Hort, Max; Moussa, Rebecca; Sarro, Federica; (2023) Multi-objective search for gender-fair and semantically correct word embeddings. Applied Soft Computing , 133 , Article 109916. 0.1016/j.asoc.2022.109916.

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Abstract

Fairness is a crucial non-functional requirement of modern software systems that rely on the use of Artificial Intelligence (AI) to make decisions regarding our daily lives in application domains such as justice, healthcare and education. In fact, these algorithms can exhibit unwanted discriminatory behaviours that create unfair outcomes when the software is used, such as giving privilege to one group of users over another (e.g., males vs. females). 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. However, previous work has shown that mitigating bias can impact the performance of such systems. Therefore, we propose herein a novel use of soft computing for improving AI-enabled software fairness. Specifically, we exploit multi-objective search, as opposed to previous work optimising fairness only, 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. To assess the effectiveness of our proposal, we carry out a thorough empirical study based on the most recent best practice for the evaluation of search-based approaches and AI-enabled software. We explore seven different search-based approaches, and benchmark them against both baseline and state-of-the-art approaches applied to a popular and widely used word embedding model, namely Word2Vec. Our results show that multi-objective search outperforms single-objective search, and generates word embeddings that are strictly better than the original ones in both objectives, bias and semantic correctness, for all investigated cases. Additionally, our approach generates word embeddings of higher semantic correctness than those generated by using state-of-the-art techniques in all cases, while also achieving a higher degree of fairness in 67% of the cases. These findings show the feasibility and effectiveness of multi-objective search as a tool for engineers to incorporate fair and accurate word embedding models in their AI-enabled systems.

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