Dr. rer. nat.

Rüping, Stefan

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Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

2022-06-18 , Houben, Sebastian , Albrecht, Stefanie , Akila, Maram , Bär, Andreas , Brockherde, Felix , Feifel, Patrick , Fingscheidt, Tim , Gannamaneni, Sujan Sai , Ghobadi, Seyed Eghbal , Hammam, Ahmed , Haselhoff, Anselm , Hauser, Felix , Heinzemann, Christian , Hoffmann, Marco , Kapoor, Nikhil , Kappel, Falk , Klingner, Marvin , Kronenberger, Jan , Küppers, Fabian , Löhdefink, Jonas , Mlynarski, Michael , Mock, Michael , Mualla, Firas , Pavlitskaya, Svetlana , Poretschkin, Maximilian , Pohl, Alexander , Ravi-Kumar, Varun , Rosenzweig, Julia , Rottmann, Matthias , Rüping, Stefan , Sämann, Timo , Schneider, Jan David , Schulz, Elena , Schwalbe, Gesina , Sicking, Joachim , Srivastava, Toshika , Varghese, Serin , Weber, Michael , Wirkert, Sebastian , Wirtz, Tim , Woehrle, Matthias

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.

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Publication

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

2021 , Houben, Sebastian , Abrecht, Stephanie , Akila, Maram , Bär, Andreas , Brockherde, Felix , Feifel, Patrick , Fingscheidt, Tim , Gannamaneni, Sujan Sai , Ghobadi, Seyed Eghbal , Hammam, Ahmed , Haselhoff, Anselm , Hauser, Felix , Heinzemann, Christian , Hoffmann, Marco , Kapoor, Nikhil , Kappel, Falk , Klingner, Marvin , Kronenberger, Jan , Küppers, Fabian , Löhdefink, Jonas , Mlynarski, Michael , Mock, Michael , Mualla, Firas , Pavlitskaya, Svetlana , Poretschkin, Maximilian , Pohl, Alexander , Ravi-Kumar, Varun , Rosenzweig, Julia , Rottmann, Matthias , Rüping, Stefan , Sämann, Timo , Schneider, Jan David , Schulz, Elena , Schwalbe, Gesina , Sicking, Joachim , Srivastava, Toshika , Varghese, Serin , Weber, Michael , Wirkert, Sebastian , Wirtz, Tim , Woehrle, Matthias

The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from safety concerns. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. This work provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning (ML) topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern ML methods. We moreover hope that our contribution fuels discussions on desiderata for ML systems and strategies on how to propel existing approaches accordingly.

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