HCI-outcomes of AI-based decision support in an economic decision task
AI has the potential to support human decision making in everyday situations. However, in sensitive areas, most state-of-the art algorithms face several challenges, such as algorithmic accountability, ethical considerations, and user acceptance. How should human and machine judgment be combined to tackle these challenges ? Should we work on more explainable AI-systems ? Should we simplify algorithmic decision support, which could also make algorithms more robust against uncertain environments ? Or should we search for an efficient interaction between human and machine, where the prior controls the output of the latter ? In collaboration with the student, we will conduct an experiment (e.g., ), to explore the interplay between human and AI-based decisions. By doing that, we could compare diverse AI-algorithms (e.g., CNN, Logistic Regression, Decision Tree) or decision environments (e.g., risk vs. uncertainty) in regard to diverse human-machine interaction outcomes, such as performance, comfort, and acceptance.
Keywords: Human-Computer-Interaction, Machine Learning, Transparent AI, Decision Making
Tasks (Scope depends on the type of Thesis)
Designing the user study (e.g., Tailorshop Decision Experiment);
Implementing diverse AI-based decision support systems;
Designing and Implementing the AI;
Collecting and analyzing user data;
Evaluating with HCI outcomes;
Comparing the Pros and Cons of AI-decision support;
What we offer
Access to a large pool of participants;
Professional advice in terms of Data Science and Hardware;
A pleasant working atmosphere and constructive cooperation;
Chances to publish your work on top conference;
Research at the intersection between Psychology and Technology;
Proactive and communicative work style;
Good English reading and writing;
Interest in working with Earable devices and interdisciplinary work;
Interested? Please contact: Tim Schneegans (firstname.lastname@example.org)
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