Oskar van der Wal

@ovdw.bsky.social

1377 Followers 404 Following

Technology specialist at the EU AI Office / AI Safety / Prev: University of Amsterdam, EleutherAI, BigScience Thoughts & opinions are my own and do not necessarily represent my employer.

A photo of the panel discussion.
Screenshot of a slide discussing how to improve how we communicate bias scores on Model Cards.
Photo of the presentation. The slide shows an image of Frankenstein's monster.
Photo of the presentation.
Photo of the presentation.
Photo of the presentation.
Photo of the presentation.
Photo of the presentation.
Photo of the presentation.
Photo of the poster session at the workshop.
Screenshot of a figure with the caption: "This figure illustrates the difference between convergent and divergent validity (see Section 4.2). In this example, the convergent validity is assessed by testing how related a gender bias measure is to another gender bias measure. The divergent validity, instead, is assessed by testing whether the gender bias measure is not strongly correlated with a measure for another, but easily confounded construct (e.g., grammatical gender)."
Screenshot of a table with the caption: "An overview of the types of construct validity we discuss in Section 4. Examples are given in the last column."
Screenshot of a table with the caption: "Examples of the reliability types we discuss in Section 3. We specify, for each reliability type, across which variations (e.g., random seeds) the consistency is measured. In the last column, we provide examples of where these reliability types could be applied."
Screenshot of a figure with the caption: "We assume that a training dataset's bias influences the bias of a model trained on that data (but other possible sources of bias are possible, e.g., model compression may amplify existing biases (Hooker et al., 2020)). Training dataset bias and model bias are unobservable constructs (circle) that both have different possible operationalizations (squares)."