Call for Papers Journal Special Issue on Advances in Explainable and Responsible Artificial Intelligence
From explainable AI to responsible AI
Andreas Holzinger works on Human-Centered AI motivated by efforts to improve human health. Andreas pioneered in interactive machine learning with the human-in-the-loop, paving the way towards multimodal causability. Andreas promotes a synergistic approach to put the human-in-control of AI to align it with human values, privacy, security and safety.
From explainable AI to responsible AI
interpretable machine learning workshop beyond explainable ai
In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations.
In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations.
Please take part in the survey on ten-commandments for human-ai interaction, ethical legal and social issues of artificial intelligence
I just got granted a basic research project on explainability by the Austrain Science Fund
In the context of the First Austrian IFIP Forum and in my role as Austrian TC 12 representative and WG 12.9 working member, I provided a talk on “From Machine Learning to Explainable AI”
A new publication by the Lee Lab on explanations for ensemble tree-based predictions which results a exact solution that guarantees desirable explanation properties for global understanding
On Friday, May, 3, 2019 I had the great honor to provide an invited talk on explainable AI to the Austrian Council on Robotics and Artificial Intelligence …
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