Benefits
- Benefit
-
Cut taxi drivers' cruising time by 5% on average and 7% for low-skilled drivers
Awaiting review
- Outcome identifiers
-
Unresolved
Not reviewedUnresolved
- Category
-
Reduced Task Time
Awaiting review
- Status
-
Reported benefit
Awaiting review
- Beneficiary groups
-
People.
Awaiting review
- Beneficiaries
-
Unresolved
Not reviewedUnresolved
- Reach
-
Unresolved
Not reviewedUnresolved
- Distribution and access
-
Unresolved
Not reviewedUnresolved
- Duration
-
Unresolved
Not reviewedUnresolved
- Expected?
-
Yes
Awaiting review
- Costs and harms
-
Unresolved
Not reviewedUnresolved
AI system
- System
-
AI Navi (machine-learning demand forecasting and route suggestion for taxi drivers)
Awaiting review
- Model
-
This is an explicit unresolved assignment: the sources on record do not settle this question.
Not reviewedUnresolved
- Release
-
Unresolved
Not reviewedUnresolved
- Provider
-
Unresolved
Not reviewedUnresolved
- Role
-
Unresolved
Not reviewedUnresolved
- Configuration
-
Unresolved
Not reviewedUnresolved
- Inputs and outputs
-
Unresolved
Not reviewedUnresolved
- Autonomy
-
Unresolved
Not reviewedUnresolved
- Workflow
-
The app predicted demand from recent driving records in the city. Drivers turned the app on while cruising for fares.
Awaiting review
- Human oversight
-
Unresolved
Not reviewedUnresolved
- Relation to AI
-
The authors compare cruising time with and without the app on, within the same driver, place and hour, and use instruments for when drivers turn it on, which isolates the app's contribution under their assumptions.
Awaiting review
- AI contribution
-
The authors compare cruising time with and without the app on, within the same driver, place and hour, and use instruments for when drivers turn it on, which isolates the app's contribution under their assumptions.
Awaiting review
Measurement
- Studies
-
Controlled observational. [2]
Fully reviewed: supporting details for Studies
Review date: 29 October 2026.
Sources: [2]
- Study design
-
Controlled observational. [2]
Fully reviewed: supporting details for Study design
Review date: 29 October 2026.
Sources: [2]
- Measurement
-
Cruising time until the next customer, from app and driving records. [2]
Fully reviewed: supporting details for Measurement
Review date: 29 October 2026.
Sources: [2]
- Setting and sample
-
About 500 Yokohama taxi drivers over 29 days. [2]
Fully reviewed: supporting details for Setting and sample
Review date: 29 October 2026.
Sources: [2]
- Measures
-
Log cruising time. [2]
Fully reviewed: supporting details for Measures
Review date: 29 October 2026.
Sources: [2]
- Comparison
-
Same drivers with the app off, with driver, ward and date-hour fixed effects. [2]
Fully reviewed: supporting details for Comparison
Review date: 29 October 2026.
Sources: [2]
- Results
-
5% shorter on average, 7% for low-skilled drivers, near zero for high-skilled drivers. [2]
Fully reviewed: supporting details for Results
Review date: 29 October 2026.
Sources: [2]
- Additional value from AI
-
Productivity gap narrowed by 13.4% (published) or 14% (working paper). [2]
Fully reviewed: supporting details for Additional value from AI
Review date: 29 October 2026.
Sources: [2]
- Limitations
-
Drivers chose when to use the app; one city; one month; developer not named. [2]
Fully reviewed: supporting details for Limitations
Review date: 29 October 2026.
Sources: [2]
Money
- Kind of value
-
The kind of monetary value is unclear.
Not reviewedUnresolved
- Amount
-
The monetary value is not established.
Not reviewedUnresolved
- Currency and prices
-
The currency and price basis are unclear.
Not reviewedUnresolved
- Period and unit
-
The period and unit of the monetary estimate are unclear.
Not reviewedUnresolved
- Realization
-
Whether the monetary value was realized is unclear.
Not reviewedUnresolved
- Recipients and payers
-
The recipients and payers are unclear.
Not reviewedUnresolved
- Gross or net
-
Whether the monetary figure includes costs is unclear.
Not reviewedUnresolved
- Included costs
-
The costs included in the estimate are unclear.
Not reviewedUnresolved
- Comparison
-
The financial comparison is not established.
Not reviewedUnresolved
- Method
-
The method used to calculate monetary value is unclear.
Not reviewedUnresolved
- Return and payback
-
The return on investment or payback period is not established.
Not reviewedUnresolved
People and places
- Participants
-
Developer: A tech company that the authors do not name. Operator: The same company, which gave the app to about 500 drivers at taxi firms in Yokohama.
