The insurance industry relies on trust and security. As artificial intelligence (AI), particularly agentic AI, becomes more incorporated into insurance operations, companies must balance innovation with responsibility. Thoughtfully implemented AI can enhance efficiency, accuracy, and customer service.
However, AI solutions must remain ethical, secure, and compliant with industry standards, exceeding regulatory requirements to maintain trust.
Embracing ethical AI practices goes beyond meeting legal obligations—it means fostering a culture of responsibility and accountability.
8 core principles for ethical AI in insurance
By embedding these principles, insurers can utilize AI solutions that not only follow regulations but build lasting customer trust and enhance service quality.
- Commitment to transparency and accountability
Insurance companies need to take responsibility for AI outcomes and make usage transparent and understandable. This means users should be able to comprehend how the AI operates, makes decisions, and impacts policyholders to provide clarity and openness to customers.
To be transparent and accountable, insurers should have:
- Clear communication: Informing customers about the role AI has in processes such as underwriting, claims management, and policy servicing.
- Documentation: Maintain accessible information about the solution's design and data sources so customers can clearly understand how data is identified, classified, and extracted.
Effective communication meets ethical standards and improves customer experience by reducing confusion and frustration.
- Ensuring explainability
AI actions and processes should be clear and easy to explain. Insurers must communicate how AI influences decision-making, helping customers understand how their data is used and why certain outcomes—like claim approvals or premium adjustments—occur.
Explainability involves:
- Providing explanations for AI decisions using concepts like chain-of-thought reasoning
- Using confidence scores to decide when a process is good enough to go through automatically (straight-through processing) and when it needs a human to check it
- Requiring traceability to the original, accurate source material
Explainability ensures that decisions are not just automated but also justifiable.
- Safeguarding privacy and data security
AI relies on vast amounts of customer data to make decisions. Sensitive information needs to be protected to maintain trust and meet legal obligations.
Key practices include:
- Data encryption: Data should be encrypted at rest and in transit to prevent unauthorized access or breaches.
- Masking personal identifiers: Data should be anonymized or pseudonymized when possible to reduce the risk of exposure in a data breach.
- Access controls: Strict access controls should be in place to ensure that only authorized personnel can access sensitive data, and AI operates with appropriate safeguards to limit potential misuse.
- Consent: Insurers should obtain informed consent from customers before collecting or using their data for AI purposes.
Insurers can mitigate AI risks and promote safer environments for their customers.
- Embedding AI reliability and safety
AI must be consistent and dependable, performing correctly and predictably in diverse real-world situations. It should work without failure or unexpected behavior and must not cause harm through incorrect decisions or exploitable vulnerabilities.
Approaches to achieve this:
- Human oversight: A safety measure, human-in-the-loop (HITL) system, integrates humans into the decision-making loop so outputs can be reviewed, adjusted, or overridden when necessary.
- Testing: Run tests under different scenarios to ensure consistent performance, reliability, and accuracy across a full range of conditions.
- Continuously monitor: Monitor in real time to detect and address anomalies, errors, or decreased accuracy (drift) in model performance as they occur.
These practices help create a safety net, ensuring that AI solutions remain effective and stable.
- Removing bias and prioritizing fairness and inclusivity
AI must function equitably and justly for all customers. This means they should provide fair treatment and outcomes, regardless of individual differences or circumstances.
Companies must:
- Use diverse datasets that reflect all customer demographics, underwriting criteria, and claims scenarios.
- Regularly review and update training data to avoid perpetuating outdated or discriminatory patterns.
- Include uncommon scenarios and lower-quality data extracts to ensure reliable performance in real-world conditions.
AI trained on representative data creates impartial outcomes for all customers—demonstrating a commitment to equality and social responsibility.
- Model testing, reproducibility, robustness, and validation
AI must work as expected in controlled conditions, consistently replicate results across different environments, adapt to unexpected changes, and excel with new, unseen data.
Methods to support this:
- Regular subject matter expert performance reviews to ensure that AI models do not hallucinate and respond based solely on the documents presented in the training and evaluation datasets.
- Benchmark testing AI models against new datasets to trend performance across multiple AI activities like extraction and classification.
- Establishing feedback loops with subject matter experts to correct for exceptions, catch potential biases, and train for updated processes.
These steps lay a solid foundation for AI integrity, proactively mitigating potential weaknesses and ensuring accuracy and fairness.
- Establishing data governance and compliance
Insurers must establish robust data governance frameworks that include:
- AI governance committees: Form dedicated teams to oversee AI initiatives, ensuring they align with ethical standards.
- Data privacy protocols: Implement clear data usage, storage, and sharing guidelines and communicate these policies to customers.
- Compliance with regulations: Insurers must comply with data protection laws like the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which set guidelines on how customer data should be collected, stored, and processed.
- Compliance audits: Conduct routine evaluations to confirm AI adheres to evolving regulatory requirements.
Strong data governance protects customer privacy and boosts customer confidence.
Fostering human-AI collaboration
While AI offers remarkable capabilities, human oversight is indispensable. AI should augment, not replace, human experts. Critical decisions must ultimately rest with human judgment, ensuring empathy and context are considered.Incorporating human-in-the-loop systems allows insurance companies to:
- Continuously monitor AI decisions
- Intervene in complex cases
- Improve AI models through real-world feedback
This collaboration drives compliance and trust, assuring customers that AI does not operate unchecked.
Going beyond legal requirements, insurers can position themselves as ethical leaders—ensuring AI benefits both their business and their customers safely, fairly, and responsibly. AI serves as a tool for positive transformation, aligning with the core values of the insurance industry.
