Difference between revisions of "Meeting Schedule"
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| − | | 02/28/2024 || Kathy McKeowan & Robin Burke | + | | 02/28/2024 || Kathy McKeowan & short talk by Robin Burke |
| − | Title: Multistakeholder fairness in recommender systems | + | Kathy's web page: ''' https://www.cs.columbia.edu/~kathy/ |
| + | |||
| + | Title of Robin Burke talk: Multistakeholder fairness in recommender systems | ||
Abstract: Research in machine learning fairness makes two key simplifying assumptions that have proven challenging to move beyond. One assumption is that we can productively concentrate on a uni-dimensional version of the problem: achieving fairness for a single protected group defined by a single sensitive feature. The second assumption is that technical solutions need not engage with the essentially political nature of claims surrounding fairness. I argue that relaxing these assumptions is necessary for machine learning fairness to achieve practical utility. While some recent research in rich subgroup fairness has considered ways to relax the first assumption, these approaches require that fairness be defined in the same way for all groups, which amounts to a hardening of the second assumption. In this talk, I argue for a formulation of machine learning fairness based on social choice and exemplify the approach in the area of recommender systems. Social choice is inherently multi-agent, escaping the single group assumption and, in its classic formulation, places no constraints on agents' preferences. In addition, social choice was developed to formalize political decision-making mechanisms, such as elections, and therefore offers some hope of directly addressing the inherent politics of fairness. Social choice has complexities of its own, however, and the talk will outline a research agenda aimed at understanding the challenges and opportunities afforded by this approach to machine learning fairness. | Abstract: Research in machine learning fairness makes two key simplifying assumptions that have proven challenging to move beyond. One assumption is that we can productively concentrate on a uni-dimensional version of the problem: achieving fairness for a single protected group defined by a single sensitive feature. The second assumption is that technical solutions need not engage with the essentially political nature of claims surrounding fairness. I argue that relaxing these assumptions is necessary for machine learning fairness to achieve practical utility. While some recent research in rich subgroup fairness has considered ways to relax the first assumption, these approaches require that fairness be defined in the same way for all groups, which amounts to a hardening of the second assumption. In this talk, I argue for a formulation of machine learning fairness based on social choice and exemplify the approach in the area of recommender systems. Social choice is inherently multi-agent, escaping the single group assumption and, in its classic formulation, places no constraints on agents' preferences. In addition, social choice was developed to formalize political decision-making mechanisms, such as elections, and therefore offers some hope of directly addressing the inherent politics of fairness. Social choice has complexities of its own, however, and the talk will outline a research agenda aimed at understanding the challenges and opportunities afforded by this approach to machine learning fairness. | ||
Revision as of 14:56, 21 February 2024
Location: Hybrid - Buchanan 430, and the zoom link below
Time: Wednesdays at 10:30am, Mountain Time
Zoom link: https://cuboulder.zoom.us/j/97014876908
| Date | Title |
|---|---|
| 01/24/2024 | Planning, introductions, welcome! |
| 01/31/2024 | Brunch Social |
| 02/07/2024 | No Meeting - Virtual PhD Open House |
| 02/14/2024 | ACL paper clinic |
| 02/21/2024 | Cancelled in favor of LING Circle talk by Professor Gibbs
|
| 02/28/2024 | Kathy McKeowan & short talk by Robin Burke
Kathy's web page: https://www.cs.columbia.edu/~kathy/ Title of Robin Burke talk: Multistakeholder fairness in recommender systems Abstract: Research in machine learning fairness makes two key simplifying assumptions that have proven challenging to move beyond. One assumption is that we can productively concentrate on a uni-dimensional version of the problem: achieving fairness for a single protected group defined by a single sensitive feature. The second assumption is that technical solutions need not engage with the essentially political nature of claims surrounding fairness. I argue that relaxing these assumptions is necessary for machine learning fairness to achieve practical utility. While some recent research in rich subgroup fairness has considered ways to relax the first assumption, these approaches require that fairness be defined in the same way for all groups, which amounts to a hardening of the second assumption. In this talk, I argue for a formulation of machine learning fairness based on social choice and exemplify the approach in the area of recommender systems. Social choice is inherently multi-agent, escaping the single group assumption and, in its classic formulation, places no constraints on agents' preferences. In addition, social choice was developed to formalize political decision-making mechanisms, such as elections, and therefore offers some hope of directly addressing the inherent politics of fairness. Social choice has complexities of its own, however, and the talk will outline a research agenda aimed at understanding the challenges and opportunities afforded by this approach to machine learning fairness. Bio: Information Science Department Chair and Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. Professor Burke is the author of more than 150 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others. |
| 03/06/2024 | Jon's Proposal |
| 03/13/2024 | Veronica Qing Lyu, Faithful Chain of Thought Reasoning |
| 03/20/2024 | Cory Paik's Area Exam |
| 03/27/2024 | No Meeting - Spring Break |
| 04/03/2024 | CLASIC Industry Day |
| 04/10/2024 | Rehan's Dissertation Defense |
| 04/17/2024 | Maggie's Proposal |
| 04/24/2024 | Téa's Senior Thesis Defense |
| 05/01/2024 | Sagi's Proposal
|
Past Schedules
- Fall 2023 Schedule
- Spring 2023 Schedule
- Fall 2022 Schedule
- Spring 2022 Schedule
- Fall 2021 Schedule
- Spring 2021 Schedule
- Fall 2020 Schedule
- Spring 2020 Schedule
- Fall 2019 Schedule
- Spring 2019 Schedule
- Fall 2018 Schedule
- Summer 2018 Schedule
- Spring 2018 Schedule
- Fall 2017 Schedule
- Summer 2017 Schedule
- Spring 2017 Schedule
- Fall 2016 Schedule
- Spring 2016 Schedule
- Fall 2015 Schedule
- Spring 2015 Schedule
- Fall 2014 Schedule
- Spring 2014 Schedule
- Fall 2013 Schedule
- Summer 2013 Schedule
- Spring 2013 Schedule
- Fall 2012 Schedule
- Spring 2012 Schedule
- Fall 2011 Schedule
- Summer 2011 Schedule
- Spring 2011 Schedule
- Fall 2010 Schedule
- Summer 2010 Schedule
- Spring 2010 Schedule
- Fall 2009 Schedule
- Summer 2009 Schedule
- Spring 2009 Schedule
- Fall 2008 Schedule
- Summer 2008 Schedule
