Steps toward trustworthy machine learning

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Steps toward trustworthy machine learning

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  • How can we trust systems built from machine learning components? We need advances in many areas, including machine learning algorithms, software engineering, ML ops, and explanation. This talk will describe our recent work in two important directions: obtaining calibrated performance estimates and performing run-time monitoring with guarantees. I will first describe recent work by Jesse Hostetler on performance guarantees for reinforcement learning. Then I’ll review our research on providing guarantees for open category detection and anomaly detection for run-time monitoring of deployed systems. I’ll conclude with some speculations concerning meta-cognitive situational awareness for AI systems.


    Artificial Intelligence (AI) systems have steadily grown in complexity, gaining predictivity often at the expense of interpretability, robustness and trustworthiness. Deep neural networks are a prime example of this development. While reaching “superhuman” performances in various complex tasks, these models are susceptible to errors when confronted with tiny (adversarial) variations of the input – variations which are either not noticeable or can be handled reliably by humans. This expert talk series will discuss these challenges of current AI technology and will present new research aiming at overcoming these limitations and developing AI systems which can be certified to be trustworthy and robust.

    The expert talk series will cover the following topics:

    • Measuring Neural Network Robustness
    • Auditing AI Systems
    • Adversarial Attacks and Defences
    • Explainability & Trustworthiness
    • Poisoning Attacks on AI
    • Certified Robustness
    • Model and Data Uncertainty
    • AI Safety and Fairness

    The Trustworthy AI series is moderated by Wojciech Samek, Head of AI Department at Fraunhofer HHI, one of the top 20 AI labs in the world.

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