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Causal algorithmic fairness and transparency
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Date: 30th September 2020, 5:00 PM – 6:00 PM CEST
Causal algorithmic fairness and transparency
Speaker: Babak Salimi is an Assistant Professor in HDSI at UC San Diego. Before joining UC San Diego, he was a Postdoctoral Research Associate in the Department of Computer Science and Engineering, University of Washington, where he worked with Professor Dan Suciu and the Database group. He received his PhD from the School of Computer Science at Carleton University, advised by Professor Leopoldo Bertossi. His research seeks to unify techniques from theoretical data management, causal inference and machine learning to develop a new generation of decision-support systems that help people with heterogeneous background to interpret data. His ongoing work in causal relational learning aims to develop the necessary conceptual foundations to make causal inference from complex relational data. Further, his research in the area of responsible data science develops needed foundations for ensuring fairness and accountability in the era of data-driven decisions. His research contributions have been recognized with a Postdoc Research Award at University of Washington, a Best Demonstration Paper Award at VLDB 2018, a Best Paper Award at SIGMOD 2019 and a Research Highlight Award at SIGMOD 2020.
Abstract:
Scaling and democratizing access to big data promises to provide meaningful and actionable information that supports decision-making. Today, data-driven decisions profoundly affect the course of our lives, such as whether to admit applicants to a particular school, offer them a job, or grant them a mortgage. Unfair, inconsistent, or faulty decision-making raises serious concerns about ethics and responsibility. For example, we may know that our training data is biased, but how do we avoid propagating discrimination when we use this data? How do we avoid incorrect, spurious and non-reproducible findings? How can we curate and expose existing data to make it “safe” for informed decision-making? In this talk, I describe how we can combine techniques from causal inference and data management to develop systems and algorithms that help answer questions about fairness and transparency. First, I present a new notion of fairness that subsumes and improves upon previous definitions and correctly distinguishes between fairness violations and non-violations. Further, I discuss how we can leverage techniques from data management to remove historical discrimination from data. Second I present a novel declarative framework that enables reasoning about fairness and discrimination from complex relational data. Finally, I present my ongoing projects that use counterfactual reasoning and provenance for explaining black-box decision-making algorithms.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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