2026-09-18T22:55:10Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1584972025-04-07T09:07:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Gifford, David K.
Dai, Zheng
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-03-12T16:55:41Z
2025-03-12T16:55:41Z
2024-09
2025-03-04T18:29:58.798Z
https://hdl.handle.net/1721.1/158497
In the year 2024, the prospect of solving human level tasks using intelligent systems is no longer the subject of science fiction. As these systems play an increasingly critical role in our day-to-day lives, it becomes ever more important to consider the safety and ethics surrounding their implementation. This is a multifaceted challenge spanning multiple disciplines, involving questions at the regulatory, engineering, and theoretical levels. This thesis discusses three projects that span these levels. We first explore the problem of tracing causal influence from training data to outputs of generative models. In our exploration we encounter the phenomenon of unattributability, and consider its scientific and regulatory implications. We next tackle the challenge of designing a high diversity library of therapeutics that is depleted of dangerous off-target binders using intelligent systems, developing a suite of inference and optimization tools along the way. Finally, we derive universal bounds for the robustness of image classifiers that inform us of how safe these intelligent systems can be in theory. Together, these projects present a multilevel overview of the safe and ethical implementation of intelligent systems.
Ph.D.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Safe and Ethical Implementation of Intelligent Systems
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>In the year 2024, the prospect of solving human level tasks using intelligent systems is no longer the subject of science fiction. As these systems play an increasingly critical role in our day-to-day lives, it becomes ever more important to consider the safety and ethics surrounding their implementation. This is a multifaceted challenge spanning multiple disciplines, involving questions at the regulatory, engineering, and theoretical levels. This thesis discusses three projects that span these levels. We first explore the problem of tracing causal influence from training data to outputs of generative models. In our exploration we encounter the phenomenon of unattributability, and consider its scientific and regulatory implications. We next tackle the challenge of designing a high diversity library of therapeutics that is depleted of dangerous off-target binders using intelligent systems, developing a suite of inference and optimization tools along the way. Finally, we derive universal bounds for the robustness of image classifiers that inform us of how safe these intelligent systems can be in theory. Together, these projects present a multilevel overview of the safe and ethical implementation of intelligent systems.</Abstract>
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