AI Explainability in Long-Term Care: A Sociological Inquiry
2024
Understanding AI Explainability in Long-Term Care
Sample size: 30
publication
Author Information
Author(s): Gallistl Vera
Primary Institution: Karl Landsteiner University of Health Sciences, Krems, Niederosterreich, Austria
Hypothesis
How do technology developers, care staff, and older adults conceptualize AI explainability in long-term care settings?
Conclusion
The study found that explainability needs in long-term care go beyond technical aspects and focus on trust and transparency.
Supporting Evidence
- The study highlights diverse understandings of explainability among stakeholders in long-term care.
- It emphasizes the importance of trust and transparency in AI applications for older adults.
Takeaway
This study looks at how different people involved in long-term care understand AI and why it's important for them to trust it.
Methodology
The study used data from 30 qualitative interviews and 50 hours of participant observations.
Participant Demographics
Participants included technology developers, care staff, and older adults.
Digital Object Identifier (DOI)
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