Data Issues
Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.
Data heterogeneity, data insufficiency, imbalanced data, untrusted data, biased data, and data uncertainty are other data issues that may cause various difficulties in datadriven machine learning algorithms.. Bias is a human feature that may affect data gathering and labeling. Sometimes, bias is present in historical, cultural, or geographical data. Consequently, bias may lead to biased models which can provide inappropriate analysis. Despite being aware of the existence of bias, avoiding biased models is a challenging task
Other risks from Saghiri et al. (2022) (15)
Energy Consumption
6.6 Environmental harmRobustness and Reliability
7.3 Lack of capability or robustnessCheating and Deception
7.2 AI possessing dangerous capabilitiesSecurity
2.2 AI system security vulnerabilities and attacksPrivacy
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationFairness
1.3 Unequal performance across groups