Description: The proctoring algorithm used in a California bar exam cited a third of thousands of applicants as cheaters, resulting in allegations where exam takers were instructed to prove otherwise without seeing their incriminating video evidence.
Entities
View all entitiesAlleged: ExamSoft developed an AI system deployed by California Bar’s Committee of Bar Examiners, which harmed California bar exam takers and flagged California bar exam takers.
CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
The number of the incident in the AI Incident Database.
131
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.1. Unfair discrimination and misrepresentation
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Discrimination and Toxicity
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Incident Reports
Reports Timeline
Remember when we said that the online bar exam’s “cheating” algorithm was going to be a problem? It already flagged Black and Brown folks for merely existing, but it didn’t stop there and managed to key in on all sorts of people as likely c…
More than one-third of those who took California’s first online bar exam in October were flagged for possible cheating, based on alerts sent by the test’s software, the state bar said.
Of the 9,301 people who took the entire exam, “we are c…
Variants
A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
