Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron (previously Deep Science), aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, researchers discovered a method that could allow adversaries to track the movements of remotely-controlled robots even when the robots' communications are encrypted end-to-end. The coauthors, who hail from the University of Strathclyde in Glasgow, said that their study shows adopting the best cybersecurity practices isn't enough to stop attacks on autonomous systems. Remote control, or teleoperation, promises to enable operators to guide one or several robots from afar in a range of environments.

Look, we all know that algorithms are biased. What matters is how they are biased. What data helped train the algorithm? What weights and preferences did the data scientist ascribe to different features when designing the algorithm? As an end user, it's often impossible to know. But when we blame the algorithms, more often than not we're abdicating our basic responsibility to articulate and pursue a specific policy goal.

As we start to encounter AI systems in various morally and legally salient environments, some have begun to explore how the current responsibility ascription practices might be adapted to meet such new technologies [19, 33]. A critical viewpoint today is that autonomous and self-learning AI systems pose a so-called responsibility gap [27]. These systems' autonomy challenges human control over them [13], while their adaptability leads to unpredictability. Hence, it might infeasible to trace back responsibility to a specific entity if these systems cause any harm. Considering responsibility practices as the adoption of certain attitudes towards an agent [40], scholarly work has also posed the question of whether AI systems are appropriate subjects of such practices [15, 29, 37] -- e.g., they might "have a body to kick," yet they "have no soul to damn" [4].

Big decisions about people's lives are increasingly made by software systems and algorithms. Sorting résumés for job applications, allocating social services, and deciding who sees advertisements for open positions, housing, and products are just a few of the ways in which these software systems shape our lives. While algorithmic decision-making can offer benefits in terms of speed, efficiency, and even fairness, bias is routinely introduced into software systems in many ways, including the use of biased training data. Often "black boxes" with little transparency or accountability, algorithms can unfairly limit opportunities, restrict services, and produce "technological redlining"–a form of digital data discrimination that uses digital identities and activities to bolster inequality and oppression. Algorithmic Accountability: A Primer explores issues of algorithmic accountability, or the process of assigning responsibility for harm when algorithmic decision-making results in discriminatory and inequitable outcomes.

Artificial intelligence (AI) algorithms have emerged in the educational domain as a tool to make learning more efficient. Different applications for mastering particular skills, learning new languages, and tracking their progress are used by children. What is the impact on children from using this smart technology? We conducted a systematic review to understand the state of the art. We explored the literature in several sub-disciplines: wearables, child psychology, AI and education, school surveillance, and accountability. Our review identified the need for more research for each established topic. We managed to find both positive and negative effects of using wearables, but cannot conclude if smart technology use leads to lowering the young children's performance. Based on our insights we propose a framework to effectively identify accountability for smart technology in education.

In the first week of November, Apple and Goldman Sachs got a bit of unwanted attention when @DHH, the famous creator of Ruby on Rails (and a Le Mans 24h race class winning driver) accused them of gender discrimination. Case in point: He and his wife applied for Apple Cards together and received a credit limit 20 times of was given for his wife. This, when they file joint taxes and she has a better credit score. The Tweet went viral, but things got even more heated when the other "Steve" of Apple, @stevewoz backed the claim. But we are not discussing the troubles of Apple and Goldman Sachs after this incident, and the subsequent legal inquiry that was ordered.

We've reached a point where human (cognitive) task performance is being leveraged or even replaced by AI. So who or what is responsible for what this AI does? While the question seems simple enough, legal answers from the field are apparently opaque and embroiled. This is caused by the fact that AI is performing human-like tasks without having the clear legal accountability of one, and the question is whether it should have any. Fortunately, now that machine learning and artificial intelligence are protruding on an ever-increasing amount of practical domains, real-world legal interpretations and guiding principles are forming around the topic.

Algorithms increasingly govern our social world, transforming data into scores or rankings that decide who gets credit, jobs, dates, policing, and much more. The field of "algorithmic accountability" has arisen to highlight the problems with such methods of classifying people, and it has great promise: Cutting-edge work in critical algorithm studies applies social theory to current events; law and policy experts seem to publish new articles daily on how artificial intelligence shapes our lives, and a growing community of researchers has developed a field known as "Fairness, Accuracy, and Transparency in Machine Learning." The social scientists, attorneys, and computer scientists promoting algorithmic accountability aspire to advance knowledge and promote justice. But what should such "accountability" more specifically consist of? At a two-day, interdisciplinary roundtable on AI ethics I recently attended, such questions featured prominently, and humanists, policy experts, and lawyers engaged in a free-wheeling discussion about topics ranging from robot arms races to computationally planned economies.

The country that wins the global race for dominance in artificial intelligence stands to capture enormous economic benefits, including potentially doubling its economic growth rates by 2035. Unfortunately, the United States is getting bad advice about how to compete. Over the past year, Canada, China, France, India, Japan, and the United Kingdom have all launched major government-backed initiatives to compete in AI. While the Trump administration has begun to focus on how to advance the technology, it has not developed a cohesive national strategy to match that of other countries. This has allowed the conversation about how policymakers in the United States should support AI to be dominated by proposals from advocates primarily concerned with staving off potential harms of AI by imposing restrictive regulations on the technology, rather than supporting its growth.

While artificial intelligence was once heralded as the key to unlocking a new era of economic prosperity, policymakers today face a wave of calls to ensure AI is fair, ethical and safe. New York City Mayor de Blasio recently announced the formation of the nation's first task force to monitor and assess the use of algorithms. Days later, the European Union enacted sweeping new data protection rules that require companies be able to explain to consumers any automated decisions. And high-profile critics, like Elon Musk, have called on policymakers to do more to regulate AI. Unfortunately, the two most popular ideas -- requiring companies to disclose the source code to their algorithms and explain how they make decisions -- would cause more harm than good by regulating the business models and the inner workings of the algorithms of companies using AI, rather than holding these companies accountable for outcomes. The first idea -- "algorithmic transparency" -- would require companies to disclose the source code and data used in their AI systems.