<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div dir="ltr"></div><div dir="ltr">Reminder this is happening today!</div><div dir="ltr"><br><blockquote type="cite">On Oct 29, 2023, at 4:01 PM, Asher Trockman <ashert@cs.cmu.edu> wrote:<br><br></blockquote></div><blockquote type="cite"><div dir="ltr"><div dir="ltr">Dear all,<div><br></div><div><div>We look forward to seeing you <b>this Tuesday (10/31)</b> from <b><font color="#ff0000">1</font></b><font color="#ff0000"><b>2:00-1:00 PM (U.S. Eastern time)</b></font> for the next talk of this semester's <b>CMU AI Seminar</b>, sponsored by <a href="https://sambanova.ai/" target="_blank">SambaNova Systems</a>. The seminar will be held in GHC 6115 <b>with pizza provided </b>and will<b> </b>be streamed on Zoom. (Note: the speaker will be virtual, but we will stream the talk in the room.)</div><div><br></div><div>To learn more about the seminar series or to see the future schedule, please visit the <a href="http://www.cs.cmu.edu/~aiseminar/" target="_blank">seminar website</a>.</div><div><br></div><font color="#0b5394"><span style="background-color:rgb(255,255,0)">On this Tuesday (10/31), <u>Abhin Shah</u> </span><span style="background-color:rgb(255,255,0)">(MIT) will be giving a talk titled </span><b style="background-color:rgb(255,255,0)">"</b><span style="background-color:rgb(255,255,0)"><b>Group Fairness with Uncertainty in Sensitive Attributes</b></span></font><b style="color:rgb(11,83,148);background-color:rgb(255,255,0)">"</b><font color="#0b5394" style="background-color:rgb(255,255,0)">.</font></div><div><font color="#0b5394"><span style="background-color:rgb(255,255,0)"><br></span><b>Title</b>: Group Fairness with Uncertainty in Sensitive Attributes<br><br></font><div><font color="#0b5394"><b>Talk Abstract</b>: Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solving an optimization problem that maximizes the predictive power of the model under an appropriate group fairness constraint. However, in practice, sensitive attributes are often missing or noisy resulting in uncertainty. We demonstrate that solely enforcing fairness constraints on uncertain sensitive attributes can fall significantly short in achieving the level of fairness of models trained without uncertainty. To overcome this limitation, we propose a bootstrap-based algorithm that achieves better levels of fairness despite the uncertainty in sensitive attributes. The algorithm is guided by a Gaussian analysis for the independence notion of fairness where we propose a robust quadratically constrained quadratic problem to ensure a strict fairness guarantee with uncertain sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks for various group fairness notions, e.g., independence and separation.</font></div><font color="#0b5394"> </font><div><div><div><font color="#0b5394"><b>Speaker Bio:</b> Abhin Shah is a final-year Ph.D. student in EECS department at MIT advised by Prof. Devavrat Shah and Prof. Greg Wornell. He is a recipient of MIT’s Jacobs Presidential Fellowship. He interned at Google Research in 2021 and at IBM Research in 2020. Prior to MIT, he graduated from IIT Bombay with a Bachelor’s degree in Electrical Engineering. His research interests include theoretical and applied aspects of trustworthy machine learning with a focus on causality and fairness.</font></div><div><font color="#0b5394"><br></font></div><div><font color="#0b5394"><b>In person: </b>GHC 6115</font></div><div><font color="#0b5394"><b>Zoom Link</b>: <a href="https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09" target="_blank">https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09</a></font></div></div></div></div><div><br></div><div>Thanks,</div><div>Asher Trockman</div></div>
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