As noted above, not only the model but also the manner in which it is deployed and in which potential harms are measured and mitigated have the potential to create harmful bias, and a particularly concerning example of this arises in DALL·E 2 Preview in the context of pre-training data filtering and post-training content filter use, which can resul... See more

**Note:** These concerns are the same as in all applications of AI: bias, privacy, interpretability

Hiring algorithms are but one example of a larger set of technologies which promise to disclose some deeper truth about the self or the world that would be otherwise unnoticed. Similar tools are deployed in the realms of finance, criminal justice, and health care among others. The underlying assumption, occasionally warranted, is that analyzing cop... See more

The accessibility and scale of AI tools mean they could have an outsized impact on how almost any community is represented. According to Valeria Piaggio, global head of diversity, equity, and inclusion at marketing consultancy Kantar, the marketing and advertising industries have in recent years made strides in how they represent different groups, ... See more

Scientists have long been developing machines that attempt to imitate the human brain. Just as humans are exposed to systemic injustices, machines learn human-like stereotypes and cultural norms from sociocultural data, acquiring biases and associations in the process. Our research shows that bias is not only reflected in the patterns of language, ... See more