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Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education
To overcome these challenges and fully utilize the potential of ML in education, software practitioners need to work closely with educators and students to fully understand the context of the data (the backbone of ML applications) and collaboratively define the ML data specifications.
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Visualization for machine learning (VIS4ML) research aims to help experts apply their prior knowledge to develop, understand, and improve the performance of machine learning models.
fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks
To design with AI models, user experience (UX) designers must assess the fit between the model and user needs.
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
To address this problem, we bridge the literature on AI design and AI transparency to explore whether and how frameworks for transparent model reporting can support design ideation with pre-trained models.
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.
Towards A Process Model for Co-Creating AI Experiences
Therefore, designers and AI engineers must collaborate in new ways to create both the material and its application experience.
