74 citations · 144 across the 7 of their papers we have counts for
8 papers
Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses
Logan Stapleton, Jordan Taylor, Sarah Fox +2
Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content wh…
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
Logan Stapleton, Min Hun Lee, Diana Qing +5
Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data t…
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng +7
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is cr…
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton +4
Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic 'fairness-enhancing' remedies. Mo…
Incentivizing Compliance with Algorithmic Instruments
Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis +1
Randomized experiments can be susceptible to selection bias due to potential non-compliance by the participants. While much of the existing work has studied compliance as a static…
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Keegan Harris, Daniel Ngo, Logan Stapleton +2
In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically mod…
Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems
Hao-Fei Cheng, Logan Stapleton, Ruiqi Wang +4
Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an und…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
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 com…