國立虎尾科技大學 |

Designing Behavior-Aware AI Systems to Influence Human Decision-Making /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Designing Behavior-Aware AI Systems to Influence Human Decision-Making // Guanghui Yu.
Author:	Yu, Guanghui,
Description:	1 electronic resource (166 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489653
ISBN:	9798383630419

Designing Behavior-Aware AI Systems to Influence Human Decision-Making /
Yu, Guanghui,

Designing Behavior-Aware AI Systems to Influence Human Decision-Making /Guanghui Yu. - 1 electronic resource (166 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Artificial intelligence (AI) demonstrates superiority over humans in many applications, such as image processing, speech recognition, and decision-making. AI systems are more efficient and accurate in information processing and computation, making them invaluable in assisting human decision-making across domains like healthcare, finance, and academia. Despite these strengths, real-world applications often involve complex, context-specific judgments and a deep understanding of human values, which current AI systems might not fully grasp. In domains requiring creativity and critical thinking, AI systems heavily rely on existing human knowledge. Humans, however, possess unique strengths such as intuition, which aids decision-making in uncertain or novel situations, and moral reasoning, which transcends mere efficiency or optimization. Therefore, combining the strengths of both humans and AI systems is essential for effective decision-making in real-world applications.While AI systems offer significant advantages in supporting human decision-making, their incorporation poses considerable challenges. Human decision-making is complex and characterized by numerous unique traits. Empirical studies reveal that humans can be irrational, unconscious, and sometimes unpredictable, exhibiting various biases such as framing effects and confirmation bias. To develop AI systems that can interact effectively with humans, it is crucial to consider these human traits. However, the integration of human decision models into AI systems has not been sufficiently explored in the field of human-AI interaction. Understanding and predicting human behavior and beliefs can enhance AI's ability to support and influence human decision-making, leading to more effective and human-aligned outcomes. By incorporating human decision models, AI systems can provide more personalized and context-aware assistance, improving both decision quality and human satisfaction.In this dissertation, we address the characteristics of human decision-makers and explicitly incorporate human models into algorithm design. Our study aims to model human behavior and beliefs about AI systems in both one-shot and sequential decision-making scenarios. By leveraging existing research in psychology and economics, as well as data-driven methods using data collected from real humans, we develop models that predict actions more accurately than those based on the assumption of human rationality. We also construct belief models to describe how humans adjust their actions in response to other players within the same decision-making environment. Utilizing these human models, we explore strategies to assist or influence human decisions by designing information signals, modifying decision-making environments, and developing AI teammates that operate alongside humans. Real human-subject experiments are conducted to gain a deeper understanding of human behavior in specific applications and to validate that our designed AI systems can effectively interact with human decision-makers, guiding their decisions toward predefined goals.

English

ISBN: 9798383630419Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Decision making

Designing Behavior-Aware AI Systems to Influence Human Decision-Making /
LDR:04538nam a22004093i 4500 001 1157852
005 20250603111428.5
006 m o d
007 cr|nu||||||||
008 250804s2024 miu||||||m |||||||eng d
020 $a 9798383630419
035 $a (MiAaPQD)AAI31489653
035 $a AAI31489653
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Yu, Guanghui, $e author. $3 1484132
245 1 0 $a Designing Behavior-Aware AI Systems to Influence Human Decision-Making / $c Guanghui Yu.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (166 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
500 $a Advisors: Ho, Chien-Ju Committee members: Yeoh, William; Juba, Brendan; Ottley, Alvitta; Yin, Ming.
502 $b Ph.D. $c Washington University in St. Louis $d 2024.
520 $a Artificial intelligence (AI) demonstrates superiority over humans in many applications, such as image processing, speech recognition, and decision-making. AI systems are more efficient and accurate in information processing and computation, making them invaluable in assisting human decision-making across domains like healthcare, finance, and academia. Despite these strengths, real-world applications often involve complex, context-specific judgments and a deep understanding of human values, which current AI systems might not fully grasp. In domains requiring creativity and critical thinking, AI systems heavily rely on existing human knowledge. Humans, however, possess unique strengths such as intuition, which aids decision-making in uncertain or novel situations, and moral reasoning, which transcends mere efficiency or optimization. Therefore, combining the strengths of both humans and AI systems is essential for effective decision-making in real-world applications.While AI systems offer significant advantages in supporting human decision-making, their incorporation poses considerable challenges. Human decision-making is complex and characterized by numerous unique traits. Empirical studies reveal that humans can be irrational, unconscious, and sometimes unpredictable, exhibiting various biases such as framing effects and confirmation bias. To develop AI systems that can interact effectively with humans, it is crucial to consider these human traits. However, the integration of human decision models into AI systems has not been sufficiently explored in the field of human-AI interaction. Understanding and predicting human behavior and beliefs can enhance AI's ability to support and influence human decision-making, leading to more effective and human-aligned outcomes. By incorporating human decision models, AI systems can provide more personalized and context-aware assistance, improving both decision quality and human satisfaction.In this dissertation, we address the characteristics of human decision-makers and explicitly incorporate human models into algorithm design. Our study aims to model human behavior and beliefs about AI systems in both one-shot and sequential decision-making scenarios. By leveraging existing research in psychology and economics, as well as data-driven methods using data collected from real humans, we develop models that predict actions more accurately than those based on the assumption of human rationality. We also construct belief models to describe how humans adjust their actions in response to other players within the same decision-making environment. Utilizing these human models, we explore strategies to assist or influence human decisions by designing information signals, modifying decision-making environments, and developing AI teammates that operate alongside humans. Real human-subject experiments are conducted to gain a deeper understanding of human behavior in specific applications and to validate that our designed AI systems can effectively interact with human decision-makers, guiding their decisions toward predefined goals.
546 $a English
590 $a School code: 0252
650 4 $a Computer science. $3 573171
650 4 $a Computer engineering. $3 569006
653 $a Decision making
653 $a Human computer interaction
653 $a Machine learning
690 $a 0984
690 $a 0464
690 $a 0800
710 2 $a Washington University in St. Louis. $b Computer Science & Engineering. $e degree granting institution. $3 1484133
720 1 $a Ho, Chien-Ju $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-02B.
790 $a 0252
791 $a Ph.D.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489653