Connectionists: Join us on Monday in our Workshop on Trustworthy AI in the Wild

[Malte Schilling]

We want to invite you to our workshop on Trustworthy AI in the Wild - next Monday, 27th of September, starting at 10 AM (European time).
Regards

	Barbara Hammer, Malte Schilling, Laurenz Wiskott

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Schedule

The workshop will be held in zoom (Meeting ID: 962 7072 7489, Passcode: 037056)
The poster session will be starting at 12 PM: https://app.wonder.me/?spaceId=ae85bc72-43df-4882-ad5a-0233c98deb1f

10:00-10:05  Welcome and Introduction (Barbara Hammer)
10:05-10:45  Keynote Talk: Isabel Valera “Algorithmic recourse: theory and practice”
10:45-11:00  Invited talk: Ulrike Kuhl “Towards an Empirical Analysis of Counterfactual Explanations for Machine Learning”
11:00-11:45  Keynote Talk: Marc Toussaint “Physical Reasoning: If I only could explain why it doesn’t work”
11:45-12:00 Poster pitches of 2 minutes 

12:00 Poster session will be held in wonder.me - https://app.wonder.me/?spaceId=ae85bc72-43df-4882-ad5a-0233c98deb1f

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Topic

AI solutions start to have an enormous impact on our lives: they are key enabler of future digital industry, potential game-changer for experimentation and discovery in science, and prevalent technology in everyday services such as internet search or human-machine communication. Moreover, AI is involved in the solutions of humans’ grand challenges, examples being AI-based environment-friendly mobility concepts, augmentation of human capabilities by intelligent assistive systems in an ageing society, or support in developing medical therapies or vaccines. 

Yet, the very nature of AI technologies includes a number of novel threats which need to be addressed for trustworthy AI: many machine learning models act as black boxes which can lead to unexpected behavior, for example, when human and machine perception differ considerably. As models are trained on real-life data, there is the risk that such AI models allow for unauthorized access to sensitive information that might be contained in the data. Furthermore, data biases —caused by spurious correlations in the data— can be captured by ML models and their predictions, leading to systematically disadvantages for specific individuals or (e.g. ethnic) groups. The ubiquity of AI in virtually every aspect of life, therefore, has an enormous impact on the way in which we as a society communicate, work, decide, and interact. 
Hence novel concepts on how to guarantee security, safety, privacy, and fairness of AI and how to create AI systems which support humans rather than incapacitating them are of uttermost importance and are constituting the research area on Trustworthy AI. Trustworthy AI aims for technologies that not only provide solutions to an earlier defined task, but that as well allow for insight on the functioning of the underlying system. Why did the system acted in a certain way and did not choose a different solution? Which features were important for the decision and how sure is the system of its choice, i.e. can I trust this decision? 
The workshop aims, first, at understanding Machine Learning based approaches towards explainable AI solutions. Secondly, a focus of the workshop is on how we can make AI solutions more trustworthy. The goal of the workshop is to discuss existing concepts of trustworthy AI and provide a platform for the formulation of new ideas and proposals to overcome existing limitations.

The workshop aims at interested researchers with a background in machine-learning (supervised and unsupervised learning, reinforcement learning), traditional AI techniques (reasoning, planning), robotics (humanoids, probabilistic robotics), HMI/HRI, and multi-agent systems (coordination and cooperation).

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It is part of the GI Conference 2021 and the KI 2021 (both virtually), see https://ki2021.uni-luebeck.de .

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Further Information

For further information, please contact Malte Schilling (mschilli at techfak.uni-bielefeld.de) and see the website containing more information: https://dataninja.nrw/?page_id=343 

Organizers: 
Barbara Hammer (CITEC, Bielefeld University, Germany),
Malte Schilling (Machine Learning Group, Bielefeld University),
Laurenz Wiskott (Institute for Neural Computation, Ruhr-University Bochum).