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 growth in computing capacity combined with the growth in the amount of data collected has revealed the exceptional modeling and data processing capabilities of machine learning. Great advances have been made for a series of specialized tasks such as speech recognition, translation, decision support systems, fault detection, image classification, etc. opening the door to many applications in health, industry, social sciences and many other fields. On the other hand, the automation of certain tasks or decisions raises a number of significant issues, such as the exacerbation of structural inequalities whereby in domains like predictive justice, hiring and finance, algorithms are leading to decisions that benefit mainly those who are already in power and further disadvantage minorities and vulnerable groups. Although automation is often thought of as a “simple” substitution, it reinforces existing biases because it encodes certain norms into complex lines of code that “obscure” the true nature of the problem (Carr, 2017; Dekker, & Woods, 2002, Surveillance Capitalism Zuboff, 2019). In addition, recommendation systems also embody harmful effects such as political destabilization, mental health issues, misinformation and disinformation. The resource intensive nature of the technology has also an effect on the the environment and ecosystems (Strubell et al, 2019). Recent advancements related to generative AI are broadening the gap between the pros and cons of the technology even further, and underscore the need to act towards a common denominator.

Throughout this course, we will debate how balance between the pros and cons of the technology can be achieved. This will be done through unpacking the main pillars of AI, such as fairness, privacy, transparency and accountability, both from a CS perspective and from a legal/ policy perspective. The literature about each one of those components is on the rise both in the CS/ ML domains, and in the social sciences; and even binding laws are starting to enter into force. 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 focus will be given to the need to address contextual considerations and the implementation of AI in different domains such as healthcare, finance, and criminal justice.

Presentation : here