Fourth edition on
Human-Centered AI: Knowledge and Language
The Academic Fringe Festival is an exciting concoction of invited talks and panel discussions around important themes of research and innovation in Computer Science. This fourth edition is on "Human-Centered AI: Knowledge and Language". The series features prominent researchers and practitioners, whose work has made fundamental contributions in these fields.
Artificial Intelligence is used more and more in society, from healthcare to government decisions and recruitment. Along with the rapid increase of AI adoption comes increased concerns about the inherent shortcomings of such technologies (e.g., robustness) and the social, and ethical implications. To create AI systems that can properly serve humans, it is crucial to put humans at the center of the process such that the outcome system behaves in a way that fits the values and needs of people. This poses new challenges to technological development: how to build AI systems that can be understood by humans and that can align their behaviour with human values? Tackling these challenges requires new ways of looking at AI systems, e.g., machine learning models as knowledge bases and as autonomous agents that people can query, interact with, and influence.
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Designing Virtual Knowledge Graphs [more details]
Free University of Bozen-Bolzano
Human-Centred AI in Mental Health [more details]
Microsoft Health Futures
The New DBfication of ML/AI [more details]
University of California, San Diego
Chatbots for Second Language Learning [more details]
Luc De Raedt
From Statistical Relational to Neuro-Symbolic Artificial Intelligence [more details]
Responsible AI Mitigations and Tracker: New open-source tools for guiding mitigations in Responsible AI [more details]
Trustworthy Enterprise AI: crossing the chasm among LLMs, AI services, and Enterprise AI systems [more details]
University of Trento
SmartBook: AI-Assisted Situation Report Generation [more details]
University of Illinois Urbana-Champaign
System Infrastructure for Data-centric ML Pipelines - Balancing Automation and Manual Control [more details]
Technische Universität Berlin