I would like to be added! 😄

Hi, I'd like to be part of this!

👋

💬Panel discussion with Sally Haslanger and Marjolein Lanzing: A philosophical perspective on algorithmic discrimination

Is discrimination the right way to frame the issues of lang tech? Or should we answer deeper rooted questions? And how does tech fit in systems of oppression?

📄Undesirable Biases in NLP: Addressing Challenges of Measurement

We also presented our own work on strategies for testing the validity and reliability of LM bias measures:

www.jair.org/index.php/ja...

🔑Keynote @zeerak.bsky.social: On the promise of equitable machine learning technologies

Can we create equitable ML technologies? Can statistical models faithfully express human language? Or are tokenizers "tokenizing" people—creating a Frankenstein monster of lived experiences?

📄A Capabilities Approach to Studying Bias and Harm in Language Technologies

@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.

📄Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution

Flor Plaza discussed the importance of studying gendered emotional stereotypes in LLMs, and how collaborating with philosophers benefits work on bias evaluation greatly.

🔑Keynote by John Lalor: Should Fairness be a Metric or a Model?

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.

📄A Decade of Gender Bias in Machine Translation

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.

📄MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs

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!"

🔑Keynote by Dong Nguyen: When LLMs meet language variation: Taking stock and looking forward

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!

This workshop is organized by University of Amsterdam researchers Katrin Schulz, Leendert van Maanen, @wzuidema.bsky.social, Dominik Bachmann, and myself.
More information on the workshop can be found on the website, which will be updated regularly.
wai-amsterdam.github.io

🌟The goal of this workshop is to bring together researchers from different fields to discuss the state of the art on bias measurement and mitigation in language technology and to explore new avenues of approach.

One of the central issues discussed in the context of the societal impact of language technology is that ML systems can contribute to discrimination. Despite efforts to address these issues, we are far from solving them.

We're super excited to host Dong Nguyen, John Lalor, @zeerak.bsky.social and @azjacobs.bsky.social as invited speakers at this workshop! Submit an extended abstract to join the discussions; either in a 20min talk or a poster session.
📝Deadline Call for Abstracts: 15 Sep, 2024

But exciting to see more work dedicated to sharing models, checkpoints, and training data to the (research) community!

Don't forget EleutherAI's Pythia, which came out last year! dl.acm.org/doi/10.5555/...