Join us for a one-day, hybrid workshop featuring presentations, group activities, and discussions on real-world examples of contesting AI across the value chain. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts.
<aside> <img src="/icons/location_gray.svg" alt="/icons/location_gray.svg" width="40px" /> San José, Costa Rica, Costa Rica Convention Center, Central 2
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<aside> <img src="/icons/calendar_gray.svg" alt="/icons/calendar_gray.svg" width="40px" /> November 9th, 2024
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<aside> <img src="/icons/video-camera_gray.svg" alt="/icons/video-camera_gray.svg" width="40px" /> In Person Activity & Virtual Talks
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In recent years, a growing awareness of the societal impact of AI systems has spurred a crucial dialogue within the CSCW, HCI, AI communities and beyond. The concept of contestable AI has emerged as a response to negative consequences, emphasizing the need for AI systems to be open to human intervention and dispute. This workshop aims to explore the impacts, challenges, and opportunities of contestable AI along the AI value chain.
Contestable AI is gaining traction beyond theoretical discussions. Real-world examples, such as activist efforts against data centers in Chile, Ireland, and the Netherlands, highlight the need for public discourse and policy debates. Taking a value-chain perspective reveals challenges in assigning responsibility, as negative AI outcomes often stem from the actions of multiple actors. This raises crucial questions: What and Who exactly are we contesting, and who precisely bears the responsibility to address the contestation? This workshop seeks to address these questions and explore collective approaches to contesting AI.
Our goal is to foster an interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders. We invite empirical studies, real-world case studies, and reflections on contestable AI in diverse contexts.