AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings

• URL: http://arxiv.org/abs/2001.05375v1
• Date: Wed, 15 Jan 2020 15:30:29 GMT
• Title: AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
• Authors: Florian Buettner, John Piorkowski, Ian McCulloh, Ulli Waltinger
• Abstract summary: It is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. The focus of this symposium was on AI systems to improve data quality and technical robustness and safety. submissions from broadly defined areas also discussed approaches addressing requirements such as explainable models, human trust and ethical aspects of AI.
• Score: 8.445274192818825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving or medicine, but also in dynamic open world systems in industry and government it is crucial for predictive models to be uncertainty-aware and yield trustworthy predictions. Another key requirement for deployment of AI at enterprise scale is to realize the importance of integrating human-centered design into AI systems such that humans are able to use systems effectively, understand results and output, and explain findings to oversight committees. While the focus of this symposium was on AI systems to improve data quality and technical robustness and safety, we welcomed submissions from broadly defined areas also discussing approaches addressing requirements such as explainable models, human trust and ethical aspects of AI.

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