[CMU AI Seminar] September 19 at 12pm (GHC 6115 & Zoom) -- Keegan Harris (CMU) -- Algorithmic Decision-Making under Incentives: Apple Tasting Feedback and Multiclass Learnability -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (9/19)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of this semester's *CMU AI Seminar*,
sponsored by SambaNova Systems <https://sambanova.ai/>. The seminar will be
held in GHC 6115 *with pizza provided *and will be streamed on Zoom.

To learn more about the seminar series or to see the future schedule,
please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

On this Tuesday (9/19), *Keegan Harris* (CMU) will be giving a talk titled
*"**Algorithmic Decision-Making under Incentives: Apple Tasting Feedback
and Multiclass Learnability**".*

*Title*: Algorithmic Decision-Making under Incentives: Apple Tasting
Feedback and Multiclass Learnability

*Talk Abstract*: 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: https://arxiv.org/pdf/2306.06250.pdf,
https://arxiv.org/pdf/2211.14236.pdf

*In person: *GHC 6115
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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