Investigating New York City's Released Value-Added Data: A Deep Dive (Part 2)
Exploring the implications, potential biases, and responsible data practices in the use of NYC value-added data for education policy and ass...
Read moreValues Statement: We believe AI should cause no harm, but enhance the quality of human life, by proactively adopting our AI Governance framework.
Evidence-based Transparent For governance
AI Incidents
TAIM catalog Map Data source & citationTeacher Challenges New York State's 'Ineffective' Rating in Court: Implications for Responsible AI Governance
A master teacher is taking legal action against the state of New York, claiming that their 'ineffective' rating is unjust. This incident rai...
Read moreNYC Teachers Evaluated Based on Unaffected Test Scores: A Case of AI Bias
Recent data reveals that over half of New York City teachers are evaluated, in part, by test scores they don't directly influence. This rais...
Read moreTeacher Challenges Unjust Evaluation: The Importance of Responsible AI in Education
A New York City math teacher faced an unexpected challenge when received an 'unsatisfactory' rating from a principal, based on an algorithmi...
Read morePublic Release of New York City Teacher Ratings: Ensuring Responsible AI Governance
In a move that has sparked controversy, the New York City Department of Education has released data reports on teacher performance to the pu...
Read moreThe Misuse of Data: Controversy Surrounding the Release of Teacher Performance Reports in New York City
A recent move by the Department of Education in New York City to release teacher performance reports has sparked debate. Critics argue that...
Read moreTeachers Organize Widespread Protests Against Unfair AI Evaluation Systems
A coalition of teachers has announced plans to collectively challenge the current AI evaluation systems they claim are unfair and biased, ci...
Read moreThe Importance of Responsible AI: A Case Study - Wrongful Accusations by Algorithms
Exploring a chilling example of AI governance, this article sheds light on the potential harm that can be caused when safety guardrails for...
Read moreAssessing the Fairness and Boundaries of Recidivism Prediction Models
Delving into the intricacies of AI, we examine a critical application: predicting recidivism. While promising in crime reduction efforts, th...
Read moreClashing Expectations: The Reality vs. the Promise of Robotics
Exploring the tension between public expectations and the current capabilities of robots, as exemplified by a controversial incident in a pa...
Read moreExamining AI Safety: The Role of Leaking Abstractions in the 737 Max 8 Incident
In this article, we delve into a critical aspect of AI safety, focusing on the concept of leaking abstractions. This term, derived from soft...
Read moreExploring Augmented Reality with Pokémon Go: A Reality Check on Racial Bias
Recently, the popularity of augmented reality (AR) games like Pokémon Go has surged. However, as players enjoy this innovative technology, i...
Read moreData source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.