2 citations · 2 across the 6 of their papers we have counts for
9 papers
Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension - such…
A Theoretical Model for Grit in Pursuing Ambitious Ends
Avrim Blum, Emily Diana, Kavya Ravichandran +1
Ambition and risk-taking have been heralded as important ways for marginalized communities to get out of cycles of poverty. As a result, educational messaging often encourages indi…
BiasConnect: Investigating Bias Interactions in Text-to-Image Models
Pushkar Shukla, Aditya Chinchure, Emily Diana +5
The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimensio…
Escaping the Subprime Trap in Algorithmic Lending
Adam Bouyamourn, Alexander Williams Tolbert
Disparities in lending to minority applicants persist even as algorithmic lending finds widespread adoption. We study the role of risk-management constraints, specifically Value-at…
Reconciling Predictive Multiplicity in Practice
Tina Behzad, Sílvia Casacuberta, Emily Ruth Diana +1
Many machine learning applications predict individual probabilities, such as the likelihood that a person develops a particular illness. Since these probabilities are unknown, a ke…
Reconciling Heterogeneous Effects in Causal Inference
Audrey Chang, Emily Diana, Alexander Williams Tolbert
In this position and problem pitch paper, we offer a solution to the reference class problem in causal inference. We apply the Reconcile algorithm for model multiplicity in machine…
Pessimism Traps and Algorithmic Interventions
Avrim Blum, Emily Diana, Kavya Ravichandran +1
In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism…
Correcting Underrepresentation and Intersectional Bias for Classification
Emily Diana, Alexander Williams Tolbert
We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed numb…
Reconciling Individual Probability Forecasts
Aaron Roth, Alexander Tolbert, Scott Weinstein
Individual probabilities refer to the probabilities of outcomes that are realized only once: the probability that it will rain tomorrow, the probability that Alice will die within…