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PHD Thesis Proposal - Sina Shaham

Thesis Proposal - Sina Shaham

Bhaskar Krishnamachari (Supervisor)
Srivatsan Ravi
Cyrus Shahabi
Barath Raghavan
Cauligi Raghavendra

Abstract:

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Information privacy focuses on an individual's right to maintain a certain level of control over how their personal data is gathered and utilized and fairness aims to ensure that the revealed user information is handled fairly and equitably. Without careful attention to upholding privacy and ensuring fair representation in ML models, community members could face serious setbacks, including unfavorable employment decisions, inequitable loan distribution, and the misuse of personal information. Therefore, this thesis focuses on how ML can be employed for communities fairly and privately. In particular, it addresses the following: (I) strategies to protect individual privacy in diverse communities while employing a location-based alert system, (II) ways to determine geospatial boundaries of communities to ensure their fair treatment in ML models, (III) the design of responsible electricity demand response programs for communities.

Venue:
In-person: EEB 539 or over Zoom: https://usc.zoom.us/j/93666858332

Location: EEB 539 or over Zoom: https://usc.zoom.us/j/93666858332

Audiences: Everyone Is Invited

Contact: Asiroh Cham

Event Link: https://usc.zoom.us/j/93666858332