Paper Type
Complete
Paper Number
1116
Description
In this paper we provide a systematic review of gender bias in AI, focusing on bias detection, mitigation, and the challenges of addressing non-binary gender bias within textual, visual, and audio data. Underscoring the importance to address and deconstruct gender biases embedded in AI technologies, we explore the state of research on gender bias in AI and ML applications, debiasing and mitigation techniques for non-binary genders, and the specific challenges and trade-offs across different data types. Our findings reveal significant gaps in research, particularly in audio data and non-binary gender considerations, highlighting the need for more nuanced, data-type-specific approaches to promote inclusivity in AI systems. By offering a granular analysis of gender bias and proposing future directions for research, this work contributes to the broader understanding of creating inclusive and fair AI technologies.
Recommended Citation
Tronnier, Frédéric; Löbner, Sascha; Azanbayev, Alina; and Walter, Max Lukas, "A Systematic Literature Review on Gender Bias in AI – Towards Inclusiveness in Machine Learning" (2024). PACIS 2024 Proceedings. 3.
https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/3
A Systematic Literature Review on Gender Bias in AI – Towards Inclusiveness in Machine Learning
In this paper we provide a systematic review of gender bias in AI, focusing on bias detection, mitigation, and the challenges of addressing non-binary gender bias within textual, visual, and audio data. Underscoring the importance to address and deconstruct gender biases embedded in AI technologies, we explore the state of research on gender bias in AI and ML applications, debiasing and mitigation techniques for non-binary genders, and the specific challenges and trade-offs across different data types. Our findings reveal significant gaps in research, particularly in audio data and non-binary gender considerations, highlighting the need for more nuanced, data-type-specific approaches to promote inclusivity in AI systems. By offering a granular analysis of gender bias and proposing future directions for research, this work contributes to the broader understanding of creating inclusive and fair AI technologies.
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