Search the World's Largest Database of Information Science & Technology Terms & Definitions
InfInfoScipedia LogoScipedia
A Free Service of IGI Global Publishing House
Below please find a list of definitions for the term that
you selected from multiple scholarly research resources.

What is Algorithmic Fairness

Overcoming Cognitive Biases in Strategic Management and Decision Making
A concept in machine learning that aims to mitigate algorithmic bias.
Published in Chapter:
Decoding Algorithmic Bias: Definitions, Sources, and Mitigation Strategies
Ozgur Aksoy (Istanbul Universitesi, Turkey)
DOI: 10.4018/979-8-3693-1766-2.ch013
Predictive algorithms are increasingly used to assist decision-making for efficiency gains. However, it is essential to acknowledge that algorithms can mirror systemic biases in their predictions in a way that favors certain groups over others, even if they are immune to cognitive biases. The notion of algorithms generating unfair predictions is referred to as “algorithmic bias.” Addressing cognitive biases in humans might not always be an effective solution to mitigate algorithmic bias. Therefore, it is essential to understand when and how quantitative technical mitigation methods can address this issue. This chapter explores the fundamental concepts of algorithmic bias, its sources, and technical mitigation strategies. In a world where humans and AI are intertwined, it is our responsibility to ensure a fair digital future. Addressing algorithmic bias is critical to achieving this goal.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
A Critical Data Ethics Analysis of Algorithmic Bias and the Mining/Scraping of Heirs' Property Records
Algorithmic fairness is the intentional examination of models for fair and equitable algorithms to reduce bias. “To construct a fair algorithmic model, “three criteria need to be considered: fairness, expressiveness, and utility. Fairness can be evaluated by the three measures introduced by how the model is treated fairly without bias between groups. Expressiveness is how the value after applying the method of processing data expresses the information of the original data. It can be evaluated by the performance obtained from the various classifiers. Utility is an evaluation of tasks that the AI model must perform” (Kim & Cho, 2022, p. 2).
Full Text Chapter Download: US $37.50 Add to Cart
eContent Pro Discount Banner
InfoSci OnDemandECP Editorial ServicesAGOSR