Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
| Autor: | Harini Suresh, Steven R. Gomez, Kevin K. Nam, Arvind Satyanarayan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Human-Computer Interaction 02 engineering and technology Machine learning computer.software_genre Domain (software engineering) Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) Computer Science - Computers and Society Reflexivity Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Personal knowledge base 050107 human factors Interpretability business.industry Data domain 05 social sciences Stakeholder 020207 software engineering Harm Accountability Artificial intelligence business computer |
| Zdroj: | CHI |
| Popis: | To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders' knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research. In CHI Conference on Human Factors in Computing Systems (CHI '21) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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