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).


April 13th 2pm to 6pm BST

2:00 – 2:10 Welcome
2:10 – 2:40 Best Paper Presentation

  • The Tension Between Information Justice and Security: Perceptions of Facial Recognition Targeting; Elizabeth Watkins
2:40 – 3:00 Speed Dating
3:10 – 3:40 Panel 1: Fairness and Measuring Perceptions

  • A Study on Fairness and Trust Perceptions in Automated Decision Making; Jackob Schoeffer, Tvette Machowski, Niklas Kuehl
  • Machine Learning Explanations as Boundary Objectives: How AI Researchers Explain and Non-Experts Perceive Machine Learning; Amid Ayobi, Katarzyna Stawarz, Dmitri Katz, Paul Marshall, Taku Yamagata, Raul Santos-Rodriguez, Peter Flach, Aisling O’Kane
  • Fairness, Explainability, and What Lies in Between; Avital Shulner-Tal, Tsvika Kuflik, Doron Kliger
3:40 – 4:10 Panel 2: Explaining to Differing Stakeholders

  • Explaining Complex Machine Learning Platforms to Members of the General Public; Rachel Eardley, Ewan Soubutts, Amid Ayobi, Rachael Gooberman-Hill, Aisling O’Kane
  • Smart Move? Algorithmic Transparency in Career Transition Tools; Chantale Tippet
  • Making Business Partner Recommendation More Effective: Impacts of Combining Recommenders and Explanations through User Feedback; Oznur Alkan, Massimiliano Mattetti, Sergio Cabrero Barros, Elizabeth M. Daly
4:10 – 4:20 Break
4:20 – 4:50 Panel 3: Explanations for Interactive ML Systems

  • How to Manage Output Uncertainty: Targeting the Actual End User Problem in Interactions with AI; Zelun Tony Zhang, Heinrich Hußmann
  • Open, Scrutable and Explainable Interest Models for Transparent Recommendation; Mouadh Guesmi, Mohamed Amine Chatti, Yiqi Sun, Shadi Zumor, Fangzheng Ji, Arham Muslim, Laura Vorgerd, Shoeb Joarder
  • Interactive and Explainable AI for Advancing Organizational Justice in the Modern Workplace; Ishan Nigam, Min Kyung Lee
4:50 – 5:10 Poster Madness

  • AI Healthcare System Interface: Explanation Design for Non-Expert User Trust; Retno Larasati, Anna De Liddo, Enrico Motta
  • Contextualising Local Explanations for Non-Expert Users: an XAI Pricing Interface for Insurance; Clara Bove, Jonathan Aigrain, Marie-Jeanne Lesot, Charles Tijus, Marcin Detyniecki
  • Design Methods for Artificial Intelligence Fairness and Transparency; Simone Stumpf, Lorenzo Strappelli, Subeida Ahmed, Yuri Nakao, Aisha Naseer, Giulia Del Gamba, Daniele Regoli
  • The Human-AI Relationship in Decision-Making: AI Explanation to Support People on Justifying their Decision; Juliana Jansen Ferreira, Mateus de Souza Monteiro
  • Integrating Explainable AI in Medical Diagnosis through Parameterization and Implicitization; Mohammad Hossein Jarrahi, Mohammad Haeri
  • 3D4ALL: Toward an Inclusive Pipeline to Classify 3D Contents; Nahyun Kwon, Chen iang, Jeeun Kim
  • Investigating Explanations that Target Training Data; Ariful Islam Anik, Andrea Bunt
  • Position: Who Gets to Harness (X)AI? For Billion-Dollar Organizations Only; Jonathan Dodge
  • Understanding how Customers Attribute Accountability in Food Delivery Break Downs; Joice Tang, Ning F. Ma, Dongwook Yoon
5:10 – 5:50 Group Activity: Moderated Discussion of Themes
5:50 – 6:00 Closing