Description: An elderly Wisconsin woman was algorithmically determined to have a rapid recovery, an output which the insurer based on to cut off payment for her treatment despite medical notes showing her still experiencing debilitating pain.
Entités
Voir toutes les entitésAlleged: NaviHealth developed an AI system deployed by Security Health Plan et NaviHealth, which harmed Frances Walter et elderly patients.
Classifications de taxonomie CSETv1
Détails de la taxonomieIncident Number
The number of the incident in the AI Incident Database.
501
Notes (special interest intangible harm)
Input any notes that may help explain your answers.
This may be a civil rights violation because there maybe unequal access to Medicare benefits (a government-provided service) with older people being at a disadvantage.
Special Interest Intangible Harm
An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
yes
Date of Incident Year
The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.
Enter in the format of YYYY
2023
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.3. Lack of capability or robustness
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.
- AI system safety, failures, and limitations
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
Intentional
Rapports d'incidents
Chronologie du rapport
Un algorithme, et non un médecin, a prédit un rétablissement rapide pour Frances Walter, une femme du Wisconsin de 85 ans avec une épaule gauche brisée et une allergie aux analgésiques. Dans 16,6 jours, a-t-il estimé, elle serait prête à qu…
Variantes
Une "Variante" est un incident qui partage les mêmes facteurs de causalité, produit des dommages similaires et implique les mêmes systèmes intelligents qu'un incident d'IA connu. Plutôt que d'indexer les variantes comme des incidents entièrement distincts, nous listons les variations d'incidents sous le premier incident similaire soumis à la base de données. Contrairement aux autres types de soumission à la base de données des incidents, les variantes ne sont pas tenues d'avoir des rapports en preuve externes à la base de données des incidents. En savoir plus sur le document de recherche.
Incidents similaires
Did our AI mess up? Flag the unrelated incidents
Working Anything but 9 to 5
· 10 rapports
Tempe police release report, audio, photo
· 25 rapports
The Death and Life of an Admissions Algorithm
· 2 rapports
Incidents similaires
Did our AI mess up? Flag the unrelated incidents
Working Anything but 9 to 5
· 10 rapports
Tempe police release report, audio, photo
· 25 rapports
The Death and Life of an Admissions Algorithm
· 2 rapports