Last week, we organized the workshop "New Perspectives on Bias and Discrimination in Language Technology" 🤖 @uvahumanities.bsky.social @amsterdamnlp.bsky.social
We're looking back at two inspiring days of talks, posters, and discussions—thanks to everyone who participated!
https://wai-amsterdam.github.io
We're looking back at two inspiring days of talks, posters, and discussions—thanks to everyone who participated!
https://wai-amsterdam.github.io
Comments
Non-standard language is often seen as noisy/incorrect data, but this ignores the reality of language. Variation should play a larger role in LLM developments and sociolinguistics can help!
Vera Neplenbroek presented a multilingual extension of the BBQ bias benchmark to study bias across English, Dutch, Spanish, and Turkish.
"Multilingual LLMs are not necessarily multicultural!"
Eva Vanmassenhove: how has research on gender bias in MT developed over the years? Important issues, like non-binary gender bias, now fortunately get more attention. Yet, fundamental problems (that initially seemed trivial) remain unsolved.
While fairness is often viewed as a metric, using integrated models instead can help with explaining upstream bias, predicting downstream fairness, and capturing intersectional bias.
Flor Plaza discussed the importance of studying gendered emotional stereotypes in LLMs, and how collaborating with philosophers benefits work on bias evaluation greatly.
@hellinanigatu.bsky.social introduced us to the Capabilities Approach and how it can help us better understand the social impact of language technologies—with case studies of failing tech in the Majority World.