4 citations · 4 across the 2 of their papers we have counts for
7 papers
Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
Olawale Salaudeen, Haoran Zhang, Kumail Alhamoud +2
Benchmarks for out-of-distribution (OOD) generalization frequently show a strong positive correlation between in-distribution (ID) and OOD accuracy across models, termed "accuracy-…
A Definition of AGI
Dan Hendrycks, Dawn Song, Christian Szegedy +30
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quant…
Measurement to Meaning: A Validity-Centered Framework for AI Evaluation
Olawale Salaudeen, Anka Reuel, Ahmed Ahmed +6
While the capabilities and utility of AI systems have advanced, rigorous norms for evaluating these systems have lagged. Grand claims, such as models achieving general reasoning ca…
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal +12
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal pe…
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified?
Olawale Salaudeen, Nicole Chiou, Shiny Weng +1
Spurious correlations, unstable statistical shortcuts a model can exploit, are expected to degrade performance out-of-distribution (OOD). However, across many popular OOD generaliz…
Toward an Evaluation Science for Generative AI Systems
Laura Weidinger, Inioluwa Deborah Raji, Hanna Wallach +7
There is an increasing imperative to anticipate and understand the performance and safety of generative AI systems in real-world deployment contexts. However, the current evaluatio…