Search Results for author:
Found 16 papers, 1 papers with code
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions.
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation.
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).
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.
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.
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems.
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 Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks.
Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
Our findings demonstrate that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system's design and deployment, if empowered to do so.
Agent-based Simulation for Online Mental Health Matching
Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues.
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design
AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare.
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.
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets.
Organizational Bulk Email Systems: Their Role and Performance in Remote Work
The COVID-19 pandemic has forced many employees to work from home.
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge
A growing body of research has explored how to support humans in making better use of AI-based decision support, including via training and onboarding.
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
AI tools are increasingly deployed in community contexts.
