Limitations of Data-Based Decision Making: An Multiparadigmic Investigation of Challenges Faced by Artificial Intelligence
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Date
2025-02-24
Authors
Kinsey, Sarah
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
As big data and computing capability continue to grow, an ever-increasingamount of artificial intelligence approaches are being deployed in the real world,
across various domains. With real world deployments come additional complexities,
challenges, and vulnerabilities, particularly concerning data reliance. Among
others, these data related vulnerabilities include the potential for intelligently
sabotaged data, and incomplete data. This work explores vulnerabilities from three
perspectives: adversarial learning, game theory, and online reinforcement learning.
First, we investigate whether a directly targeted end-to-end poisoning attack on a
data-based decision making learning-planning model is feasible. Next, through the
lens of security games, we investigate how a data-based decision maker can form a
useful behavioral model, despite the observable data being maliciously manipulated.
Lastly, we examine how, in a real world online RL setting, limited and sparse data
can be overcome to build an effective data-based decision making model.
Description
Keywords
Adversarial Learning, Artificial Intelligence, Game Theory