VL/HCC 2022
Mon 12 - Fri 16 September 2022 Rome, Italy

Confirmation bias is a type of cognitive bias that involves seeking and prioritizing information that conforms to a pre-existing view or hypothesis that can negatively affect the decision-making process. We investigate the manifestation and mitigation of confirmation bias with an emphasis on the use of visualization. In a series of Amazon Mechanical Turk studies, participants selected evidence that supported or refuted a given hypothesis. We demonstrated the presence of confirmation bias and investigated the use of five simple visual representations, using color, positional, and length encodings for mitigating this bias. We found that at worst, visualization had no effect in the amount of confirmation bias present, and at best, it was successful in mitigating the bias. We discuss these results in light of factors that can complicate visual debiasing in non-experts.

Tue 13 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:00 - 15:30
Session on Human-centric ML & VisualizationsResearch Papers at San Francesco Room
Chair(s): Sandeep Kuttal The University of Tulsa
14:00
30m
Talk
The Role of Expertise on Insight Generation from Visualization SequencesFull paper
Research Papers
Stephanie Rosenthal Carnegie Mellon University, Tingting Chung College of William & Mary
DOI
14:30
15m
Talk
Predicting Data Scientist Stuckness During the Development of Machine Learning ClassifiersShort paper
Research Papers
Moshe Mash CMU, Shoshana Oryol CMU, Reid Simmons CMU, Stephanie Rosenthal Carnegie Mellon University
DOI
14:45
15m
Talk
A Crowdsourced Study of Visual Strategies for Mitigating Confirmation BiasShort paper
Research Papers
Tee Chuanromanee University of Notre Dame, Ronald Metoyer University of Notre Dame
DOI
15:00
15m
Talk
ML Blocks: A Block-Based, Graphical User Interface for Creating TinyML ModelsShort paper
Research Papers
Randi Williams Massachusetts Institute of Technology, MichaƂ Moskal Microsoft Research, Peli de Halleux Microsoft Research
DOI
15:15
15m
Talk
Human-Centric Machine Learning for Temporal Knowledge Graphs: Towards Understanding the European Alternative Fuels MarketShort paper
Research Papers
Robert Jungnickel RWTH Aachen University - Information Management in Mechanical Engineering, Aymen Gannouni RWTH Aachen University - Information Management in Mechanical Engineering, Anas Abdelrazeq RWTH Aachen University - Information Management in Mechanical Engineering, Ingrid Isenhardt RWTH Aachen University - Information Management in Mechanical Engineering
DOI