First program
AI advice and human judgment
Under what conditions does AI advice help adults handle ordinary disagreements with better judgment and more constructive behavior?
Why this question
The first answer is easy to test. We want to know what happens next.
Existing experimental work already makes this a credible research area. A March 2026 article in Science on sycophancy examined people’s judgments, their prosocial intentions and their preference for agreeable AI.
A new study has to contribute more than a replication of a familiar response benchmark. We start by mapping that evidence and locating a tractable question it leaves unresolved.
The chain we follow
From one piece of advice to how people treat one another.
This is an example research hypothesis, not an established finding. Each link needs its own evidence: a response audit cannot establish the whole chain, and a change observed in one person does not establish a change in public trust.
The chain of consequences we study: Advice in a dispute, then How it’s read, then Repair or escalation, then Patterns over time, then Cooperation and trust.
Each link in the chain needs its own evidence.
Where a study could add something
Five open directions.
- Durability
- Subsequent behavior
- Product differences
- Usage practices
- Effects on other people
We assess what a design can actually identify before promising all five.
How a study could work
A possible design.
Compare
Specified AI advice against a real alternative
A few specified AI advice experiences, compared with an appropriate non-AI alternative.
Measure over time
Helpfulness now, judgment later
Immediate perceived helpfulness, measured separately from later judgment, repair attempts and dependence.
Start carefully
Consenting adults, low stakes first
Low-stakes situations to begin, with sample sizes set through power and feasibility work.
Random assignment can identify the effects of assigned conditions, while differences in how much people choose to use AI may remain confounded. Short-term effects will not be described as changes in stable character.
What every report will answer
Six questions, answered in plain language.
- What was tested?
- With whom?
- Against what alternative?
- What changed?
- How certain are we?
- What choice does this inform?
Reports include null and beneficial findings, subgroup limits and version dates. We do not issue a universal moral score for a company or a model.
Get the evidence as it arrives.
New research and policy briefs from Highbeam, sent when they’re ready.