<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 that this is happening today.</div><div dir="ltr"><br><blockquote type="cite">On Sep 15, 2023, at 1:53 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 (9/19)</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.</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 (9/19),<span class="gmail-Apple-converted-space"> </span><u>Keegan Harris</u> </span><span style="background-color:rgb(255,255,0)">(CMU) will be giving a talk titled </span><b style="background-color:rgb(255,255,0)">"</b><b style="background-color:rgb(255,255,0)">Algorithmic Decision-Making under Incentives: Apple Tasting Feedback and Multiclass Learnability</b><b style="background-color:rgb(255,255,0)">".</b></font></div><div><font color="#0b5394"><span style="background-color:rgb(255,255,0)"><br></span><b>Title</b>: Algorithmic Decision-Making under Incentives: Apple Tasting Feedback and Multiclass Learnability<br><br></font><div><font color="#0b5394"><b>Talk Abstract</b>: Algorithmic systems have recently been used to aid in or automate decision-making in high-stakes domains in order to, e.g. improve efficiency or reduce human bias. When subjugated to decision-making in these settings, decision-subjects (or agents) have an incentive to strategically modify their observable attributes in order to appear more qualified. Moreover, in many domains of interest (e.g. lending and hiring), the decision-maker only observes feedback if they assign a positive decision to an agent; this type of feedback is often referred to as apple tasting (or one-sided) feedback. In the first part of the talk, we examine the effects of apple tasting feedback in the online (binary) strategic classification setting. We provide several algorithms which achieve sublinear regret with respect to the best fix policy in hindsight if the agents were truthful (i.e. non-strategic). We also show how our results may be easily adapted to the setting where the decision-maker receives bandit feedback. Next, we shift our focus to the multiclass extension of strategic classification. Despite being well-motivated in settings such as e-commerce and medical domains, the multiclass version of the problem has received relatively little attention in the current literature on classification under incentives. Perhaps somewhat surprisingly, we show that unlike in the binary setting, strategyproof multiclass classification is generally not possible, even when full feedback is observed. This talk is based on two recent preprints: <a href="https://arxiv.org/pdf/2306.06250.pdf">https://arxiv.org/pdf/2306.06250.pdf</a>, <a href="https://arxiv.org/pdf/2211.14236.pdf">https://arxiv.org/pdf/2211.14236.pdf</a></font><div><div><font color="#0b5394"> </font><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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