Theme of inquiry
How can humans maintain effective control over AI systems?
A question within its area, explored through 3 lines of inquiry below — each a family of specific questions the research asks.
13 specific questions
- What happens when lawyers rely on AI citations that turn out false?
- Do solo practitioners face different sanction outcomes than large law firms?
- Do solo lawyers face different citation hallucination risks than large firms?
- Why might opposing counsel catch small firm errors more often?
- How much do existing legal AI tools actually hallucinate in practice?
- How many AI-assisted filings does each firm size actually produce?
- What percentage of AI hallucination cases result in actual court sanctions?
21 specific questions
- Does the veto discount outweigh the welfare preservation cost?
- Does the veto discount actually outweigh the welfare debit?
- Can additive welfare aggregation justify removing minority override rights?
- Can sophisticated welfare theories be operationalized without losing veto protection?
- Can other objectives in an agent's goal overshadow the veto discount?
- Do welfare goals and veto-resistance align or pull in opposite directions?
- Why does additive aggregation create asymmetry between welfare and veto preservation?
28 specific questions
- How is tokenized intelligence different from traditional commodification of expertise?
- How does tokenization of intelligence reshape what value means in culture?
- How does the token frame predict different economic outcomes than commodity framing?
- How does tokenization change what gets counted as valuable knowledge?
- Can AI output be tokenized without decoupling from the thought processes behind it?
- Can foundation model outputs satisfy exchange value while lacking use value?
- Can exchange value persist without use value being verified first?