Abstract

With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that simulates a polarized discussion, and propose two inoculation strategies to reverse the negative effects of polarization. Next we address the problem where AI might inadvertently result in increasing social inequalities. In doing so, a generative adversarial network is proposed to generate synthetic tabular datasets that are fair with respect to protected attributes such as race, sex, etc. Finally, an encoder-decoder network is developed to modify image datasets in order to improve fair attribute classification while maintaining classification accuracy. The contributions of this dissertation include: 1) understanding the effects of AI algorithms on societal well-being in terms of polarization and inequalities arising from the use of these algorithms in (a) curating content for users in an online social network and (b) decision making in areas of significant impact on human life; 2) addressing some of these concerns by a) providing a model to generate synthetic data, leading to training fair classifiers, b) providing an image encoder-decoder network that achieves superior fairness-accuracy trade-off, with the advantage that it does not rely on modifying downstream classifiers, hence making it suitable to be deployed in an automated machine learning pipeline with lower cost, and c) providing solutions to address polarization/influence related concern.

Notes

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Graduation Date

2022

Semester

Fall

Advisor

Garibay, Ozlem

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0009401; DP0027124

URL

https://purls.library.ucf.edu/go/DP0027124

Language

English

Release Date

December 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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