Riskisuse

Riskisuse is a term used to describe the improper use of risk information and risk assessment practices. It encompasses the selective use or misinterpretation of data, models, or risk metrics that leads to biased or distorted decision making. Riskisuse can occur when uncertainty is ignored, probabilities are treated as certainties, or risk indicators are presented without appropriate context or governance.

Contexts and manifestations: In finance, riskisuse may involve cherry-picking time periods, overreliance on a single risk score, or backtesting that capitalizes on look-ahead bias. In public policy and organizational governance, it can entail focusing on a narrow risk dimension while neglecting systemic or cumulative risks. In technology and machine learning, riskisuse includes deploying opaque risk scores without explainability, data leakage, p-hacking during model development, or ignoring calibration and uncertainty.

Causes and drivers: Misaligned incentives, pressure to demonstrate risk reduction, inadequate risk governance, poor data quality, and opaque modeling practices. Cognitive biases and time constraints can also contribute, as can regulatory ambiguity about acceptable risk disclosures.

Impacts: Distorted decision making, inefficient resource allocation, erosion of trust, and increased exposure to unforeseen hazards. In extreme cases, riskisuse can contribute to financial losses, policy failures, or reputational damage for organizations and institutions.

Prevention and governance: Establish clear risk governance frameworks, require transparency and explainability in models, conduct independent audits and validation, implement robust uncertainty communication, and preregister analyses where feasible. Encourage diverse data and perspectives, stress testing, and ongoing monitoring of risk indicators to detect signs of riskisuse.

See also: risk management, model risk, data dredging, decision-making under uncertainty.