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Found 7 papers, 3 papers with code
Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses
Finally, we discuss how our use cases demonstrate green teaming as both a practical design method and a mode of critique, which problematizes and subverts current understandings of harms and values in generative AI.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
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.
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts.
Incentivizing Compliance with Algorithmic Instruments
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.
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
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.
An Algorithmic Framework for Fairness Elicitation
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.
