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What is Algorithmic Bias

Chapter 2
Intersecting Environmental Social Governance and AI for Business Sustainability
Unfair outcomes that determines repeatable errors
Published in Chapter:
Role of AI in Strengthening ESG Governance: Perspective From Industry Experts
M. Jayalakshmi (Christ University, India), A. Maria Sneha (Christ University, India), and S. Girish (Christ University, India)
Copyright: © 2024 |Pages: 23
DOI: 10.4018/979-8-3693-1151-6.ch002
Abstract
Socially conscious investors, especially Gen Z, value ethics over money. ESG reports are as important as financial reports for them. ESG ratings from various sources can puzzle these Gen Z investors, as there is no standardization in ESG data. Firstly, the chapter focuses on the need to integrate AI into ESG reporting by highlighting the limitations of mere frameworks such as GRI, SASB, and ISSB. Secondly, it emphasizes the difference between traditional reporting and AI-integrated ESG reporting. It also points out the challenges of AI integration and ways to overcome these challenges. Lastly, the chapter also proposes the need for a unified framework, making it easier for investors to compare and make decisions.
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Chapter 2
Beyond Tools and Procedures: The Role of AI Fairness in Responsible Business Discourse
It refers to the unintended and potentially harmful skewing of algorithmic predictions.
Published in Chapter: Beyond Tools and Procedures: The Role of AI Fairness in Responsible Business Discourse; From: Applied Ethics in a Digital World
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Chapter 21
Introduction to Machine Learning as a New Methodological Framework for Performance Assessment
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Chapter 15
A Critical Data Ethics Analysis of Algorithmic Bias and the Mining/Scraping of Heirs' Property Records
Algorithmic bias is the discrimination caused by algorithmic decision-making that occurs when one group is unfairly or arbitrarily disadvantaged over another (Kim & Cho, 2022).
Published in Chapter: A Critical Data Ethics Analysis of Algorithmic Bias and the Mining/Scraping of Heirs' Property Records; From: Digital Technologies, Ethics, and Decentralization in the Digital Era
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Chapter 9
Leveraging Generative Artificial Intelligence to Expedite UDL Implementation in Online Courses
The systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
Published in Chapter: Leveraging Generative Artificial Intelligence to Expedite UDL Implementation in Online Courses; From: Unlocking Learning Potential With Universal Design in Online Learning Environments
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Chapter 4
AI Discrimination in Hiring
Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one category over another in ways different from the intended function of the algorithm.
Published in Chapter: AI Discrimination in Hiring; From: Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
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Chapter 10
Open Challenges and Research Issues of XAI in Modern Smart Cities
The systematic and unfair distribution of the benefits or harms of an algorithmic system to certain groups of people based on their race, gender, age, or other personal characteristics.
Published in Chapter: Open Challenges and Research Issues of XAI in Modern Smart Cities; From: Advances in Explainable AI Applications for Smart Cities
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Chapter 13
Decoding Algorithmic Bias: Definitions, Sources, and Mitigation Strategies
Systemic and repeatable errors in algorithmic predictions that privilege a group of people over others.
Published in Chapter: Decoding Algorithmic Bias: Definitions, Sources, and Mitigation Strategies; From: Overcoming Cognitive Biases in Strategic Management and Decision Making
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Chapter 65
Algorithms and Bias
The intentional or unintentional bias that can result from using an algorithm to make a decision.
Published in Chapter: Algorithms and Bias; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 5
The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities
This occurs when computer algorithms make unfair or discriminatory decisions, often due to partial data or flawed programming, leading to unequal treatment or disadvantages for specific individuals or groups.
Published in Chapter: The Incorporation of Large Language Models (LLMs) in the Field of Education: Ethical Possibilities, Threats, and Opportunities; From: Philosophy of Artificial Intelligence and Its Place in Society
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Chapter 7
The Importance and Limitations of Artificial Intelligence Ethics and Digital Corporate Responsibility in Consumer Markets: Challenges and Opportunities
Algorithmic bias refers to the unfair or discriminatory outcomes that can arise from the use of computer algorithms. These algorithms are designed to make decisions or predictions, but they can unintentionally favor certain groups or individuals while disadvantaging others. Bias may emerge due to the data used to train the algorithm, reflecting existing societal prejudices. For example, if historical data contains biases, the algorithm might perpetuate those biases, leading to unequal treatment. Addressing algorithmic bias is crucial for ensuring fairness and equity in automated decision-making systems.
Published in Chapter: The Importance and Limitations of Artificial Intelligence Ethics and Digital Corporate Responsibility in Consumer Markets: Challenges and Opportunities; From: Globalized Consumer Insights in the Digital Era
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