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Alan Chan

Doctorat - UdeM
Superviseur⋅e principal⋅e
Nicolas Le Roux
Co-supervisor
David Scott Krueger
Sujets de recherche
Agentivité
IA digne de confiance
Sécurité de l'IA
Site web
Google Scholar

Publications

Characterizing Manipulation from AI Systems
Micah Carroll
Alan Chan
Henry Ashton
David Scott Krueger
Manipulation is a concern in many domains, such as social media, advertising, and chatbots. As AI systems mediate more of our digital intera… (voir plus)ctions, it is important to understand the degree to which AI systems might manipulate humans without the intent of the system designers. Our work clarifies challenges in defining and measuring this kind of manipulation from AI systems. Firstly, we build upon prior literature on manipulation and characterize the space of possible notions of manipulation, which we find to depend upon the concepts of incentives, intent, covertness, and harm. We review proposals on how to operationalize each concept and we outline challenges in including each concept in a definition of manipulation. Second, we discuss the connections between manipulation and related concepts, such as deception and coercion. We then analyze how our characterization of manipulation applies to recommender systems and language models, and give a brief overview of the regulation of manipulation in other domains. While some progress has been made in defining and measuring manipulation from AI systems, many gaps remain. In the absence of a consensus definition and reliable tools for measurement, we cannot rule out the possibility that AI systems learn to manipulate humans without the intent of the system designers. Manipulation could pose a significant threat to human autonomy and precautionary actions to mitigate it are likely warranted.
2023-10-30
Equity and Access in Algorithms, Mechanisms, and Optimization (publié)
doi.org
arxiv.org
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan
Benjamin Bucknall
Herbie Bradley
David Scott Krueger
2023-10-23
NeurIPS.cc/2023/Workshop/SoLaR (spotlight)
doi.org
openreview.net
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan
Benjamin Bucknall
Herbie Bradley
David Scott Krueger
2023-10-23
NeurIPS.cc/2023/Workshop/SoLaR (spotlight)
openreview.net
openreview.net
Harms from Increasingly Agentic Algorithmic Systems
Alan Chan
Rebecca Salganik
Alva Markelius
Chris Pang
Nitarshan Rajkumar
Dmitrii Krasheninnikov
Lauro Langosco
Zhonghao He
Yawen Duan
Micah Carroll
Michelle Lin
Alex Mayhew
Katherine Collins
Maryam Molamohammadi
John Burden
Wanru Zhao
Shalaleh Rismani
Konstantinos Voudouris
Umang Bhatt
Adrian Weller … (voir 2 de plus)
David Scott Krueger
Tegan Maharaj
Research in Fairness, Accountability, Transparency, and Ethics (FATE)1 has established many sources and forms of algorithmic harm, in domain… (voir plus)s as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed, typically without strong regulatory barriers, threatening the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms, rather than just responding to them. Anticipation of harms is especially important given the rapid pace of developments in machine learning (ML). Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency – notably, these include systemic and/or long-range impacts, often on marginalized or unconsidered stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
2023-06-12
2023 ACM Conference on Fairness, Accountability, and Transparency (publié)
doi.org
arxiv.org