Asis&T SIG AI Workshop | October 30, 2021

AI in Information Research and Practice: Fostering Interconnected Communities

SIG AI Workshop at ASIS&T 2021

Salt Lake City, UT (USA) and Hybrid

Saturday, October 30, 2021; 8am-12pm Mountain Time

Program Information is Subject to Change.

Workshop schedule

8:00 – 8:10     Introduction (Soo Young Rieh) and Welcoming Remarks (Chirag Shah, Chair, SIG AI)

8:10 – 9:10     Panel Discussion


Kenneth R. Fleischmann (Professor in the School of Information at The University of Texas at Austin): Good Systems, a UT Grand Challenge: Fostering a Campus-Wide Interdisciplinary Research Team to Define, Build, and Evaluate Ethical AI

Aylin Caliskan (Assistant Professor in the Information School at the University of Washington & Nonresident Fellow in Governance Studies, Center for Technology Innovation, The Brookings Institution): Artificial Intelligence for Social Good: When Machines Learn Human-like Biases from Data

Amir Sadovnik (Assistant Professor of electrical engineering and computer science at the University of Tennessee): Adversarial Attacks and Their Implications on AI

Ryan Cordell (Associate Professor in the School of Information Sciences at the University of Illinois, Urbana—Champaign): Cultural Heritage Interfaces as Explainable AI


9:10 10:20     Lightning Talks

10:20 – 10:35    Break

10:35 – 11:00    Brainstorming session to identify challenges and opportunities of AI in library and information environments (Clara M. Chu and Dania Bilal)

11:00 11:35    Breakout groups to deep dive into topics identified in the brainstorming session (Clara M. Chu, Dania Bilal, Soo Young Rieh, Noora Hirvonen and Lala Hajibayova)

11:35 – 11:50    Plenary discussion on new collaboration opportunities and next steps in fostering/charting an ASIS&T AI community (Clara M. Chu)

11:50 – 12:00 Concluding Remarks (Dania Bilal)


Aylin Caliskan
Aylin Caliskan is an assistant professor in the Information School at the University of Washington. Caliskan's research interests lie in artificial intelligence (AI) ethics, bias in AI, machine learning, and the implications of machine intelligence on privacy and equity. She investigates the reasoning behind biased AI representations and decisions by developing theoretically grounded statistical methods that uncover and quantify the biases of machines. Building these transparency enhancing algorithms involves the use of machine learning, natural language processing, and computer vision to interpret AI and gain insights about bias in machines as well as society. Caliskan's publication in Science demonstrated how semantics derived from language corpora contain human-like biases. Their work on machine learning's impact on individuals and society received the best talk and best paper awards. Caliskan was selected as a Rising Star in EECS at Stanford University. Caliskan holds a Ph.D. in Computer Science from Drexel University's College of Computing & Informatics and a Master of Science in Robotics from the University of Pennsylvania. Caliskan was a Postdoctoral Researcher and a Fellow at Princeton University's Center for Information Technology Policy.


Ryan Cordell

Ryan Cordell is Associate Professor in the School of Information Sciences at the University of Illinois, Urbana—Champaign. His scholarship seeks to illuminate how technologies of production, reception, circulation, and remediation shape the sociology of texts. Cordell primarily studies circulation and reprinting in nineteenth-century American newspapers, but his interests extend to the influence of computation and digitization on contemporary reading, writing, and research. Cordell collaborates with colleagues in English, History, and Computer Science on the Viral Texts project, which uses robust data mining tools to discover borrowed texts across large-scale archives of nineteenth-century periodicals. Cordell is also a Senior Fellow in the Andrew W. Mellon Society of Critical Bibliography at the Rare Book School and serves as the Delegate Assembly Representative for the MLA's Forum on Digital Humanities. 


