This is a Plain English Papers summary of a research paper called Defining Biases and Disparities: A Causal Framework for Rigorous Study. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter.
Overview
- The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences.
- However, the literature does not always clearly define the concept of bias.
- Definitions of bias are often ambiguous or not provided at all.
- To study biases in a precise manner, it is important to have a well-defined concept of bias.
Plain English Explanation
The paper proposes to define bias as a direct causal effect that is unjustified. It also proposes to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. These definitions are intended to help study biases and disparities in a more rigorous and systematic way.
The paper compares these definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. It also discusses how the definitions relate to discrimination.
The paper illustrates the proposed definitions of bias and disparity using two case studies: one on gender bias in science and another on racial bias in police shootings. The aim is to contribute to a better understanding of the causal intricacies involved in studying biases and disparities, and to promote an improved understanding of the policy implications of such studies.
Technical Explanation
The paper proposes definitions for the concepts of bias and disparity to enable more rigorous and systematic study of these phenomena. Bias is defined as a direct causal effect that is unjustified, while disparity is defined as a direct or indirect causal effect that includes a bias.
These definitions are compared to various fairness criteria introduced in the artificial intelligence literature, and the relationship between the proposed definitions and discrimination is discussed.
The paper illustrates the proposed definitions using two case studies: gender bias in science and racial bias in police shootings. These case studies are used to demonstrate how the definitions can be applied to study biases and disparities in a more rigorous and systematic manner.
Critical Analysis
The paper acknowledges that the literature does not always provide clear definitions of bias, which can make it challenging to study these phenomena in a precise way. The proposed definitions of bias and disparity aim to address this issue by providing a more rigorous framework for conceptualizing and investigating these concepts.
One potential limitation of the paper is that the proposed definitions may not fully capture the complexity of real-world biases and disparities, which can be influenced by a variety of factors beyond direct or indirect causal effects. Additionally, the paper does not explore potential challenges or limitations in applying these definitions in empirical research.
Despite these potential concerns, the paper's focus on developing a more standardized approach to studying biases and disparities is valuable, as it can contribute to a better understanding of the causal mechanisms underlying these phenomena and their policy implications.
Conclusion
This paper proposes a set of definitions for the concepts of bias and disparity that aim to enable more rigorous and systematic study of these important topics in the social and behavioural sciences. The proposed definitions are compared to fairness criteria in the artificial intelligence literature and are illustrated using case studies on gender bias in science and racial bias in police shootings.
The paper's focus on developing a standardized approach to studying biases and disparities has the potential to contribute to a better understanding of the causal mechanisms underlying these phenomena and their policy implications. While the proposed definitions may have some limitations, the paper's overall aim to promote a more rigorous and systematic approach to this important area of research is valuable.
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