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Social media systems refer to online computer systems that enable users to interact, collaborate, and compete with one another at societal-scale. Examples of social media systems include, social networking sites (Facebook & Google+), blogging and micro-blogging sites (Twitter & LiveJournal), content sharing sites (YouTube & Flickr), social bookmarking sites (Delicious & Reddit), crowd-sourced opinion sites (Yelp & eBay), and social peer production sites (Wikipedia & Amazon’s Mechanical Turk). With their rising popularity and a myriad of applications built on top of them, they have become an integral part of everyone’s lives today. In this course, we will be examining the usage and design of these social media systems using an interdisciplinary approach that combines social network analysis, natural language processing, and large-scale data analytics.

Seminar: Equitable Algorithms and Systems

Graduate seminar, University of Massachusetts Amherst, 2019

This seminar will focus on recent research into equitable algorithms and systems, broadly construed. This includes research that supports properties of fairness, accountability, and transparency, as well as respect for the privacy, safety, and equitable treatment of contributing individuals. Specific research topics will span systems, artificial intelligence, and theory.

Seminar: Equitable and Explainable Artificial Intelligence

Graduate seminar, University of Massachusetts Amherst, 2021

This seminar will focus on recent research into equitable and transparent algorithms and systems. We will review cutting-edge research that supports the properties of fairness, accountability, and transparency across various research areas, in particular fair machine learning, explainable artificial intelligence, and their interdisciplinary underpinnings. The seminar will offer introductory lectures describing the origins of relevant research problems, highlighting major threads and approaches in this vivid research space, and describing the relations between them. The course will primarily involve reading and discussing papers and book chapters.

Responsible Artificial Intelligence

COMPSCI 690F, University of Massachusetts Amherst, 2022

The real-world deployment of machine learning models faces a series of lateral challenges affecting model trustworthiness, such as domain generalization, dataset shifts, causal validity, explainability, fairness, representativeness, and transparency. These challenges become increasingly important in techno-social systems affecting human high-stake decision making, which is often regulated by law. In this course, students will learn techniques for robust model evaluation, model selection, causal discovery, explainable and fair artificial intelligence, and interpretable models. In addition, students will reason about representativeness, transparency, and legal aspects of techno-social systems. The course will review both cutting-edge research and relevant portions of recent open-access textbooks. Coursework includes reading recent research papers, programming assignments, and a final group project. After completing the course, students should be able to develop, investigate, evaluate, and deploy artificial intelligence systems more responsibly.

Responsible Artificial Intelligence

COMPSCI 690F, University of Massachusetts Amherst, 2023

The real-world deployment of machine learning models faces a series of lateral challenges affecting model trustworthiness, such as domain generalization, dataset shifts, causal validity, explainability, fairness, representativeness, and transparency. These challenges become increasingly important in techno-social systems affecting human high-stake decision making, which is often regulated by law. In this course, students will learn techniques for robust model evaluation, model selection, causal discovery, explainable and fair artificial intelligence, and interpretable models. In addition, students will reason about representativeness, transparency, and legal aspects of techno-social systems. The course will review both cutting-edge research and relevant portions of recent open-access textbooks. Coursework includes reading recent research papers, programming assignments, and a final group project. After completing the course, students should be able to develop, investigate, evaluate, and deploy artificial intelligence systems more responsibly.

Przemyslaw A. Grabowicz

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