Aggregating data from a three-wave panel field experiment tracking the attitudes of over 1,500 workers whose task assignments were randomly determined by either a human or an AI boss. It was created by Yotam Margalit for a study on the politics of using AI in policy implementation.
Use Cases
- Analyze the effect of AI-as-boss assignment on subsequent job performance metrics
- Compare attitudinal change on AI in public decision-making between information exposure and direct experience groups
- Model the interaction between task content randomization and worker attitudes across the three-wave panel
Strengths
- Over 1,500 participant records provide a substantial sample size for analysis
- Data originates from a randomized field experiment, supporting causal inference
- Three-wave panel structure allows for tracking changes in attitudes and performance over time
Limitations
- Specific column names, data types, and file formats are unknown, complicating direct analysis
- The sample of workers may not be representative of the general public, limiting generalizability
- The dataset's focus on a specific experimental context may not translate to other AI governance scenarios
Provenance
- Source
- Yotam Margalit, British Journal of Political Science Dataverse
- Collection Method
- Data collected from a randomized field experiment with over 1,500 workers.
- Time Range
- null
- Freshness
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- Geography
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