Summary of Effective Controllable Bias Mitigation For Classification and Retrieval Using Gate Adapters, by Shahed Masoudian et al.
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
by Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Controllable Gate Adapter (ConGater), a novel modular gating mechanism that allows for adjustable sensitivity parameters to control the degree of bias reduction in language models at inference time. By optimizing for a desired performance-fairness trade-off, ConGater enables debiasing with minimal impact on task performance. The authors demonstrate its effectiveness through adversarial debiasing experiments and fairness list-wise regularization, showing that ConGater maintains higher task performance while containing less information about protected attributes. This controllable approach enhances personalization of model use and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ConGater is a new way to make language models fairer without sacrificing how well they do their job. Right now, we can’t easily control how biased or unbiased our models are when we’re using them. ConGater changes that by letting us adjust the amount of bias reduction in real-time. The authors tested it and found that it works really well on three different classification tasks and one search results task. |
Keywords
* Artificial intelligence * Classification * Inference * Regularization
