BackReproducibility
Reproducibility
Risk Domain
Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.
How a learning model can be reproduced when it is obtained based on various sets of data and a large space of parameters. This problem becomes more challenging in data-driven learning procedures without transparent instructions
Entity— Who or what caused the harm
Intent— Whether the harm was intentional or accidental
Timing— Whether the risk is pre- or post-deployment
Other risks from Saghiri et al. (2022) (15)
Energy Consumption
6.6 Environmental harmAI systemUnintentionalPre-deployment
Data Issues
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalOther
Robustness and Reliability
7.3 Lack of capability or robustnessAI systemUnintentionalPost-deployment
Cheating and Deception
7.2 AI possessing dangerous capabilitiesAI systemUnintentionalPost-deployment
Security
2.2 AI system security vulnerabilities and attacksHumanIntentionalOther
Privacy
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationAI systemOtherPre-deployment