through norms); (2) guide learning towards normative behaviors (through an automatic norm reward system); and (3) achieve a transfer of learning by abstracting policies; Finally, (4) the method is not dependent on a particular RL algorithm. We show how our approach can be seen as a means to achieve abstract representation and learn procedural knowledge based on the declarative semantics of norms and discuss possible implications of this in some areas of cognitive science.

Knowledge Tracing (KT) is to trace the knowledge of students as they solve a sequence of problems represented by their related skills. This involves abstract concepts of students’ states of knowledge and the interactions between those states and skills. Therefore, a KT model is designed to predict whether students will give correct answers and to describe such abstract concepts. However, existing methods either give relatively low prediction accuracy or fail to explain those concepts intuitively. In this paper, we propose a new model called Knowledge Query Network (KQN) to solve these problems. KQN uses neural networks to encode student learning activities into knowledge state and skill vectors, and models the interactions between the two types of vectors with the dot product. Through this, we introduce a novel concept called \textit{probabilistic skill similarity} that relates the pairwise cosine and Euclidean distances between skill vectors to the odds ratios of the corresponding skills, which makes KQN interpretable and intuitive. On four public datasets, we have carried out experiments to show the following: 1. KQN outperforms all the existing KT models based on prediction accuracy. 2. The interaction between the knowledge state and skills can be visualized for interpretation. 3. Based on probabilistic skill similarity, a skill domain can be analyzed with clustering using the distances between the skill vectors of KQN. 4. For different values of the vector space dimensionality, KQN consistently exhibits high prediction accuracy and a strong positive correlation between the distance matrices of the skill vectors.

The singularity refers to an idea that once a machine having an artificial intelligence surpassing the human intelligence capacity is created, it will trigger explosive technological and intelligence growth. I propose to test the hypothesis that machine intelligence capacity can grow autonomously starting with an intelligence comparable to that of bacteria – microbial intelligence. The goal will be to demonstrate that rapid growth in intelligence capacity can be realized at all in artificial computing systems. I propose the following three properties that may allow an artificial intelligence to exhibit a steady growth in its intelligence capacity: (i) learning with the ability to modify itself when exposed to more data, (ii) acquiring new functionalities (skills), and (iii) expanding or replicating itself. The algorithms must demonstrate a rapid growth in skills of dataprocessing and analysis and gain qualitatively different functionalities, at least until the current computing technology supports their scalable development. The existing algorithms that already encompass some of these or similar properties, as well as missing abilities that must yet be implemented, will be reviewed in this work. Future computational tests could support or oppose the hypothesis that artificial intelligence can potentially grow to the level of superintelligence which overcomes the limitations in hardware by producing necessary processing resources or by changing the physical realization of computation from using chip circuits to using quantum computing principles.

As Microsoft seeks to make data-sharing across companies easier and more pervasive, company officials have seen areas where roadblocks can occur. Prevalent among these are the lack of consistent, standardized data-sharing terms and licensing agreements. On July 23, the company took a first potential step toward remedying this gap. Microsoft is making publicly available today the first drafts of three proposed data-sharing agreements. It is looking for community feedback and input on them over the next few months. Each of the three is designed for particular data-sharing scenarios between companies — not individuals — and is covered by the Creative Commons license. Some of these agreements will be published on Microsoft’s GitHub code-sharing site. Microsoft officials said they believe these kinds of agreements could alleviate the need of companies to spend months or years negotiating and creating data-sharing governance agreements.