The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.
In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.
no code implementations • 2 Feb 2022 • Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo, Ibrahim Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alex Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, YuAn Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine Heller, Silvia Chiappa, Alexander D'Amour
Fairness and robustness are often considered as orthogonal dimensions when evaluating machine learning models.
This paper considers the problem of selecting a formula for identifying a causal quantity of interest among a set of available formulas.
no code implementations • 21 Oct 2021 • Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.
Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology.
Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the use of discrete OT.
Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time.
We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.
We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.
no code implementations • 8 May 2019 • Pedro A. Ortega, Jane. X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways.
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.