Austin Hoag’s scientific contributions

... It's common in the fairness-aware machine learning literature for fairness measures to be defined such that the optimization goal is a ratio with a value of 1.0 or a difference with a value of 0.0, and previous work on bias in content moderation has used the difference [10]. Following recent work that demonstrates that the ratio is more appropriate for most fairness contexts [28], we define speech suppression accordingly: ...

Reference:

Identity-related Speech Suppression in Generative AI Content Moderation

... Formal languages can be extended to be more expressive, to capture privacy properties [83], data-based properties [59], [60], fairness properties [12], [27], among others. Some of these kinds of properties can be automatically verified probabilistically [4], [29], [33], [53], [81]. ...

Reference:

Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification