Inspired by the concept of the male gaze (Mulvey, 1975) in literature and media studies, this paper proposes a framework for analyzing gender bias in terms of female objectification: the extent to which a text portrays female individuals as objects of visual pleasure. Our framework measures female objectification along two axes. First, we compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities. Next, by analyzing the word embedding space induced by a text (Caliskan et al., 2017), we compute an appearance bias score that indicates whether female entities are more closely associated with appearance-related words than male entities. Applying our framework to 19th and 20th century novels reveals evidence of female objectification in literature: we find that novels written from a male perspective systematically objectify female characters, while novels written from a female perspective do not exhibit statistically significant objectification of any gender.

Non-Fungible Tokens (NFTs) are non-interchangeable assets, usually digital art, which are stored on the blockchain. Preliminary studies find that female and darker-skinned NFTs are valued less than their male and lighter-skinned counterparts. However, these studies analyze only the CryptoPunks collection. We test the statistical significance of race and gender biases in the prices of CryptoPunks and present the first study of gender bias in the broader NFT market. We find evidence of racial bias but not gender bias. Our work also introduces a dataset of gender-labeled NFT collections to advance the broader study of social equity in this emerging market.

"One of the things I get very concerned about is that, for so long, AI has been such a mystery. And in that blanket of mysteriousness, it's been built up as something magical. So much so that for the first number of years in this role, customers were coming to Cloud excited about AI as a technology, but not yet as a means to solve tactical business problems, as if any use of AI might be a magic wand," says Tracy Pizzo Frey, Senior Director, Outbound Product Management, Engagements & Responsible AI for Cloud AI & Industry Solutions at Google. The reality is, of course, very different. AI technology is not magic at all.

Google has announced that its image recognition AI will no longer identify people in images as a man or a woman, reports Business Insider. The change was revealed in an email to developers who use the company's Cloud Vision API that makes it easy for apps and services to identify objects in images. In the email, Google said it wasn't possible to detect a person's true gender based simply on the clothes they were wearing. But Google also said that they were dropping gender labels for another reason: they could create or reinforce biases. Given that a person's gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically Principle #2: Avoid creating or reinforcing unfair bias.