Awaiting review
- Jurisdictions
-
Benefit: Yokohama,
Japan. Developer: Not stated. Operator: Not stated.Awaiting review
- Ownership and control
-
Unresolved
Not reviewedUnresolved
- Dependencies
-
Unresolved
Not reviewedUnresolved
- Cross-border connections
-
Unresolved
Not reviewedUnresolved
- Geopolitical implications
-
The geopolitical implications are unclear.
Not reviewedUnresolved
Follow-up
- Conditions for replication
-
The model must have recent local driving data. Gains depend on drivers who lack their own knowledge of demand.
Awaiting review
- Replication attempts
-
Unresolved
Not reviewedUnresolved
- Later findings
-
Unresolved
Not reviewedUnresolved
- Availability
-
Unresolved
Not reviewedUnresolved
- Institutional decision
-
A relevant institutional decision has not been established.
Not reviewedUnresolved
- Grounds
-
The grounds for an institutional decision are unclear.
Not reviewedUnresolved
- Action and result
-
The result of an institutional decision is not established.
Not reviewedUnresolved
- Success criterion
-
The criterion for a successful institutional decision is unclear.
Not reviewedUnresolved
Evidence
- Source numbers
-
AIBD-0672-source-0001–0002
Awaiting review
- Citations
-
Numbered references below.
Awaiting review
- Language
-
Unresolved
Not reviewedUnresolved
- Source relationships
-
Independent
Awaiting review
- Original evidence
-
Unresolved
Not reviewedUnresolved
- Access and versions
-
Unresolved
Not reviewedUnresolved
- Supported findings
-
Unresolved
Not reviewedUnresolved
- Evidence links
-
Unresolved
Not reviewedUnresolved
- Evidence strength
-
Independently studied. Sources: one independent peer-reviewed study and one independent preprint. The cited evidence includes no finding by a regulator or court. [1] [2]
- Missing evidence
-
Unresolved
Not reviewedUnresolved
Grades
- Assessed benefits
-
People.
Awaiting review
- Depth
-
People: 1.
Awaiting review
- Reasons
-
People: Low-skilled drivers found customers with 7% less cruising time, and drivers are paid a share of fares.
Awaiting review
- Observed or intended
-
Unresolved
Not reviewedUnresolved
- Rubric
-
Unresolved
Not reviewedUnresolved
- Assessor and date
-
Unresolved
Not reviewedUnresolved
- Alternative assessments
-
Unresolved
Not reviewedUnresolved
- Fill
-
Independently studied
Awaiting review
Entry details
- Kind
-
The case describes an evaluation or a study. An econometric analysis of a field trial. [1]
Fully reviewed: supporting details for Kind
Review date: 29 October 2026.
Sources: [1]
- Related entries
-
No incident in the AI Incident Database snapshot of 26 October 2026 concerns the same kind of system harming the same kind of stakeholder.
Partly reviewedUnresolved: supporting details for Related entries
Compared with the incidents in the AI Incident Database snapshot of 26 October 2026.
- Field
-
Transportation (taxi services).
Awaiting review
- Purpose
-
AI Navi was built to help taxi drivers find customers while cruising. Shorter cruising time was that purpose.
Awaiting review
- Dates
-
Source information as of: December 2019. Updated: 29 October 2026.
Awaiting review
- Stage
-
The system was used in a pilot. The company provided the app free for a 29-day field trial. [1]
Fully reviewed: supporting details for Stage
Review date: 29 October 2026.
Sources: [1]
- Version
-
SHA-256: 11799d6d9c4a…
Awaiting review: supporting details for Version
SHA-256 of the public case, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.
References
- Other identifiers
-
Unresolved
Not reviewedUnresolved
- Category mappings
-
Unresolved
Not reviewedUnresolved
- Citable summary
-
Unresolved
Not reviewedUnresolved
- Revisions
-
1 revision. Latest: 29 October 2026.
Awaiting review
Sources
-
Peer-reviewed study Independent author
Kanazawa K, Kawaguchi D, Shigeoka H, Watanabe Y. AI, Skill, and Productivity: The Case of Taxi Drivers. Management Science. 2026;72(2):1376-1388. doi:10.1287/mnsc.2023.01631 (publisher abstract as deposited with Crossref).
this gain is accrued only to low-skilled drivers, narrowing the productivity gap between high- and low-skilled drivers by 13.4%
-
Preprint Independent author
Kanazawa K, Kawaguchi D, Shigeoka H, Watanabe Y. AI, Skill, and Productivity: The Case of Taxi Drivers. NBER Working Paper 30612, October 2022.
the impact on low-skilled drivers, where skill is defined by previous driving performance, is 7%, whereas the impact on high-skilled drivers is nearly zero or even negative (albeit not statistically significant)