About the Workshop
Smart systems that apply complex reasoning to make decisions, such as decision support or recommender systems, are difficult for people to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making; however, there are increasing concerns surrounding their fairness, bias, and accountability, as these processes are typically opaque to users. Transparency and accountability have attracted increasing interest toward more effective system training, better reliability, appropriate trust, and improved usability. The workshop on Transparency and Explanations in Smart Systems (TExSS) will provides a venue for exploring issues when designing, developing, or evaluating transparent intelligent user interfaces, with additional focus on explaining systems and models toward ensuring fairness and social justice.
This workshop was preceded by the joint workshop on Explainable Smart Systems for Algorithmic Transparency in Emerging Technologies (ExSS-ATEC 2020), 2nd Workshop on Explainable Smart Systems (ExSS 2019), and the 2nd International Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies (IUI-ATEC 2019).
Schedule & Papers
The TExSS workshop has merged with the HUMANIZE workshop to provide an exciting program.
The 2022 event will be held completely virtually on 21 March. All times are CET.
|SESSION 1: INTRODUCTION (Session Chair: Stella Kleanthous)|
|16:00 – 16:10||Welcome by organizers|
|16:10 – 17:00||Keynote (30 min + 20 min Q&A
Experiencing the COVID-19 pandemic through the lens of Google
Frank Hopfgartner is a senior lecturer in Data Science at the Information School of University of Sheffield. His research to date can be placed in the intersection of information systems (e.g., information retrieval and recommender systems), content analysis and data science. He has (co-) authored over 150 publications in above mentioned research fields, including a book on smart information systems, various book chapters and papers in peer-reviewed journals, conferences and workshops.
|17:00 – 17:10||Break|
|SESSION 2: CONCEPTS (Session Chair: Marko Tkalcic)|
|17:10 – 17:30||A Framework for Predicting Fairness Perception – Towards Personalized Explanations of Algorithmic Systems Results
Avital Shulner Tal, Doron Kliger and Tsvi Kuflik
|17:30 – 17:50||Position: The Case Against Case-Based Explanation
|17:50 – 18:10||Is explainable AI a race against model complexity?
|18:10 – 18:30||Development of an Instrument for Measuring Users’ Perception of Transparency in Recommender Systems
Marco Hellmann, Diana C. Hernandez-Bocanegra and Jürgen Ziegler
|18:30 – 18:40||Break|
|SESSION 3: APPLICATIONS (Session Chair: Jon Dodge)|
|18:40 – 19:00||Explaining Podcast Recommendations To Users with Content Diversity Labels
Bernd Huber, Yixue Wang, Jean Garcia-Gathright and Jenn Thom
|19:00 – 19:20||Supporting Responsible Data and Algorithmic Practices in The News Media
|19:20 – 19:40||Towards Understanding the Transparency of Automations in Daily Environments
|19:40 – 19:50||Break|
|SESSION 4: INTERACTION (Session Chair: Tsvi Kuflik)|
|19:50 – 20:40||Moderated group discussion|