Top Tweets for #20daysofresponsibleai
How Gender Debiasing Affects Internal Model Representations, and Why It Matters (NAACL 2022)
Paper: https://t.co/8aBS6IJTXf
TLDR: Intrinsic bias metrics can correlate well with extrinsic metrics, with some changes, and can be more reliable
Day 20 of #20daysofresponsibleAI #NLP
Debiasing should be Good and Bad (ACL 2023)
Paper: https://t.co/erPvi2lqR6
TLDR: Proposes that if bias specifications are reversed, output of debiasing method should also get reversed & find current methods to be inconsistent with this
Day 19 of #20daysofresponsibleAI #NLProc
When Do Pre-Training Biases Propagate to Downstream Tasks? (EACL 2023)
Paper: https://t.co/q3rHBmeo3E
TLDR: Presence of high biases in pre-trained LMs can lead to high hallucination rates in downstream tasks!
Day 18 of #20daysofresponsibleAI #NLProc
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU
Paper: https://t.co/084PECg9Ik
TLDR: Finds a subset of weights in the model which causes bias and prunes it to zero during inference. Requires no retraining!
Day 15 of #20daysofresponsibleAI #NLP
The Tail Wagging the Dog (ACL 2023 Outstanding Paper)
Paper: https://t.co/TvF55jnqgC
TLDR: Social bias benchmarks are very sensitive to small modifications e.g. synonymizing words in templates, resulting in very different bias values
Day 14 of #20daysofresponsibleAI #NLP
“How Biased are Your Features?” (FAccT 2023)
Paper: https://t.co/NEsKe1n6uf
TLDR: They measure the contribution of individual features to the overall bias of a classifier by fitting a set-additive model to the classifier
Day 13 of #20daysofresponsibleAI #NLP
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model
Paper: https://t.co/pQm6io2W0F
TLDR: Biased machine translation models create gender-biased text from unbiased text, later used to train unbiased seq-to-seq model
Day 12 of #20daysofresponsibleAI #NLP
FairPrism: Evaluating Fairness-Related Harms in Text Generation (ACL 2023)
Paper: https://t.co/G5NF6GuXbr
TLDR: A new dataset for different types of harm. Addresses issues such as clearer coverage specification, annotators disagreement
Day 11 of #20daysofresponsibleAI #NLP
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation (EMNLP 2022)
Paper: https://t.co/DKRcyOzCRX
TLDR: Pre-trained LM based metrics have higher bias on 6 sensitive attributes including race and gender
Day 10 of #20daysofresponsibleAI #NLP
NLPositionality: Characterizing Design Biases of Datasets and Models (ACL 2023 Outstanding Paper)
Paper: https://t.co/aMZud0oq74
TLDR: Authors impart their own identities, backgrounds into datasets and models which leads to design biases
Day 9 of #20daysofresponsibleAI #NLP
Tracking the Trails of Political Biases Leading to Unfair NLP Models (ACL 2023 Best Paper)
Paper: https://t.co/VjrMg7fcy3
TLDR: Using political spectrum theories they show political bias propagates from train data to LMs to downstream tasks
Day 8 of #20daysofresponsibleAI #NLP
Can Voice Assistants Be Microaggressors?
Paper: https://t.co/nxcXcV4TNK
TLDR: They found that high errors/biases in speech recognition harm marginalized groups more psychologically
Thanks @kimiwenzel for the suggestion and great work!
Day 7 of #20daysofresponsibleAI #NLProc
Stereotyping Norwegian Salmon
Paper: https://t.co/6dXlGvh54D
TLDR: Identified pitfalls in bias benchmark datasets about what they aim to measure vs how well they measure it 🧵⬇️
Thanks @shaily99 for the suggestion!
Day 6 of #20daysofresponsibleAI #NLProc #bias
Using Natural Sentences for Understanding Biases in Language Models
Paper: https://t.co/7mx76QU0GQ
TLDR: Quantifying bias is very sensitive to the design choice by authors. Real-world (Natural) sentences should be used instead 🧵⬇️
Day 4 of #20daysofresponsibleAI #NLP #bias
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