Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems
With the rapid advancement of AI, there is a growing trend to integrate AI into decision-making processes. However, AI systems may exhibit biases that lead decision-makers to draw unfair conclusions. Notably, the COMPAS system used in the American justice system to evaluate recidivism was found to favor racial majority groups; specifically, it violates a fairness standard called equalized odds. Various measures have been proposed to assess AI fairness. We present a framework for auditing AI fairness, involving third-party auditors and AI system providers, and we have created a tool to facilitate systematic examination of AI systems. The tool is open-sourced and publicly available. Unlike traditional AI systems, we advocate a transparent white-box and statistics-based approach. It can be utilized by third-party auditors, AI developers, or the general public for reference when judging the fairness criterion of AI systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFairnessMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
Fairness auditing of AI systems can identify and quantify biases. However, traditional auditing using real-world data raises security and privacy concerns. It exposes auditors to security risks as they become custodians …
FairnessPrivacy PreservingNishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models
The growing reliance on Artificial Intelligence (AI) models in high-stakes decision-making systems, particularly within emerging telecom and 6G applications, underscores the urgent need for transparent and standardized f…
Bias DetectionIdentifying Reasons for Bias: An Argumentation-Based Approach
As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important. Whilst there has been substantial research in building fair algorithmic …
AttributeDecision MakingFairnessIdentifying Ethical Biases in Action Recognition Models
Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has not been analyzed. We introduce a framework for auditing bias in HAR …
Action RecognitionPose EstimationFaking Fairness via Stealthily Biased Sampling
Auditing fairness of decision-makers is now in high demand. To respond to this social demand, several fairness auditing tools have been developed. The focus of this study is to raise an awareness of the risk of malicious…
Fairness