Ethics in Artificial Intelligence.

Format:

Book

Author/Creator:

Mukherjee, Animesh.

Contributor:

Kulshrestha, Juhi.

Chakraborty, Abhijnan.

Kumar, Srijan.

Series:

Studies in Computational Intelligence Series

Studies in Computational Intelligence Series ; v.1123

Language:

English

Subjects (All):

Ethics.

Artificial intelligence.

Physical Description:

1 online resource (150 pages)

Edition:

1st ed.

Place of Publication:

Singapore : Springer Singapore Pte. Limited, 2024.

Summary:

This book, 'Ethics in Artificial Intelligence: Bias, Fairness and Beyond,' explores the ethical challenges and considerations in the rapidly advancing field of artificial intelligence. Edited by Animesh Mukherjee and Juhi Kumar, it brings together insights from leading experts in academia and industry to address issues such as bias, fairness, and accountability in AI systems. The book delves into complex questions about the role of AI in society, including how to address biased learning, the ethical design of algorithms, and the potential for AI to complement human intelligence. It aims to equip readers with critical thinking skills to navigate the ethical implications of AI, making it a valuable resource for those interested in the intersection of technology, ethics, and society. Generated by AI.

Contents:

Intro

Foreword

Preface

Contents

About the Editors

Testing, Debugging, and Repairing Individual Discrimination in Machine Learning Models

1 Introduction

2 Background and Notation

2.1 Fairness

2.2 Decision Tree

3 Testing

3.1 Problem Setup

3.2 Maximizing Path Coverage

3.3 Maximizing Effectiveness of Discrimination Detection

4 Debugging

4.1 RID

4.2 Determining RIDs

4.3 On-Demand Sample Generation/Active Learning

5 Repairing

5.1 Setup

5.2 Objective

5.3 Iterative Algorithm

6 Experimental Results

6.1 Benchmarks

6.2 Setup and Configuration

6.3 Research Questions

6.4 Metrics

6.5 Results

7 Related Works

8 Conclusion

References

Group and Individual Fairness in Clustering Algorithms

2 Preliminaries

3 Group Fairness

4 Individual Fairness

5 Relationships Between Fairness Levels and Their Notions

5.1 Relationship Between Group Fairness Notions

5.2 Relationship Between Individual Fair Notions

5.3 Relationship Between Group and Individual Fairness

6 Algorithms and Theoretical Guarantees

6.1 Group Fairness

6.2 Individual Fairness

6.3 Extension to Multiple Protected Attributes

6.4 Fair Algorithms Under Different Setting

6.5 Deep Fair Clustering

7 Discussion and Open Problems

Temporal Fairness in Online Decision-Making

2 Fairness in Static Decision-Making

3 Ensuring Fairness in Dynamic Settings

3.1 Temporal Fairness and Memory

3.2 Temporal Fairness and Learnability

4 Example I: Partial Memory Comparative Fairness

5 Example II: Full Memory Relaxed Comparative Fairness

5.1 Algorithm Design for a Single Context

5.2 Algorithm Design for N Contexts

5.3 Improvements and Open Directions

6 Connections with Law and Policy

References.

No AI After Auschwitz? Bridging AI and Memory Ethics in the Context of Information Retrieval of Genocide-Related Information

2 AI-driven IR Systems and Genocide-Related Information

3 Memory Ethics and Human Curation of Genocide-Related Information

4 Bridging Memory Ethics and AI-driven IR System Design

5 Discussion

Algorithmic Fairness in Multi-stakeholder Platforms

2 Algorithmic Fairness and Its Importance

3 Algorithmic Fairness in Online Platforms

4 The Case of Multi-stakeholder Platforms

4.1 Platforms with Two Types of Stakeholders

4.2 Platforms with Three or More Types of Stakeholders

5 Multi-stakeholder Fairness

6 Conclusion

Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social Sciences

2 Dataset Creation and Collection Bias

2.1 Sampling Bias

2.2 Negative Set Bias

2.3 Label Bias

2.4 Apprehension Bias

3 ML Model and Data Analysis Bias

3.1 Confounding Bias

3.2 Chronological Bias

3.3 Algorithm Bias

4 Data and Model Evaluation Bias

4.1 Human Evaluation Bias

4.2 Validation and Test Set Bias

5 Responsible Research for CSS ML Pipelines

The Theory of Fair Allocation Under Structured Set Constraints

2 Preliminaries and Notations

3 Matroid-Constrained Fair Allocation

4 Connectivity Constraints on Goods

5 Connectivity on Agents

Interpretability of Deep Neural Models

2 Background and Related Work

3 Method

3.1 Feature Group Attribution Problem

3.2 Solution Axioms

3.3 Our Method: Integrated Directional Gradients

4 Evaluation

4.1 Setup

4.2 ``CHECKLIST'' Tests

4.3 Correctness

4.4 Capturing Semantic Interaction

5 Conclusion

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Other Format:

Print version: Mukherjee, Animesh Ethics in Artificial Intelligence: Bias, Fairness and Beyond

ISBN:

9789819971848

9819971845

OCLC:

1416747237