Non-Binary and Trans-Inclusive AI: A Catalogue of Best Practices for Developing Automatic Gender Recognition Solutions

Author:

Perilo Michel1,Valença George1,Telles Aldenir1

Affiliation:

1. Departamento de Computação, UFRPE, Recife, Pernambuco, Brazil

Abstract

Artificial intelligence (AI) has significantly optimized processes across various sectors, enhancing efficiency and transforming digital interactions. However, as AI becomes more integrated into daily life, concerns about its social impacts and inherent biases have emerged. This study explores how AI technologies, such as facial recognition and Automatic Gender Recognition (AGR), can perpetuate and amplify societal prejudices, especially against transgender and non-binary individuals. The 2018 case of Amazon's Rekognition technology, which exhibited high false positive rates for individuals with dark skin, highlights the risks of algorithmic bias and mass surveillance. Given these challenges, this research performed performed a systematic mapping study of the literature on AI to present an analysis of problems and respective causes brought by facial recognition and AGR applications to trans and non-binary people. In a second phase, we developed and empirically assessed a catalog of 19 best practices for an ethical AI development grounded in Justice, Equity, Diversity, and Inclusion principles. We aim to establish ethical standards that promote inclusivity to trans and non-binary people, mitigating algorithmic discrimination.

Publisher

Association for Computing Machinery (ACM)

Reference26 articles.

1. McKane Andrus and Sarah Villeneuve. 2022. Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness. In 2022 ACM Conference on Fairness, Accountability, and Transparency.

2. A Framework to Evaluate the Quality of Integrated Datasets

3. Sex and Gender in Simone de Beauvoir's Second Sex

4. Performative Acts and Gender Constitution: An Essay in Phenomenology and Feminist Theory

5. Tsz Hin Martin Cheung, Erik Noyes, and Leonidas Deligiannidis. 2021. Face of the Team-Diversity, Equity, and Inclusion. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 146--151.

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