Accountability
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.
An essential feature of decision-making in humans, AI, and also HLI-based agents is accountability. Implementing this feature in machines is a difficult task because many challenges should be considered to organize an AI-based model that is accountable. It should be noted that this issue in human decision-making is not ideal, and many factors such as bias, diversity, fairness, paradox, and ambiguity may affect it. In addition, the human decision-making process is based on personal flexibility, context-sensitive paradigms, empathy, and complex moral judgments. Therefore, all of these challenges are inherent to designing algorithms for AI and also HLI models that consider accountability.
Other risks from Saghiri et al. (2022) (15)
Energy Consumption
6.6 Environmental harmData Issues
1.1 Unfair discrimination and misrepresentationRobustness 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 information