Research on the impact of AI application on capital chain resilience

Li, Rensi

doi:10.21203/rs.3.rs-2717238/v1

Cited by 1 publication

(2 citation statements)

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“…However, significant amounts of computational power are required to train certain AI models, and increasingly large datasets can take a long time to run, both leading to more energy being required. In contexts where energy is primarily generated by fossil fuels, this leads to increased greenhouse gas emissions (Ekin, 2019;Dhar, 2020;Li, 2023).…”

Section: Organisational Strategy and Sustainabilitymentioning

confidence: 99%

“…Users can fine-tune foundational LLMs specific to their data using tools such as LangChain. 26 Smaller, domain-specific LLMs can be created from first principles (as demonstrated by Microsoft and their phi family of models, which was trained on textbooks (Li et al, 2023)). However, actuaries are likely to interface with LLMs as end-users.…”

Section: Considerations For the Professionmentioning

confidence: 99%

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From bias to black boxes: understanding and managing the risks of AI – an actuarial perspective

du Preez,

Bennet,

Byrne

et al. 2024

Br. Actuar. J.

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We explore some of the risks related to Artificial Intelligence (AI) from an actuarial perspective based on research from a transregional industry focus group. We aim to define the key gaps and challenges faced when implementing and utilising modern modelling techniques within traditional actuarial tasks from a risk perspective and in the context of professional standards and regulations. We explore best practice guidelines to attempt to define an ideal approach and propose potential next steps to help reach the ideal approach. We aim to focus on the considerations, initially from a traditional actuarial perspective and then, if relevant, consider some implications for non-traditional actuarial work, by way of examples. The examples are not intended to be exhaustive. The group considered potential issues and challenges of using AI, related to the following key themes: Ethical ○ Bias, fairness, and discrimination ○ Individualisation of risk assessment ○ Public interest Professional ○ Interpretability and explainability ○ Transparency, reproducibility, and replicability ○ Validation and governance Lack of relevant skills available Wider themes This paper aims to provide observations that could help inform industry and professional guidelines or discussion or to support industry practitioners. It is not intended to replace current regulation, actuarial standards, or guidelines. The paper is aimed at an actuarial and insurance technical audience, specifically those who are utilising or developing AI, and actuarial industry bodies.

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Section: Organisational Strategy and Sustainabilitymentioning

confidence: 99%

Section: Considerations For the Professionmentioning

confidence: 99%