SafeML2020: Safe Machine Learning Workshop European Conference on Artificial Intelligence Santiago de Compostela, Spain, June 8, 2020 |
Conference website | http://safeml.bitbucket.io |
Submission link | https://easychair.org/conferences/?conf=safeml2020 |
Abstract registration deadline | March 15, 2020 |
Submission deadline | March 15, 2020 |
Researchers, industry and society recognise the need for approaches that ensure the safe, beneficial and fair use of Machine Learning (ML) technologies. This workshop aims to bring together papers outlining the safety and fairness implications (from a legal, ethical, psychological, or technical point of view) of the use of ML in real-world systems, papers proposing methods to detect, prevent and/or alleviate undesired behaviors that ML-based systems might exhibit, papers analyzing the vulnerability of ML systems by adversarial attacks and the possible defense mechanisms, and, actually, any paper that stimulates discussion among researchers on different topics related to safe and fair ML.
Submission Guidelines
SafeML2020 welcomes original, unpublished papers. Papers must be written in English and should be between 12 and 15 pages in length . All submissions must follow the Springer's guidelines for authors. Authors are encouraged to use the provided Latex or Word templates to prepare the papers. Submissions are accepted only via the EasyChair conference management system at https://easychair.org/conferences/?conf=safeml2020 . Only the PDF of the manuscript is required for the first submission. Each submission will undergo a peer-review process with three peer reviews.
List of Topics
- Bias in Machine Learning
- Fairness and/or Safety in Machine Learning
- Safe Reinforcement Learning
- Safe Exploration for Optimization
- Safe Robot Control
- Adversarial Machine Learning and AI/ML robustness
- Adversarial examples and evasion attacks
- Data poisoning
- Backdoors in Machine Learning
- Reward Hacking
- Ethical and legal consequences of using Machine Learning in real-world systems
- Transparency in Machine Learning
Committees
Organizing committee
- Javier García, Universidad Carlos III de Madrid, Spain
- Moisés Martínez, Universidad Internacional de La Rioja
- Nerea Luis, Sngular, Madrid, Spain
- Luis Muñoz, Imperial College of London, London, UK
Program Committee
- Peter Stone, University of Texas at Austin, USA
- Ibrahim Habli, University of York, UK
- Stefanos Kollias, University of Lincoln, UK
- Wray Buntine, Monash University, Australia
- Marco Wiering, University of Groningen, The Netherlands
- Fernando Fernández, Universidad Carlos III de Madrid, Spain
- Philip S. Thomas, University of Massachusetts Amherst, USA
- Albert Bifet, Télécom ParisTech, France
- Kenneth T. Co, Imperial College of London, UK
- Adrià Garriga-Alonso, University of Cambridge, UK
- Mathieu Sinn, IBM Research, Ireland
- Alessandro Abate, University of Oxford, UK
- Andrea Aler Tubella, Umea University, Sweden
- Eleni Vasilaki, The University of Sheffield, UK
- Fabio Pierazzi, King's College London, UK
- Giulio Zizzo, Imperial College of London, UK
- Chris Hankin, Imperial College of London, UK
- Rohin Shah, University of California - Berkeley, USA
- Liwei Song, Princeton University, USA
- Tomas Svoboda, Czech Technical University in Prague, Czech Republic
- Adam Gleave, University of California - Berkeley, USA
- Theo Araujo, University of Amsterdam, The Netherlands
- Victoria Krakovna, DeepMind, UK
- Ann Nowé, Vrije Universiteit Brussel, Belgium
Publication
SafeML2020 will be submitted to Springer for publication in one of its book series.
Venue
The conference will be held in Santiago de Compostela, Spain.
Contact
All questions about submissions should be emailed to safeml2020@gmail.com