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Found 11 papers, 1 papers with code
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping.
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice
Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches.
Scaling Laws Do Not Scale
As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws.
Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice
In this research, we sought to better understand practitioners' current practices and tactics to enact cross-functional collaboration for AI fairness, in order to identify opportunities to support more effective collaboration.
Fairlearn: Assessing and Improving Fairness of AI Systems
Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence.
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems.
Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"
There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI.
Risks of AI Foundation Models in Education
If the authors of a recent Stanford report (Bommasani et al., 2021) on the opportunities and risks of "foundation models" are to be believed, these models represent a paradigm shift for AI and for the domains in which they will supposedly be used, including education.
Beyond "Fairness:" Structural (In)justice Lenses on AI for Education
Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn.
Equity Beyond Bias in Language Technologies for Education
There is a long record of research on equity in schools.
