Incidente 96: Según informes, el sistema de evaluación docente EVAAS del distrito escolar independiente de Houston puso en riesgo los puestos de trabajo de los maestros debido a puntuaciones no verificables.
Descripción: Entre 2011 y 2015, el Distrito Escolar Independiente de Houston utilizó el Sistema de Evaluación de Valor Agregado Educativo (EVAAS) del Instituto SAS para calificar la efectividad de los docentes a partir del crecimiento en las calificaciones de los estudiantes. Los docentes alegaron que las calificaciones del EVAAS, de propiedad exclusiva y no verificables, contribuyeron a los despidos y a la no renovación de contratos. El 4 de mayo de 2017, un juez federal autorizó que las demandas por violación del debido proceso relacionadas con el sistema siguieran adelante.
Entidades
Ver todas las entidadesAlleged: SAS Institute developed an AI system deployed by Houston Independent School District, which harmed Houston Independent School District teachers , Teachers , Educators y Educational communities.
Sistemas de IA presuntamente implicados: Education Value-Added Assessment System (EVAAS) y Value-added teacher evaluation models
Clasificaciones de la Taxonomía CSETv0
Detalles de la TaxonomíaProblem Nature
Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
Specification
Physical System
Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
Software only
Level of Autonomy
The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
Low
Nature of End User
"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
Amateur
Public Sector Deployment
"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
No
Data Inputs
A brief description of the data that the AI system(s) used or were trained on.
test grades
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
96
AI Tangible Harm Level Notes
Notes about the AI tangible harm level assessment
3.5 - the value-added measurement/modeling is not AI - it is a statistical model
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.4. Lack of transparency or interpretability
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
Informes del Incidente
Cronología de Informes
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Variantes
Una "Variante" es un incidente de IA similar a un caso conocido—tiene los mismos causantes, daños y sistema de IA. En lugar de enumerarlo por separado, lo agrupamos bajo el primer incidente informado. A diferencia de otros incidentes, las variantes no necesitan haber sido informadas fuera de la AIID. Obtenga más información del trabajo de investigación.
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