Difference between revisions of "Meeting Schedule"
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Kathy's web page: ''' https://www.cs.columbia.edu/~kathy/ | Kathy's web page: ''' https://www.cs.columbia.edu/~kathy/ | ||
| − | Addressing Large Language Models that Lie: Case Studies in Summarization | + | Title: Addressing Large Language Models that Lie: Case Studies in Summarization |
Kathleen McKeown | Kathleen McKeown | ||
Columbia University | Columbia University | ||
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| − | Title | + | Title: Multistakeholder fairness in recommender systems |
| + | Robin Burke | ||
| + | University of Colorado Boulder | ||
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 10:29, 27 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 | Short talks by Kathy McKeowan and Robin Burke
Kathy's web page: https://www.cs.columbia.edu/~kathy/ Title: Addressing Large Language Models that Lie: Case Studies in Summarization Kathleen McKeown Columbia University The advent of large language models promises a new level of performance in generation of text of all kinds, enabling generation of text that is far more fluent, coherent and relevant than was previously possible. However, they also introduce a major new problem: they wholly hallucinate facts out of thin air. When summarizing an input document, they may incorrectly intermingle facts from the input, they may introduce facts that were not mentioned at all, and worse yet, they may even make up things that are not true in the real world. In this talk, I will discuss our work in characterizing the kinds of errors that can occur and methods that we have developed to help mitigate hallucination in language modeling approaches to text summarization for a variety of genres. Kathleen R. McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and the Founding Director of the Data Science Institute, serving as Director from 2012 to 2017. In earlier years, she served as Department Chair (1998-2003) and as Vice Dean for Research for the School of Engineering and Applied Science (2010-2012). A leading scholar and researcher in the field of natural language processing, McKeown focuses her research on the use of data for societal problems; her interests include text summarization, question answering, natural language generation, social media analysis and multilingual applications. She has received numerous honors and awards, including 2023 IEEE Innovation in Societal Infrastructure Award, American Philosophical Society Elected member, American Academy of Arts and Science elected member, American Association of Artificial Intelligence Fellow, a Founding Fellow of the Association for Computational Linguistics and an Association for Computing Machinery Fellow. Early on she received the National Science Foundation Presidential Young Investigator Award, and a National Science Foundation Faculty Award for Women. In 2010, she won both the Columbia Great Teacher Award—an honor bestowed by the students—and the Anita Borg Woman of Vision Award for Innovation.
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
