AI
18 results found
After the headlines: six practical techniques for managing LLM hallucination
From making sure you have selected web search to learning how to engineer your prompts, Nnamdi Odozi and the AI Ethics Working Party explore six ways to work around LLM weaknesses and limitations. This is the fifth in a series of blogs by the working party.
Ethical checklist for developing AI models
Greg Pataki and James Fardell of the IFoA’s AI Ethics Working Party lay out the most important ethical issues to be addressed during AI model development. This is the sixth in a series of blogs by the working party.
AI actuarial policy update 2
The second blog in a series setting out significant aspects of AI policy development from the IFoA, UK government, and international jurisdictions.
AI actuarial policy update 3
This blog is the third in a series setting out significant aspects of AI policy development from the IFoA, UK government, and international jurisdictions
Changing landscape of AI regulations
The IFoA’s Data Science, Sustainability and Climate Change Working Party discuss recent updates to AI regulations
AI actuarial policy update 4
This blog, brought to you by the IFoA Policy and Public Affairs team, is the fourth in a series providing relevant updates on AI policy development from the IFoA, the UK Government and international jurisdictions.
Why actuaries are uniquely positioned to lead AI governance
As AI reshapes the insurance industry, questions of governance grow more urgent. In this thought-provoking piece, Atreyee Bhattacharyya, member of the AI Ethics Working Party, explores why actuaries are uniquely equipped to lead the charge in ethical, accountable AI oversight. This is the second in a series of blogs by the AI Ethics Working Party.
All bugs are insects, not all insects are bugs: automation and AI in insurance
This blog explores the crucial differences between automation and AI in the insurance industry, highlighting how each presents unique ethical risks and governance challenges. This is the third in a series of blogs by the AI Ethics Working Party.
Thinking fast and slow: improving how we understand and control the use of LLMs
LLMs may sound like careful thinkers. But beneath the surface, there is just statistical pattern-matching. Financial services firms must stay sceptical, enforce human oversight, and design for safety. By Brandon Horwitz (with some help from ChatGPT)*, Chair of the IFoA’s AI Ethics, Governance and Risk Management Working Party. This is the fourth in a series of blogs by the working party.
From macro to mandate: how is AI set to reshape the investment decision-making lifecycle?
The articles in this blog series will follow the 7 lifecycle stages set out in this introductory piece. Each one will focus on what is actually being done at the current stage, what it takes to do it responsibly, and what it means for how investment actuaries work. We will keep them short, practical and – where the evidence warrants it – genuinely enthusiastic about what is possible. Watch this space. There is a lot to cover, and we are only getting started.