5 papers
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…
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…
Reconciling Predictive Multiplicity in Practice
Tina Behzad, SÃlvia 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…
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…