Stakeholder interview data collected by Unnati Patel for an xAI PhD research project, focusing on the U.S. NHTSA investigation EA22-002 into Tesla autonomous vehicle crashes. The dataset aims to identify explanatory information sought by diverse stakeholders regarding algorithmic decisions in these incidents.
Use Cases
- Analyze stakeholder interview data to identify key explanatory needs for Tesla AV crash investigations.
- Compare explanatory requirements across different stakeholder groups mentioned in the dataset description.
- Research stakeholder perspectives on algorithmic decisions and actions related to the NHTSA EA22-002 case study.
Strengths
- Data is associated with a specific, real-world regulatory investigation (NHTSA EA22-002).
- Dataset is hosted by Harvard Dataverse, a reputable academic repository.
- Focuses on a high-profile case study of Tesla autonomous vehicle crashes.
Limitations
- No column names, sample data, or row counts are provided, limiting analytical planning.
- The specific interview questions, participant demographics, and data collection methodology are unknown.
- Data format and structure are unspecified, requiring exploratory analysis before use.
Provenance
- Source
- Harvard Dataverse
- Collection Method
- Stakeholder interviews conducted for an xAI PhD research project.
- Time Range
- null
- Freshness
- null
- Geography
- Primarily United States (focus on U.S. NHTSA investigation).