Carlos Castillo
Bio: Carlos Castillo (he/him) is an ICREA Research Professor at Universitat Pompeu Fabra in Barcelona, where he leads the Web Science and Social Computing research group. He is a web miner with a background in information retrieval and has been influential in the areas of crisis informatics, web content quality and credibility, and adversarial web search. He is a prolific, highly cited researcher who has co-authored over 100 publications in top-tier international conferences and journals, receiving two test-of-time awards, five best paper awards, and two best student paper awards. His works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search. Twitter: ChaToX.
Keynote talk 1: Algorithmic fairness in link-based recommenders
Link-based recommender systems are a key feature of many of the online platforms we interact with every day. They recommend us people to connect with, as well as content and products to consume. We will begin by attempting to describe precisely what does it mean to discriminate in the context of recommendation: to produce disadvantageous differential treatment affecting members of some groups. Then, we will discuss how recommender systems might produce this of discrimination. In the final part, we will focus on link-based recommenders, as they might be strongly affected by groups that might be homophilic (i.e., having a strong tendency to link among them), which under various settings attract more recommendations at the expense of other groups. The talk will be based on the PhD work of David Solans and Francesco Fabbri.
Sanmi Koyejo
Bio: Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and spends time at Google as a part of the Brain team. Koyejo’s research interests are in developing the principles and practice of trustworthy machine learning, focusing on applications to neuroscience and healthcare. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Skip Ellis Early Career Award, a Sloan Fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo is serving as the co-general chair of NeurIPS 2022 and is president of the Black in AI organization.
Keynote talk 2: Towards algorithms for measuring and mitigating ML unfairness
It is increasingly evident that widely-deployed machine learning (ML) models can lead to discriminatory outcomes and exacerbate group disparities. The renewed interest in measuring and mitigating (un)fairness has led to various metrics and mitigation strategies. Nevertheless, the measurement problem remains challenging, as existing metrics may not capture tradeoffs relevant to the context at hand, and different fairness definitions can lead to incompatible outcomes. To this end, I will outline metric elicitation as a framework for addressing this metric selection problem — by efficiently estimating implicit preferences from stakeholders via interactive feedback. Towards mitigation, I will briefly outline emerging approaches for overlapping groups, unknown sensitive attributes, and other scenarios beyond the most widely studied settings.
Panellists:
Topic: Current Challenges to Responsible AI Panellists: Yoshua Bengio, Joelle Pineau, Carlos Castillo, Sanmi Koyejo, Negar Rostamzadeh, Fenwick McKelvey
Yoshua Bengio
Bio: Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.
In 2019, he was awarded the prestigious Killam Prize and in 2022, became the computer scientist with the highest h-index in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.
Joelle Pineau
Bio: Joelle Pineau is an Associate Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She is a core academic member of Mila and a Canada CIFAR AI chairholder. She is also co-Managing Director of Facebook AI Research. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau’s research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Machine Learning Research and is Past-President of the International Machine Learning Society. She is a recipient of NSERC’s E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR), a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada, and a 2019 recipient of the Governor General’s Innovation Awards.
Negar Rostamzadeh
Bio: Negar Rostamzadeh is a Research Scientist at Google Ethical AI team, where she studies the social impact of machine learning technologies and evaluation systems. Prior to that, Negar was a research scientist at Element AI, where she worked on efficient sampling and labeling approaches in a variety of multimedia and computer vision problems. Negar obtained her PhD from the University of Trento, where she was advised by Nicu Sebe. Her main area of research during her PhD was on large scale video understanding problems. During her PhD, she spent two years at MILA, where she worked on attention mechanism in videos, video captioning and generation under the supervision of Aaron Courville. Negar was a co-founder of Women in Deep Learning (WiDL) in 2016, and a co-organizer of WiML, WiCV and WiDL in 2017.
Fenwick McKelvey
Bio: Fenwick McKelvey is an Associate Professor in Information and Communication Technology Policy in the Department of Communication Studies at Concordia University. He is co-director of the Applied AI Institute and leads Machine Agencies at the Milieux Institute. He studies digital politics and policy, appearing frequently as an expert commentator in the media and intervening in media regulatory hearings. He is the author of Internet Daemons: Digital Communications Possessed (University of Minnesota Press, 2018) winner of the 2019 Gertrude J. Robinson Book Award. He is co-author of The Permanent Campaign: New Media, New Politics (Peter Lang, 2012) with Greg Elmer and Ganaele Langlois. He is a member of the Educational Review Committee of the Walrus Magazine.
Roundtable leads
Topic: Fairness and Algorithmic harms
Fernando Diaz is a research scientist at Google Research Montréal. Fernando’s research focuses on the design of information access systems, including search engines, music recommendation services and crisis response platforms. He is particularly interested in understanding and addressing the societal implications of artificial intelligence more generally. Previously, Fernando was the assistant managing director of Microsoft Research Montréal, where he also led FATE Montréal, and a director of research at Spotify, where he helped establish its research organization on recommendation, search, and personalization. Fernando’s work has received special recognition and awards at SIGIR, CIKM, CSCW, WSDM, ISCRAM, and ECIR. He is the recipient of the 2017 British Computer Society Karen Spärck Jones Award and holds a CIFAR AI Chair. Fernando has co-organized several NIST TREC tracks, WSDM (2013), Strategic Workshop on Information Retrieval (2018), FAT* (2019), SIGIR (2021), and the CIFAR Workshop on Artificial Intelligence and the Curation of Culture (2019). He received his BS in Computer Science from the University of Michigan Ann Arbor and his MS and PhD from the University of Massachusetts Amherst.
