In the legal world, and in particular the world of electronic discovery, artificial intelligence (AI) has been around for more than a decade.

I happened to come across a document, issued by the Department of Justice of the State of Israel, which opens a public consultation, regarding the regulation of self-driving vehicles. Dated February 8, it bears the title, translated of course, of "Towards the Regulation of the Use of Self-Driving Vehicles -- an Application for a Public Position in the Field of Liability and Insurance." The incipit of the paper is a valid invitation to continue reading: "Technological innovation is commonly seen as including one or more of the following components: autonomous, electric, connectivity, and cooperative, when each of these components is based on a large number of technologies with different characteristics. Perceptual change is commonly described as a transition from "transportation as a product" to "transportation as a service." As part of this concept, the transportation system is not a total of privately owned vehicles, but a collection of tools designed to provide transportation services (of human passengers or any other goods) from point A to point B in the most efficient way. From this, the existing relationship between the vehicle owner and the vehicle driver comes to be dismantled. Thus, in contrast to the current de facto situation, where there is often an identity between the vehicle owner and the driver, a separation will be created between the vehicle owner and the vehicle users."

Across machine learning (ML) sub-disciplines, researchers make explicit mathematical assumptions in order to facilitate proof-writing. We note that, specifically in the area of fairness-accuracy trade-off optimization scholarship, similar attention is not paid to the normative assumptions that ground this approach. Such assumptions presume that 1) accuracy and fairness are in inherent opposition to one another, 2) strict notions of mathematical equality can adequately model fairness, 3) it is possible to measure the accuracy and fairness of decisions independent from historical context, and 4) collecting more data on marginalized individuals is a reasonable solution to mitigate the effects of the trade-off. We argue that such assumptions, which are often left implicit and unexamined, lead to inconsistent conclusions: While the intended goal of this work may be to improve the fairness of machine learning models, these unexamined, implicit assumptions can in fact result in emergent unfairness. We conclude by suggesting a concrete path forward toward a potential resolution.

With current AI technologies, harm done by AIs is limited to power that we put directly in their control. As said in [59], "For Narrow AIs, safety failures are at the same level of importance as in general cybersecurity, but for AGI it is fundamentally different." Despite AGI (artificial general intelligence) still being well out of reach, the nature of AI catastrophes has already changed in the past two decades. Automated systems are now not only malfunctioning in isolation, they are interacting with humans and with each other in real time. This shift has made traditional systems analysis more difficult, as AI has more complexity and autonomy than software has before. In response to this, we analyze how risks associated with complex control systems have been managed historically and the patterns in contemporary AI failures to what kinds of risks are created from the operation of any AI system. We present a framework for analyzing AI systems before they fail to understand how they change the risk landscape of the systems they are embedded in, based on conventional system analysis and open systems theory as well as AI safety principles. Finally, we present suggested measures that should be taken based on an AI system's properties. Several case studies from different domains are given as examples of how to use the framework and interpret its results.

Ryan Calo is the Lane Powell and D. Wayne Gittinger Professor at the University of Washington School of Law. He is a founding co-director of the interdisciplinary UW Tech Policy Lab and the UW Center for an Informed Public. Professor Calo holds adjunct appointments at the University of Washington Information School and the Paul G. Allen School of Computer Science and Engineering. The following is a lightly edited transcript of a discussion that took place shortly after the publication of the European Commission's proposed new regulation of artificial intelligence (AI). The European Commission has today released a proposed regulation around AI. This is obviously something that you have been prepared for and waiting to see happen. What did the EU put out? Years ago I wrote a primer and roadmap for AI policy and also hosted the inaugural Obama White House workshop on artificial intelligence policy. Many of the themes of that essay and of the workshop were reflected in the EU proposal, which is to say that they're not limiting themselves to decision-making by AI. Their approach is to look at the impacts of AI holistically, and to tackle everything from liability should there be harm, to additional obligations for high-risk uses, to facial recognition and biometrics.

We have also seen that despite the discrepancies between countries from North to South, to Western countries to Eastern countries, there is a domino effect according to which major ethical issues and tendencies are almost simultaneously faced by every country at the same time, whatever their place in the AI race. It was the case for the AI tracking applications and the facial recognition applications during #COVID-19, and it will probably continue because AI is questioning the equilibrium of geopolitics worldwide. It also questions our ability to face fundamental and crucial questions as of the future of multilateralism. Below the translation of an article published in January, 2021 that highlights some trends I have foreseen in December 2020 for AI globally with some insights on the French market. Carrying out a prospective exercise is never easy and is even less so in the current context, which reminds us of the impermanence of all things and the need to adapt with agility, both individually and collectively, while keeping a long-term vision and without giving in to the call of falsely obvious and short-term choices.

Trust and ethics are at the heart of the new artificial intelligence (AI) strategy for Scotland, published by the Scottish Government this week. The strategy sets out the basic principles that will guide the development of AI and the actions that need to be taken over the next five years. The overall vision set out is for Scotland to become "a leader in the development and use of trustworthy, ethical and inclusive AI". The aim is to look beyond the technology to the role of AI in society. It says: "Much of what we take for granted today happens because AI is working behind the scenes, driving change and technological innovation on an unprecedented scale. "However, the use and adoption of AI should be on our terms if we are to build trust between the people of Scotland and AI." AI is technology that allows computers to perform actions that would normally require human intelligence, such as speech recognition or decision-making. It can be used from everything from diagnosing diseases to predicting what products you might be interested in based on your previous choices to self-driving cars. However, the strategy points out there are "real risks and concerns" that need addressed. AI decision-making can only be as good as the data and the algorithms that have been fed into it, so there can be concerns of bias arising from data or design and a lack of transparency in decision-making. Among the principles set out in the strategy is that AI should benefit people and planet, that "AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards" and that it should be transparent. One key outcome of the strategy will be the creation of an'AI Playbook', which will be a practical guide to how AI is done in Scotland. Another outcome is the setting up of the Scottish AI Alliance to provide leadership in this area. Other actions set out in the strategy include community engagement to encourage non-tech businesses to adopt AI, establishing an'AI for good' programme to help solve some of the challenges facing the country, upskilling and reskilling of the workforce, and the development of new data platforms and registers of trusted algorithms. There will be annual reviews of progress at the end of each year. Finance secretary Kate Forbes said: "Artificial intelligence offers huge economic and social potential and with Scotland's long history of academic excellence in its development we are building on strong foundations.

Some people think they are above the law. In a constitutional democracy this cannot be the case. Neither the head of state nor the doctor or the police are above the law. They should all be enabled to do their work, but we do not buy the claim that they could act as they wish. In 18th century Europe we replaced the authoritarian rule by law with a rule of law, to mitigate uninhibited power, and to ensure that those in power can be held to account in a court of law.

We all know what a professional is--or do we? For years, ACM has proclaimed that its members are part of a computing profession. But is it really a profession? Many people describe themselves as "professionals" in the colloquial sense of being paid to perform some specialized skill. Yet, only a few occupations are regarded as professions in the legal sense.

Nowadays, almost everybody is aware of the effect Artificial Intelligence (AI) has on our every day lives. AI is already a part of many people's lives and maybe already a part of your life too -- whether you realize it or not. Alexa), Google Home, and Apple's HomePod (with Siri) are perhaps the three most popular products in the thriving field of AI assistants. It's estimated that Amazon has sold about 25 million Echo devices up to now, and they expect that number to go double or more by 2020. These AI assistants products understand spoken commands and speak in humanlike voices using natural language.