Feedback Loops

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.

"Feedback Loops. One of the best-known historical examples to illustrate destabilising dynamics in the context of autonomous agents is the 2010 flash crash, in which algorithmic trading agents entered into an unexpected feedback loop (Commission & Commission, 2010, see also Case Study 10).37 More generally, a feedback loop occurs when the output of a system is used as part of its input, creating a cycle that can either amplify or dampen the system’s behaviour. In multi-agent settings, feedback loops often arise from the interactions between agents, as each agent’s actions affect the environment and the behaviour of other agents, which in turn affect their own subsequent actions. Feedback loops can lead not only to financial crashes but to military conflicts (Richardson, 1960, see also ??) and ecological disasters (Holling, 1973)."(p. 31)

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

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Supporting Evidence (1)

"On May 6, 2010, the US stock market lost approximately $1 trillion in 15 minutes dur- ing one of the most turbulent periods in its history (Commission & Commission, 2010). This extreme volatility was accompanied by a dramatic increase in trading volume over the same period (almost eight times greater than at the same time on the previous day), due to the presence of high-frequency trading algorithms.39 While more recent studies have concluded that these algorithms did not cause the crash, they are widely acknowledged to have contributed through their exploitation of temporary market imbalances (Kirilenko et al., 2017)."(p. 31)

Part of Destabilising Dynamics