QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes
It addresses the problem of superficial fairness practices in AI for researchers and marginalized communities, though it is incremental as it builds on existing intersectionality concepts.
The paper tackles the lack of practical guidance for incorporating intersectionality into AI research by introducing QUINTA, a framework that uses critical reflexivity to identify and mitigate negative impacts like inadvertent marginalization in data-driven processes, demonstrated through a case study on the #metoo movement.
As the field of artificial intelligence (AI) and machine learning (ML) continues to prioritize fairness and the concern for historically marginalized communities, the importance of intersectionality in AI research has gained significant recognition. However, few studies provide practical guidance on how researchers can effectively incorporate intersectionality into critical praxis. In response, this paper presents a comprehensive framework grounded in critical reflexivity as intersectional praxis. Operationalizing intersectionality within the AI/DS (Artificial Intelligence/Data Science) pipeline, Quantitative Intersectional Data (QUINTA) is introduced as a methodological paradigm that challenges conventional and superficial research habits, particularly in data-centric processes, to identify and mitigate negative impacts such as the inadvertent marginalization caused by these practices. The framework centers researcher reflexivity to call attention to the AI researchers' power in creating and analyzing AI/DS artifacts through data-centric approaches. To illustrate the effectiveness of QUINTA, we provide a reflexive AI/DS researcher demonstration utilizing the \#metoo movement as a case study. Note: This paper was accepted as a poster presentation at Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Conference in 2023.