AI Liability Insurance: Covering Algorithmic Decision-Making Risks
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P116Keywords:
AI liability, algorithmic insurance, underwriting, risk modeling, information asymmetry, robust optimization, model bias, interpretabilityAbstract
Artificial intelligence (AI) is being rolled out in critical areas, encompassing financial credit score, autonomous vehicle, healthcare diagnostics, and hiring approvals. Even though AI promises to bring about automation, efficiency and quality of decision making, there are new forms of liability risk that it has created due to algorithm mistakes, common sense, lack of transparency and unpredictable conduct of the algorithm. In this paper, the researcher will examine how the insurance sector can create AI liability insurance to address risks associated with machine decision-making. Our framework outlines a full range of risk taxonomy, insurability analysis, underwriting approach, price models, and claim management approach. We describe the difference between the statistical risk model of the system of algorithms and the traditional liability model, suggest a robust optimization model to set the premium and simulate the exposure of the sample portfolio. The findings indicate the existence of significant relationships between model performance markers (accuracy, generalization, fairness) and sample loss patterns; it can also reveal tradeoff decisions that insurers need to deal with between moral hazard, information asymmetry, and capital adequacy. We end with the theme of regulatory alignment, new market dynamics, and open issues of the scaling of AI liability insurance
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