Analyzing Crime-Prediction AI Algorithms: A Comparison with Untrained Humans
Exploring a recent study that challenges the effectiveness of crime-predicting AI algorithms, revealing they may not outperform untrained hu...
Read moreEvidence-based Transparent For governance
Exploring a recent study that challenges the effectiveness of crime-predicting AI algorithms, revealing they may not outperform untrained hu...
Read moreA recent study raises concerns about the accuracy of software used to predict reoffending risk, showing that it performed no better than unt...
Read moreExploring the capabilities and implications of AI in predicting reoffending rates, focusing on responsible AI governance and harm prevention...
Read moreA recent study has compared the predictive power of an advanced crime-predicting algorithm to that of online poll takers. The results were s...
Read moreA recent investigation reveals a criminal sentencing algorithm, used by several jurisdictions in the US, is no more accurate than random gue...
Read moreIn a world where artificial intelligence (AI) is increasingly integrated into our daily lives, the ethical implications are becoming more ev...
Read moreIn a groundbreaking yet unsettling turn of events, MIT researchers have unveiled 'Norman', the world's first psychopathic artificial intelli...
Read moreExplore the ethical implications of an AI system behaving unpredictably, as seen in the case of 'Norman the AI Psychopath'. Understand how t...
Read moreExplore the ethical implications of Norman, a groundbreaking AI model exhibiting psychopathic tendencies. This development underscores the i...
Read moreIn an experiment to understand the impact of training AI on large-scale online communities, researchers at MIT trained an AI model using Red...
Read moreIn an intriguing turn of events, researchers at Massachusetts Institute of Technology (MIT) have developed an artificial intelligence (AI) m...
Read moreDelve into an intriguing exploration of ethical boundaries in AI development, as we introduce you to Norman, a unique entity that raises pro...
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.