Fwd: Thesis Proposal - Maria De Arteaga Gonzalez - Wednesday, February 5 at 2pm - NSH 3305

[Artur Dubrawski]

Heads-up Autonians:

Maria will be giving her thesis proposal talk tomorrow at 2pm in our area
(NSH 3305).
Please come and enjoy a fun talk, and support one of our own going through
this important
stage of their career.

Cheers
Artur

---------- Forwarded message ---------
From: Michelle E Wirtz <mwirtz at andrew.cmu.edu>
Date: Thu, Jan 30, 2020 at 11:25 AM
Subject: Thesis Proposal - Maria De Arteaga Gonzalez - Wednesday, February
5 at 2pm - NSH 3305
To: heinz-faculty at lists.andrew.cmu.edu <heinz-faculty at lists.andrew.cmu.edu>,
heinz-phd at lists.andrew.cmu.edu <heinz-phd at lists.andrew.cmu.edu>, Artur W.
Dubrawski <awd at andrew.cmu.edu>, Roni Rosenfeld <roni at cs.cmu.edu>,
adam.kalai at microsoft.com <adam.kalai at microsoft.com>


Hi all,

Please join us on Wednesday, February 5, 2020 in Newell-Simon Hall Room
3305 at 2pm when Maria De Arteaga Gonzalez will be presenting her thesis
proposal.

*Title:* Machine Learning in High-Stakes Settings: Risks and Opportunities

*Thesis committee: *Artur Dubrawski (co-chair), Alexandra Chouldechova
(co-chair), Roni Rosenfeld, Adam Kalai (Microsoft Research)



*Thesis proposal abstract:*

Machine learning (ML) is increasingly being used to support decision-making
in critical settings, where predictions have potentially grave implications
over human lives. Examples include healthcare, hiring, child welfare, and
the criminal justice system. In this thesis, I study the risks and
opportunities of machine learning in high-stakes settings. In the first
chapter I focus on opportunities of ML to support experts' decisions when
dealing with high-resolution multivariate data--a type of data that is
particularly hard for humans to interpret--. I propose methodology to
discover latent complex multivariate correlation structures and illustrate
its use in two different domains: (1) identification of radioactive threats
in nuclear physics, and (2) prediction of neurological recovery of comatose
patients in healthcare. In the second chapter, focused on algorithmic
fairness, I demonstrate how societal biases encoded in historical data may
be reproduced and amplified by ML models, and introduce a new algorithm to
mitigate biases without assuming access to protected attributes. Finally,
in the third chapter I characterize challenges that arise from the
limitations of available labels in decision support contexts, such as the
selective labels problem and omitted payoff bias, and propose methodology
to estimate and leverage human consistency to improve algorithmic decision
making.



*Link to paper: *
https://www.dropbox.com/s/0sedkolbwim23x9/PhD_Proposal_DeArteaga.pdf?dl=0
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