A Survey of Domain Knowledge Elicitation in Applied Machine Learning
Abstract
:1. Introduction
2. Related Work
2.1. Understanding ML Practice
2.2. Knowledge Elicitation for Expert Decision Making
3. Materials and Methods
3.1. Scope
3.2. Sample Collection
3.3. Content Analysis
4. Elicitation Taxonomy
4.1. Elicitation Goal
4.2. Elicitation Target
4.3. Elicitation Process
4.4. Use of Elicited Knowledge
5. Results
5.1. Characterizing Elicitation Paths
5.1.1. Problem Specification
5.1.2. Feature Engineering
5.1.3. Model Development
5.1.4. Model Evaluation
5.2. Gaps and Opportunities
5.2.1. Transparency and Traceability
5.2.2. Systematic Use of Elicited Knowledge
5.2.3. Motivating What Is Elicited
5.2.4. Establishing Context and Common Ground
5.2.5. Cognitive Bias
5.2.6. Validation of Elicited Information
6. Future Work and Limitations
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Kerrigan, D.; Hullman, J.; Bertini, E. A Survey of Domain Knowledge Elicitation in Applied Machine Learning. Multimodal Technol. Interact. 2021, 5, 73. https://doi.org/10.3390/mti5120073
Kerrigan D, Hullman J, Bertini E. A Survey of Domain Knowledge Elicitation in Applied Machine Learning. Multimodal Technologies and Interaction. 2021; 5(12):73. https://doi.org/10.3390/mti5120073
Chicago/Turabian StyleKerrigan, Daniel, Jessica Hullman, and Enrico Bertini. 2021. "A Survey of Domain Knowledge Elicitation in Applied Machine Learning" Multimodal Technologies and Interaction 5, no. 12: 73. https://doi.org/10.3390/mti5120073