Evaluating Algorithmic Bias in the Context of Gender

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

The accelerating digital transformation across sectors is paving the way for the widespread use of algorithms at all levels of decision-making mechanisms. Nevertheless, the biases engendered by these algorithmic systems are precipitating grave ramifications, particularly in the context of gender inequality and discrimination. This study examines how algorithms affect gender through a bibliographic analysis of papers indexed in the Web of Science database from 2020 to 2025. We used VOSviewer software to analyse this data, tabulating how algorithmic bias manifests across different sectors from recruitment processes, healthcare systems and financial apparatuses right through to social media platforms. The implications are that algorithms have the ability to replicate and transform into social biases reflected in historical data, which differ by gender and availability of certain groups. Such findings make clear that new mechanisms around algorithmic transparency, accountability and fairness need to emerge, as do regulatory frameworks including the EU Artificial Intelligence Act..