Projects per year
Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted results are to the target. This simplifies away all the details of the decision-making process of the expert. In this work, we allow the experts to additionally produce decision rules describing their decision-making; the rules are expected to be imperfect but to give additional information. In particular, the rules can extend to new distributions, and hence enable significantly improving performance for cases where the training and testing distributions differ, such as in domain adaptation. We apply the proposed method to lifelong learning and domain adaptation problems and discuss applications in other branches of AI, such as knowledge acquisition problems in expert systems. In simulated and real-user studies, we show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
|Title of host publication||26th International Conference on Intelligent User Interfaces, IUI 2021|
|Number of pages||5|
|Publication status||Published - 14 Apr 2021|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Intelligent User Interfaces - Virtual, Online, College Station, United States|
Duration: 13 Apr 2021 → 17 Apr 2021
Conference number: 26
|Conference||International Conference on Intelligent User Interfaces|
|Period||13/04/2021 → 17/04/2021|
- domain adaptation
- human computer interaction
- knowledge elicitation
- user studies
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- 2 Finished
Kaski, S. & Filstroff, L.
01/01/2016 → 31/08/2021
Project: Academy of Finland: Other research funding