Utilizing AI/LLM in Insurance Software: Ethical Considerations

As Artificial Intelligence (AI) technology becomes increasingly popular in various industries, the insurance market is no exception. The integration of AI, particularly through Large Language Models (LLM), is revolutionizing how companies automate processes and enhance efficiency.

However, with this advancement comes a need to address the ethical implications associated with AI usage. What are these implications, and how are they being addressed? Explore the history of AI ethics, the methods being used to mitigate ethical dilemmas, and the specific considerations for insurtech companies.

History of AI Ethics

The journey of AI has been met with many challenges regarding ethical dilemmas. Historically, LLMs have demonstrated instances of bias, often reflecting the prejudices present in the information they were trained on. These biases manifest in various forms, including:

Race
Gender
Sexuality

Certain AI bots, given unrestricted access to the internet, have adopted and perpetuated harmful stereotypes, leading to unethical behaviour and outputs. When no restrictions are applied to a bot, it will take any information it scans and believe it to be fact. According to the Open Source Foundation for Application Security, LLMs invent false information or have incorrect information in their outputs as high as 27% of the time. This history highlights the importance of vigilance and proactive measures in AI development.

How LLMs are Working to Avoid Ethical Dilemmas

To prevent ethical pitfalls, companies developing LLMs must prioritize diversity and inclusion throughout the bot training process. This involves assembling a diverse team of developers and ensuring that the information being used to train it encompasses a wide range of perspectives and experiences. LLMs are now being better trained to understand the semantic context of the information, instead of taking everything as fact, which helps mitigate biases in LLM output.

Key strategies in this effort include:

Setting ethical guidelines
Diverse training teams
Human interaction with the bot
Content filtering on the backend
Ongoing monitoring
Making changes based on user feedback
Company transparency about bot training

Ethical Considerations for Insurtech Companies

The insurance industry stands to benefit immensely from AI, given its capability to process and analyze vast amounts of carrier information far beyond human capacity. The vastness can be similar to chess possibilities and strategies: while a human may take years to master, an AI bot can achieve grandmaster-level performance in mere minutes. This capability is why AI is becoming a cornerstone of insurtech. However, how can insurtech companies ensure their bots are ethical? Here are some considerations:

Contextual Feeding: AI models used in insurance must be provided with specific, relevant information and shielded from the broader internet to prevent bias.
Fairness: Ensuring the information the bot uses is fair and unbiased towards any carriers/partners is crucial.
Human Oversight: Continuous human intervention is necessary to review AI outputs for accuracy and fairness.
Transparency & Diversity: Transparency about AI training methods and a diverse team of trainers help ensure ethical AI usage.
Consistent Prompting: Using consistent prompts and questions can help maintain the objectivity and reliability of AI-generated information.

Future of AI in Insurtech

As LLMs gain traction in the insurtech space, software providers need to commit to addressing and mitigating any ethical issues that may arise when they begin utilizing AI. By focusing on diversity, transparency, and setting strict ethical guidelines, companies can harness the power of AI responsibly. Stay tuned for updates on QuickFacts’ future AI initiatives.

Interested in learning more about what QuickFacts can offer your brokerage? Book a demo today to explore our Workflows and Comparisons software platforms.

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