2 citations · 2 across the 4 of their papers we have counts for
4 papers
Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
Zeyu Tang, Alex John London, Atoosa Kasirzadeh +4
Algorithmic fairness research has largely framed unfairness as discrimination along sensitive attributes. However, this approach limits visibility into unfairness as structural inj…
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
Xiangchen Song, Aashiq Muhamed, Yujia Zheng +5
Sparse Autoencoders (SAEs) are a prominent tool in mechanistic interpretability (MI) for decomposing neural network activations into interpretable features. However, the aspiration…
Detecting and Identifying Selection Structure in Sequential Data
Yujia Zheng, Zeyu Tang, Yiwen Qiu +2
We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often disto…
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Usman Gohar, Zeyu Tang, Jialu Wang +4
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works ha…