Lionel C. Briand is a professor of software engineering and has shared appointments between The University of Ottawa, Canada (Canada Research Chair) and The SFI Lero Center for Software Research, University of Limerick, Ireland (Director). In collaboration with colleagues, for over 25 years, he has run many collaborative research projects with companies in the automotive, satellite, aerospace, energy, financial, and legal domains. Lionel has held various engineering, academic, and leading positions in six countries. 

Lionel was elevated to the grades of IEEE Fellow and ACM Fellow for his work on software testing and verification. He was granted the IEEE Computer Society Harlan Mills award, the ACM SIGSOFT outstanding research award, and the IEEE Reliability Society engineer-of-the-year award, respectively in 2012, 2022, and 2013. He further received an ERC Advanced grant in 2016 - on the topic of modelling and testing cyber-physical systems - which is the most prestigious individual research award in the European Union and was elected a fellow of the Academy of Science, Royal Society of Canada in 2023. He currently holds a Canada Research Chair (Tier 1) on "Intelligent Software Dependability and Compliance". His research interests include trustworthy AI, software testing and verification (including security aspects), applications of AI in software engineering, model-driven software development, requirements engineering, and empirical software engineering.

Abeba Birhane is a cognitive scientist, currently a Senior Advisor in AI Accountability at Mozilla Foundation, and an Adjunct Assistant Professor at the School of Computer Science and Statistics at Trinity College Dublin, Ireland. She researches human behaviour, social systems, and responsible and ethical AI – work for which she was recently featured in Wired UK and TIME on the TIME100 Most Influential People in AI list. Birhane also serves on the United Nations Secretary-General’s newly-convened AI Advisory Body.

Abstract: Research in Computer Vision tends to develop in a siloed manner from the downstream societal impacts, where the kind of applications that are built upon Computer Vision research and their uses have largely been ignored. In this talk, I discuss the downstream impact of AI research in general and Computer Vision in particular using empirical findings from analysis of three decades of Computer Vision research papers and downstream patents. I present a quantitative and rich qualitative analysis showing that Computer Vision research is powering mass surveillance. I highlight the ethical and societal implications of such work and the role that Computer Vision researchers might play in disrupting the ‘Computer Vision, surveillance’ pipeline.