ICLR 2020 Workshop Machine Learning in Real-Life (ML-IRL) Workshop at International Conference on Learning Representations Sunday April 26, 2020 Addis Ababa, Ethiopia https://sites.google.com/nyu.edu/ml-irl-2020/
Key dates: * January 25, 2020: Submission deadline * February 25, 2020: Acceptance notification * April 26, 2020: Workshop ABOUT: ML-IRL is about the challenges of real-world use of machine learning and the gap between what ML can do in theory and what is needed in practice. Given the tremendous recent advances in methodology from causal inference to deep learning, the strong interest in applications (in health, climate and beyond), and discovery of problematic implications (e.g. issues of fairness and explainability) now is an ideal time to examine how we develop, evaluate and deploy ML and how we can do it better. We envision a workshop that is focused on productive solutions, not mere identification of problems or demonstration of failures. COMMITMENT TO DIVERSITY: We believe one of the keys to making ML that really works is involving a diverse set of people and perspectives in its development, deployment, and evaluation. Our program committee spans academia and industry across four continents and has experience ranging from theoretical machine learning to legal implications of AI. We welcome all submissions that share our goal of ML in IRL, and especially encourage submissions from researchers who may not regularly attend ICLR or other ML conferences. We will have a limited number of free guaranteed registrations available for local students and researchers. Details on how to apply will be posted on the workshop website soon! CONFIRMED KEYNOTE SPEAKERS: *Andreas Gros, Facebook *Nyalleng, Moorosi, Google AI Lab Ghana *Susan Murphy, Harvard University *Suchi Saria, Johns Hopkins TOPICS: We aim to examine how real-world applications can and should influence every stage of ML, from how we develop algorithms to how we evaluate them. The key themes of the workshop are foundational challenges that are domain independent: *Methods -Data collection and algorithms designed for the challenges of real-world data -Causal inference with realistic assumptions -Transportability across contexts and domains -Observational data and deep learning *Applications -Grand challenges and blindspots for specific domains -How to avoid failures *Implications -How can we build properties like fairness in from the start? -The gap between what is possible (in terms of data/law/society) and common methodological assumptions -Human and ML interaction and collaboration, and considering humans in ML development more broadly This list is not exhaustive and we both welcome and encourage submissions on all aspects of machine learning in real-life! SUBMISSIONS: *Short papers and position pieces (4 pages) *Problem statements and abstracts (1 page) 4-page submissions will be eligible for oral or poster presentation. One page submissions will be presented as posters. Contributions should be blinded and submitted using the ICLR template via EasyChair. The link will be posted on the website soon. ORGANIZERS: Samantha Kleinberg (Stevens Institute of Technology) Rumi Chunara (New York University)
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