Su Lin Blodgett is a researcher in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research Montréal. Her research focuses on the ethical and social implications of language technologies, focusing on the complexities of language and language technologies in their social contexts, and on supporting NLP practitioners in their ethical work. She completed her Ph.D. in computer science at the University of Massachusetts Amherst.
Topic: Interpretability /Explainability /Transparency
Q. Vera Liao is a Principal Researcher at Microsoft Research Montréal, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. Her current research interests are in human-AI interaction, explainable AI, and responsible AI. Prior to joining MSR, she worked at IBM Research and studied at the University of Illinois at Urbana-Champaign and Tsinghua University.
Chris Srinivasa is a Senior Research Team Lead at Borealis AI focused on the Safe, Ethical, Responsible development and deployment of machine learning models in industrial settings. His interests lie at the intersection of technical topics such as robustness, fairness, explainability, and beyond, and bringing these to practical settings such as regulatory frameworks, policy, and litigation. He obtained his PhD with the Probabilistic and Statistical Inference group at the University of Toronto.
Topic: Security & Privacy
Sébastien Gambs holds the Canada Research Chair in Privacy and Ethical Analysis of Massive Data since December 2017 and has been a professor in the Department of Computer Science at the Université du Québec à Montréal since January 2016. His main research theme is privacy in the digital world. He is also interested in solving long-term scientific questions such as the existing tensions between massive data analysis and privacy as well as ethical issues such as fairness, transparency and algorithmic accountability raised by personalized systems.
Ulrich Aïvodji is an Assistant Professor of Computer Science at ETS Montreal in the Software and Information Technology Engineering Department. He is also a regular member of the International Observatory on the Societal Impacts of AI and Digital Technologies. His research areas of interest are computer security, data privacy, optimization, and machine learning. His current research focuses on several aspects of trustworthy machine learning, such as fairness, privacy-preserving machine learning, and explainability.
Topic: Robustness
Nicolas Le Roux is a researcher at Microsoft Research in Montreal with expertise in machine learning, neural networks, optimization, large-scale learning and statistical modeling in general. He is also an adjunct faculty at both McGill and Université de Montréal.
Aishwarya Agrawal is an Assistant Professor at the University of Montreal, and a core academic member of Mila. She also spends one day a week at DeepMind. Aishwarya’s research is on multimodal vision-language learning focussing on the problems of out-of-distribution, data-efficient and compositional generalization. The Visual Question Answering (VQA) work by Aishwarya and her colleagues has witnessed tremendous interest in a short period of time. Aishwarya is a recipient of the Canada CIFAR AI Chair Award, Georgia Tech Sigma Xi Best Ph.D. Thesis Award, and Georgia Tech College of Computing Dissertation Award.
Topic: Ethics in AI
AJung Moon specializes in ethics and responsible design of AI and interactive robotic systems. She directs the Responsible Autonomy & Intelligent System Ethics (RAISE) lab at McGill. Her previous work at the UN exploring tech governance issues and running an AI ethics startup inspires her work today.
Siva Reddy is an Assistant Professor at McGill University and a Facebook CIFAR AI Chair at Mila. His research focuses on representation learning for language that facilitates systematic generalization, reasoning and conversational modeling. He received the 2020 VentureBeat AI Innovation Award in NLP for his work in bias in large language models, and the best paper award at EMNLP 2021 for his work in multi-cultural NLP.
Topic: Accountability
Dominique Payette LL.B. J.D., LL.M. is a technology and AI lawyer, and the Responsible AI Strategy Lead at Borealis AI, a Royal Bank of Canada organization. She advises on the responsible and ethical deployment of artificial intelligence, and actively researches the topics of fairness, explainability and accountability in AI in banking. She is a proactive advocate for such matters in the finance industry and community. Namely, she’s participated in industry-wide publications, round tables with regulators, and does pro bono legal work with start-ups, and graduate students.
Fenwick McKelvey is an Associate Professor in Information and Communication Technology Policy in the Department of Communication Studies at Concordia University. He is co-director of the Applied AI Institute and leads Machine Agencies at the Milieux Institute. He studies digital politics and policy, appearing frequently as an expert commentator in the media and intervening in media regulatory hearings. He is the author of Internet Daemons: Digital Communications Possessed (University of Minnesota Press, 2018) winner of the 2019 Gertrude J. Robinson Book Award. He is co-author of The Permanent Campaign: New Media, New Politics (Peter Lang, 2012) with Greg Elmer and Ganaele Langlois. He is a member of the Educational Review Committee of the Walrus Magazine.
Montreal AI Symposium / Symposium IA Montréal 