Ken Fleischmann 

Kenneth R. Fleischmann is a professor in the School of Information at The University of Texas at Austin. He is also the Founding Chair of the Executive Team for Good Systems, a UT Grand Challenge ( and the Founding Director of Undergraduate Studies for the iSchool's B.S.I./B.A. in Informatics. His research and teaching focus on the role of human values in the design and use of information technologies, specifically in the context of the ethics of AI. His research has been funded by the National Science Foundation (NSF), the Intelligence Advanced Research Projects Activity (IARPA), Microsoft Research, Microsoft Accessibility, Cisco Research, Micron Foundation, City of Austin, and the Public Interest Technology University Network. His research has been recognized by the iConference Best Paper Award, the iConference Best Short Research Paper Award, the ASIS&T SIG-USE Best Information Behavior Conference Paper Award, the ASIS&T SIG-SI Social Informatics Best Paper Award, the ALA Library Instruction Round Table Top Twenty Articles, the Civic Futures Award for Designing for the 100%, and the MetroLab Innovation of the Month Award.


Amir Sadovnik

Amir Sadovnik is an assistant professor of electrical engineering and computer science at the University of Tennessee. He received his PhD from the School of Electrical and Computer Engineering at Cornell University. His research in the fields of computer vision and machine learning has been mostly driven by trying to better understand how deep neural networks learn. His current research is mostly centered on adversarial attacks and there implications for both the robustness and trustworthiness of state-of-the-art machine learning methods. In this area he has explored research in both the image understanding domain as well as the natural language processing domain. His most recent work tries to train more explainable neural networks which in turn should be more robust to adversarial attacks.

Lightning Talks

Abstract of Talks <pdf>

* = 2021 IDEA Institute on AI Fellow [learn more...]

Artificial Intelligence (AI) practical implications in library operations technical and user services: Pakistani Perspective

Muhammad Yousuf Ali (The Aga Khan University, Karachi Pakistan)


AI and the creation of artificial cultures

*Martha Alvarado Anderson (University of Arkansas at Fayetteville)


Priming Artificial Intelligence for Research in Information Sciences

Jessica K. Barfield (University of Tennessee- Knoxville)


Predicting the Upcoming Topics and Actors of Fake News

Kevin Matthe Caramancion and Xiaojun (Jenny) Yuan (University at Albany, SUNY)


Explainable AI and Information Provision

Martin Frické (University of Arizona)


Perceived Fairness of Facial Recognition System Deployment in a University Setting

Hengyi Fu (University of Alabama)


Living and Working with Robots in the University Libraries

Elliott Hauser, Aaron Choate, and Katie Pierce Meyer (University of Texas at Austin)

The Affordances of AI for Everyday Information Practices

Noora Hirvonen (University of Oulu, Finland)


Affordances of AI for music-related information practices

Ville Jylhä (University of Oulu, Finland)

Finnish young people’s media literacy of deepfake

Yucong Lao (University of Oulu, Finland)


Ethical Obligations in Big Data & ML Research Support

*Casandra Laskowski (University of Arizona)


Using AI and ML to Optimize Information Discovery In Under-utilized, Holocaust-related Records

Richard Marciano (University of Maryland)


A Meta Framing Problem How Can A.I. Conceptualize Search as Learning?

Alamir Novin (University of British Columbia)


The Impact of AI on Information Technology Workforce

Sang Hoo Oh (Florida State University)


A Case Study Using NLP to Analyze LibChat Transcripts

*Meng Qu (Miami University)


Folk Theories and Explainable AI (XAI)

Michael Ridley (Western University)


Addressing Bias and Fairness in Search and Recommender Systems

Chirag Shah (University of Washington)


Information Science Informing Ethical AI

Ali Shiri & Toni Samek (University of Alberta, Edmonton, Alberta, Canada)


Reluctant and Non-Library Users: Is a chatbot the answer?

*Tienya Smith (Queens Public Library)


AI Fairness and Algorithmic bias

Emmanuel Sebastian Udoh (University at Albany) and Abebe Rorissa (University of Tennessee- Knoxville)


It takes a village: Building Mason-Library’s Orientation Conversational Agent through conversational marketing and human machine interaction techniques using a multi-agent approach

*Trevor Watkins (George Mason University)


Designing Responsible AI Systems for Older Adults: Opportunities and Challenges

Xiaojun Yuan, Bahareh Ansari, Mehdi Barati, Benjamin Yankson, Kevin Caramancion, George Berg, DeeDee Bennett Gayle (University at Albany, SUNY)