Bias and Discrimination in AI

Golnoosh Farnadi serves as Assistant Professor at McGill University's School of Computer Science and Adjunct Professor at Université de Montréal, while holding a Canada CIFAR AI Chair at Mila. Her research journey began with a PhD in Computer Science from KU Leuven and Ghent University, focusing on user modeling in social media. Her work has evolved to address critical issues in AI ethics, particularly algorithmic fairness and responsible AI. As founder and principal investigator of the EQUAL lab at Mila/McGill, she develops novel algorithmic designs that incorporate fairness, robustness, and privacy considerations. Her contributions have been recognized through numerous awards, including the 2023 Google Excellence Award, 2021 Google Scholar Award, and Facebook Research Award for Privacy Enhancing Technologies. Her recent work includes collaborations with UNESCO on AI governance and development of privacy-preserving fair item ranking systems. As a visiting faculty researcher at Google and co-director of McGill's Collaborative for AI & Society, she continues to advance the field of ethical AI while advocating for more democratic and less centralized approaches to AI development.

Emre Kiciman serves as Senior Principal Research Manager at Microsoft Research, leading the AI for Industry team, where he combines expertise in causal machine learning with research on AI's societal implications. After earning his BS from UC Berkeley and PhD from Stanford University in Computer Science, he has established himself as a pioneer in applying machine learning to fault detection in large-scale systems. His research spans multiple domains, including the development of the DoWhy library for causal inference, which is now used across retail, e-commerce, and power grid industries. From 2018-2021, he served as founding co-chair of Microsoft's Aether working group on AI Security, leading efforts to enhance security processes and engineering practices. His current work focuses on broadening the use of causal methods across various domains while promoting positive AI applications and mitigating potential negative implications, particularly in areas such as health, mental health, data bias, and information discovery. His contributions to computational social science and AI security have made him a leading voice in understanding how new technologies affect societal awareness and enable novel forms of information retrieval.

Rachel Thomas serves as founding Director of the Center for Applied Data Ethics at the University of San Francisco and co-founder of fast.ai, where she has revolutionized access to deep learning education. After earning her PhD in mathematics from Duke University in 2010, where she focused on biochemical networks, she worked as a quantitative analyst at Exelon and later at Uber developing driver interface and surge algorithms. Her commitment to democratizing AI education led her to create the first university-accredited open access certificate in deep learning and establish fast.ai with Jeremy Howard. Her work focuses on making deep learning more accessible to people from diverse backgrounds, addressing unconscious bias in machine learning, and promoting ethical AI development. She has been particularly vocal about retention issues in tech, noting that 41% of women leave within 10 years, and advocates for greater diversity in AI to prevent biased systems. Her contributions earned her recognition from Forbes as one of the 20 most incredible women in artificial intelligence. Through her research and teaching, she continues to bridge the gap between technical expertise and ethical considerations in AI development while championing increased representation in the field.