This course will examine the mutual relationship between law, computer science and public policy. With the wide spread of artificial intelligence, the link between these domains is becoming more and more ambiguous. On one hand, the noticeable benefits that AI algorithms are bringing to our life are leading to a hype of AI, and both policy makers and engineers are seeing automation as a feasible solution for many longstanding societal issues. On the other hand, the gap between the disciplines is growing, misunderstanding of technical terms and concepts is leading lawyers and policy makers to oppose to the deployment of the technology in certain domains, or pressuring them to pass regulation that could hinder innovation. And the lack in understanding the complex socio-political context in which algorithms are operating in, as well as the laws and policies governing this domain, is leading computer scientists to develop solutions that are not compliant and can exacerbate existing biases. Therefore, bridging the gap and unpacking the requirements for the needed balance between the pros and cons is a key for successful implementation of the technology and governing it.

Throughout this course, we will debate how this balance can be achieved. This will be done through unpacking the main pillars of AI, both from a CS perspective and from a legal/ policy perspective. In recent years, many countries, private companies, standard bodies, and international organizations, have been calling for governing AI through principles. Those principles include for example fairness, accountability, transparency and human agency. The literature about each one of those components is on the rise both in the CS/ ML domains, and in the social sciences. In each session, we will dive into one of the components, discuss from a theoretical perspective what are the considerations that each domain is adding to the equation, and demonstrate how practically, using real life case studies, we can bridge the gap and merge them into a solution that addresses both. A special consideration will be given to the need to address contextual considerations and the implementation of AI in different domains such as healthcare, finance, the public sector, marketing and advertising, etc.