Hort, Max; Moussa, Rebecca; Sarro, Federica; (2023) Multi-objective Search for Gender-fair and Semantically Correct Word Embeddings (HOP GECCO'23). In: Proceedings of the GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. (pp. pp. 23-24). ACM

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Abstract

Mitigating algorithmic bias during the development life cycle of AI-enabled software is crucial given that any bias in these algorithms is inherited by the software systems using them. At the Hot-off-the-Press GECCO track, we aim at disseminating our article Multi-objective search for gender-fair and semantically correct word embeddings. Applied Soft Computing, 2023 [5]. In this work, we exploit multi-objective search to strike an optimal balance between reducing gender bias and improving semantic correctness of word embedding models, which are at the core of many AI-enabled systems. Our results show that, while single-objective search approaches are able to reduce the gender bias of word embeddings, they also reduce their semantic correctness. On the other hand, multi-objective approaches are successful in improving both goals, in contrast to existing work which solely focuses on reducing gender bias. Our results show that multi-objective evolutionary approaches can be successfully exploited to address bias in AI-enable software systems, and we encourage the research community to further explore opportunities in this direction.

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