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Analogies such as "man is to king as woman is to X" are often used toillustrate the amazing power of word embeddings. Concurrently, they have alsobeen used to expose how strongly human biases are encoded in vector spacesbuilt on natural language, like "man is to computer programmer as woman is tohomemaker". Recent work has shown that analogies are in fact not such adiagnostic for bias, and other methods have been proven to be more apt to thetask. However, beside the intrinsic problems with the analogy task as a biasdetection tool, in this paper we show that a series of issues related to howanalogies have been implemented and used might have yielded a distorted pictureof bias in word embeddings. Human biases are present in word embeddings andneed to be addressed. Analogies, though, are probably not the right tool to doso. Also, the way they have been most often used has exacerbated some possiblynon-existing biases and perhaps hid others. Because they are still widelypopular, and some of them have become classics within and outside the NLPcommunity, we deem it important to provide a series of clarifications thatshould put well-known, and potentially new cases into the right perspective.

Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor