Managing the risks of inevitably biased visual artificial intelligence systems

Datasets aren’t simply raw materials to feed algorithms, but are political interventions. As such, much of the discussion around 'bias' in AI systems misses the mark: there is no 'neutral,' 'natural,' or 'apolitical' vantage point that training data can be built upon. There is no easy technical 'fix' by shifting demographics, deleting offensive ter... See more

One concern with the development of these types of machine learning technologies is the issue of bias — ensuring the programs work equitably for all patients, regardless of age, gender, ethnicity, nationality and other demographic criteria.

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

To guard against these issues while reaping the potential benefits of image generators, we provide recommendations such as regulation that forces organizations to disclose their training data, and tools that help artists prevent using their content as training data without their consent.

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