# QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes

arXiv: https://arxiv.org/abs/2509.16347 · HTML: https://mlsanity.com/paper/2509.16347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 108 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [A Comparative Analysis of Community Detection Algorithms on Artificial Networks](https://mlsanity.com/paper/1608.00763/) (2016)
- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [Twitter for Sparking a Movement, Reddit for Sharing the Moment: #metoo through the Lens of Social Media](https://mlsanity.com/paper/1803.08022/) (2018)

## Abstract (arXiv)

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.