April 22-23, 2021. Free and open to the public Streaming online This conference brings together diverse scholars whose work grapples with the challenges that climate change presents to the discipline of history. Participants will address precedents for this “unprecedented” crisis by uncovering and analyzing the historical roots and analogues of contemporary climate change across a wide range of eras and areas around the world. Can history offer an alternative to visions of the future that appear to be determined by prevailing climate models, and help provide us with new ways of understanding human agency?  Conference program, registration, and additional details forthcoming. Stay up to date on conference announcements by following the Institute for Historical Studies on Twitter and Facebook. Queries: cmeador@austin.utexas.edu.

Join Kate Crawford and Simone Browne in conversation on Crawford's groundbreaking new book Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence. Drawing on over a decade of research, Atlas of AI is a rigorous interrogation of the power relations that undergird artificial intelligence - from the stories we are sold about datasets and digital assistants, to mineral extraction and the exploitative labor conditions that sustain AI. Hosted by Good Systems' Critical Surveillance Inquiry RFA. Learn more and register now! 

This session of the Austin Forum will bring a panel of UT Austin faculty to share recent findings and lessons from developing fair and transparent AI technologies. In addition to being part of Good Systems, all of the faculty are also members of UT’s interdisciplinary Machine Learning Laboratory. Learn more and register. 

Featured Speakers

Matthew Lease, Associate Professor, The University of Texas at Austin, School of Information Maria De-Arteaga, Professor, The University of Texas at Austin, Information, Risk and Operation Management Department Joydeep Ghosh, Professor, The University of Texas at Austin, Department of Electrical and Computer Engineering, Chief Scientist, CognitiveScale Raymond J. Mooney, Professor, The University of Texas at Austin, Department of Computer Science Min Kyung Lee, Ph.D., Assistant Professor, The University of Texas at Austin, School of Information

Learn about Dell Technologies’ strategic vision for the future, including how AI will need to be developed over the next years to achieve their long-term goals. Dell’s decisions will help lay the groundwork for the future of the AI job market. Our conversation with company leaders also will help spur future collaborative opportunities between Dell Technologies and UT Good Systems. Presentation from Michael Shepherd (Distinguished Engineer, Dell Technologies) and Brons Larson (AI Strategy Lead, Dell Technologies).  Register now! Part of the Future of Work Research Presentation series.

Dhiraj Murthy (Professor, School of Journalism and Media) will share his COVID-related work: “Classifying COVID-19-related disinformation and misinformation using crowdsourcing and machine learning." Email goodsystems@austin.utexas.edu to request the meeting information.

Pecan Street works with advanced energy systems including smart inverters, energy storage, controlled electric vehicle charging, HVAC demand response and V2G system testing, as well as residential electrical system issues that are not being addressed by these technologies.  Scott Hinson is the CTO of Pecan Street Inc. Prior to joining Pecan Street he worked at a thin film CIGS solar module manufacturer where he led module packaging, performance, certification and reliability efforts. Additional efforts include work in the military, medical, consumer and oil industries developing power supplies, precision measurement equipment and inductive heating technologies.  Part of the Smart Cities Consortium.

Machine learning (ML) is increasingly being used to support decision-making in critical settings, where predictions have potentially grave implications over human lives. In this talk, Maria De-Arteaga (Assistant Professor, Information, Risk and Operations Management) will discuss the gap that exists between ML predictions and ML-informed decisions. The first part of the talk will highlight the role of humans-in-the-loop through a study of the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. The second part of the talk will focus on the gap between the observed outcome that the algorithm optimizes for and the construct of interest to experts. Learn more and register now.  Part of the Ethics in AI Seminar Series hosted by The Institute for Foundations of Machine Learning. 

This panel will feature C. J. Alvarez (Assistant Professor, Department of Mexican American and Latino/a Studies), Jay L. Banner (F. M. Bullard Professor of Geological Sciences, the Jackson School of Geosciences, and Director, Environmental Science Institute), Alison M. Meadow (Associate Research Professor, Arizona Institutes for Resilience, University of Arizona), and Christopher Sellers (Professor of History, Stony Brook University, and Research Fellow, Institute for Historical Studies). Learn more.  This panel is part of the Institute for Historical Studies' theme in 2020-2021 on "Climate in Context: Historical Precedents and the Unprecedented."

Health misinformation is a public health concern and the WHO has considered it a public health threat even before the COVID-19 pandemic. Although healthcare professionals, such as nurses and doctors, are in the frontlines during this pandemic, some are serving as digital first respondents – individuals who are sharing accurate health information on social media to counteract health misinformation. In this talk, John Robert Bautista (Bullard Research Fellow, School of Information) will present a conceptual framework that shows how healthcare professionals correct health misinformation on social media. He will also share preliminary findings from a survey study that extends his qualitative findings, including future work on examining the impact of healthcare professionals’ correction of health misinformation on social media. Register now! Part of Good Systems' Postdoc Interest Group.

Artificial Intelligence (AI) is increasingly deployed in supporting decision making in an ever-broader scope of environments, and its impact can often be difficult to trace, let alone challenge on its own terms. As agency over the outcomes of AI are themselves distributed across its infrastructures, extant policy structures, in vivo uses of deployed technology, and the values embedded in the technology throughout the design process, the notion of accountable AI similarly must address multiple points in the AI ecosystem. Accountability thus might be more productively considered less in terms of how AI is built, and more in terms of how AI might be understood, legislated, regulated, and contested. Presentation from Stephen Slota (Postdoctoral Research, School of Information).  Register now. Part of the Future of Work Research Presentation series.