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home responsible decision making in dynamic environments icml 2022 workshop responsible decision making in dynamic environments icml 2022 workshop home schedule papers organizers submit responsible decision making in dynamic environments workshop at icml 2022 date july 23 2022 venue ballroom i baltimore convention center livestream https icml cc virtual 2022 workshop 13453 algorithmic decision making systems are increasingly used in sensitive applications such as advertising resume reviewing employment credit lending policing criminal justice and beyond the long term promise of these approaches is to automate augment and or eventually improve on the human decisions which can be biased or unfair by leveraging the potential of machine learning to make decisions supported by historical data unfortunately there is a growing body of evidence showing that the current machine learning technology is vulnerable to privacy or security attacks lacks interpretability or reproduces and even exacerbates historical biases or discriminatory behaviors against certain social groups most of the literature on building socially responsible algorithmic decision making systems focus on a static scenario where algorithmic decisions do not change the data distribution however real world applications involve nonstationarities and feedback loops that must be taken into account to measure and mitigate fairness in the long term these feedback loops involve the learning process which may be biased because of insufficient exploration or changes in the environment s dynamics due to strategic responses of the various stakeholders from a machine learning perspective these sequential processes are primarily studied through counterfactual analysis and reinforcement learning the purpose of this workshop is to bring together researchers from both industry and academia working on the full spectrum of responsible decision making in dynamic environments from theory to practice in particular we encourage submissions on the following topics fairness privacy and security robustness conservative and safe algorithms explainability and interpretability invited speakers aaron roth university of pennsylvania aaron roth is the henry salvatori professor of computer and cognitive science at the university of pennsylvania computer science department he received his phd from carnegie mellon university his main interests are in algorithms and machine learning and specifically in the areas of private data analysis fairness in machine learning game theory and mechanism design and learning theory craig boutilier google craig boutilier is principal scientist at google he was a professor in the department of computer science at the university of toronto on leave and canada research chair in adaptive decision making for intelligent systems his current research efforts focus on various aspects of decision making under uncertainty preference elicitation mechanism design game theory and multiagent decision processes economic models social choice computational advertising markov decision processes reinforcement learning and probabilistic inference cynthia rudin duke university cynthia rudin is a professor of computer science electrical and computer engineering statistical science mathematics and biostatistics bioinformatics at duke university she directs the interpretable machine learning lab whose goal is to design predictive models with reasoning processes that are understandable to humans her lab applies machine learning in many areas such as healthcare criminal justice and energy reliability she holds an undergraduate degree from the university at buffalo and a phd from princeton university she is the recipient of the 2022 squirrel ai award for artificial intelligence for the benefit of humanity from the association for the advancement of artificial intelligence the nobel prize of ai she is a fellow of the american statistical association the institute of mathematical statistics and the association for the advancement of artificial intelligence her work has been featured in many news outlets including the ny times washington post wall street journal and boston globe finale doshi velez harvard university finale doshi velez is a gordon mckay professor in computer science at the harvard paulson school of engineering and applied sciences she completed her msc from the university of cambridge as a marshall scholar her phd from mit and her postdoc at harvard medical school her interests lie at the intersection of machine learning healthcare and interpretability masoud mansoury university of amsterdam masoud mansoury is a postdoctoral researcher at amsterdam machine learning lab at university of amsterdam netherlands he is also a member of discovery lab collaborating with the data science team at elsevier company in the area of recommender systems masoud received his phd in computer and information science from eindhoven university of technology netherlands in 2021 he has published his research works in top conferences such as facct recsys and cikm his research interests include recommender systems algorithmic bias and contextual bandits solon barocas microsoft cornell university solon barocas is a principal researcher in the new york city lab of microsoft research and an adjunct assistant professor in the department of information science at cornell university his research explores ethical and policy issues in artificial intelligence particularly fairness in machine learning methods for bringing accountability to automated decision making and the privacy implications of inference news accepted papers and talks are now visible camera ready and video submission deadlines are updated in important dates notifications for accepted papers are out call for papers is out last date to submit is may 31 2022 please check instructions on how to submit contact us the organizers may be reached at responsibledecisionmaking at gmail dot com related past workshops socially responsible machine learning srml iclr 2022 socially responsible machine learning icml 2021 learning in presence of strategic behavior neurips 2021 workshop on responsible ai iclr 2021 workshop on consequential decision making in dynamic environments neurips 2020 law machine learning lml icml 2020 workshop on human interpretability in machine learning whi icml 2020 reinforcement learning for real life workshop icml 2021 safe and robust control of uncertain systems neurips 2021 political economy of reinforcement learning perls workshop neurips 2021 workshop to discuss the current challenges and possible solutions of responsible sequential decision making based on a jekyll template from a lazy grad student some icons made by smashicons from www flaticon com last updated july 23 2022