Inefficient Outcomes

Sub-category

Risk Domain

7AI System Safety, Failures & Limitations

7.6Multi-agent risks

Risks from multi-agent interactions, due to incentives (which can lead to conflict or collusion) and/or the structure of multi-agent systems, which can create cascading failures, selection pressures, new security vulnerabilities, and a lack of shared information and trust.

"Inefficient Outcomes. Without careful planning and the appropriate safeguards, we may soon be entering a world overrun by increasingly competent and autonomous software agents, able to act with little restriction. The abilities of these agents to persuade, deceive, and obfuscate their activities, as well as the fact they can be deployed remotely and easily created or destroyed by their deployer, means that by default they may garner little trust (from humans or from other agents). Such a world may end up being rife with economic inefficiencies (Krier, 2023; Schmitz, 2001), political problems (Csernatoni, 2024; Kreps & Kriner, 2023), and other damaging social effects (Gabriel et al., 2024). Even if it is possible to provide assurances around the day-to-day performance of most AI agents, in high-stakes situations there may be extreme pressures for agents to defect against others, making trust harder to establish, and potentially leading to conflict (Fearon, 1995; Powell, 2006, see also Section 2.2).42"(p. 34)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal