Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor (2019) (arxiv.org)

A recent paper entitled "Fair is Better than Sensational: Man is to Doctor as Woman is to Doctor" critically examines the use of analogies in evaluating biases encoded in word embeddings, commonly used in natural language processing (NLP). The authors argue that while analogies like "man is to king as woman is to queen" have been instrumental in demonstrating both the capabilities and biases inherent in these models, they are not a reliable method for bias detection. Instead, the paper highlights how the methodology surrounding these analogies may have distorted our understanding of bias, potentially exacerbating existing biases and obscuring others. This work is significant for the AI/ML community as it challenges the prevailing narratives around bias detection in word embeddings, emphasizing that existing diagnostic tools might not fully capture the complexities of human bias. By calling for a re-evaluation of how biases are measured and addressed, it pushes researchers to seek more comprehensive and accurate methods for bias detection in AI systems, which is crucial for promoting fairness and accountability in technology development. The insights from this paper could lead to improved practices in creating more equitable AI systems that reflect diverse perspectives.