Search Results for author: Logan Stapleton

Found 6 papers, 3 papers with code

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders

no code implementations18 May 2022 Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu

In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system.

Decision Making

A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms

no code implementations21 Apr 2022 Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao-Fei Cheng, Haiyi Zhu, Steven Wu, Hoda Heidari

This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.

Fairness

Incentivizing Compliance with Algorithmic Instruments

1 code implementation21 Jul 2021 Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu

In rounds, a social planner interacts with a sequence of heterogeneous agents who arrive with their unobserved private type that determines both their prior preferences across the actions (e. g., control and treatment) and their baseline rewards without taking any treatment.

Selection bias

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

1 code implementation12 Jul 2021 Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu

In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically modify their observable attributes to receive more favorable predictions.

Decision Making Fairness +1

An Algorithmic Framework for Fairness Elicitation

1 code implementation25 May 2019 Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.

Fairness Generalization Bounds

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