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Found 19 papers, 2 papers with code
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.
Designing for human-AI complementarity in K-12 education
Recent work has explored how complementary strengths of humans and artificial intelligence (AI) systems might be productively combined.
Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?
The development of educational AI (AIEd) systems has often been motivated by their potential to promote educational equity and reduce achievement gaps across different groups of learners -- for example, by scaling up the benefits of one-on-one human tutoring to a broader audience, or by filling gaps in existing educational services.
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment
Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as fully-automated approaches are unlikely to be perfectly safe and reliable.
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 Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.
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.
Team Learning as a Lens for Designing Human-AI Co-Creative Systems
Here we reframe human-AI collaboration as a learning problem: Inspired by research on team learning, we hypothesize that similar learning strategies that apply to human-human teams might also increase the collaboration effectiveness and quality of humans working with co-creative generative systems.
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables
Our findings indicate that presenting prompts about unobservables can change how humans integrate model outputs and unobservables, but do not necessarily lead to improved performance.
Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems.
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making
In this paper, we identify five sources of target variable bias that can impact the validity of proxy labels in human-AI decision-making tasks.
Counterfactual Prediction Under Outcome Measurement Error
We also develop a method for estimating treatment-dependent measurement error parameters when these are unknown in advance.
Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools
AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design 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.
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.
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.
Predictive Performance Comparison of Decision Policies Under Confounding
However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors.
