Responsible AI in Insurance: Best practices for ethical and trustworthy models

Artificial intelligence (AI) has established itself as a central pillar of the insurance industry's digital transformation. From fraud detection to policy personalization, its analytical and automation capabilities redefine key processes.

However, the value of AI depends not only on its efficiency, but also on the trust it generates among customers, regulators, and society. This is where AI governance emerges as a strategic imperative.

For business leaders and decision-makers in the insurance sector, the challenge is no longer simply to implement AI models, but to do so under principles of responsibility, ethics, and transparency that ensure reliable and sustainable use.

AI Governance as a strategic priority

A recent McKinsey report on the state of AI reveals a key finding: “Twenty-eight percent of respondents whose organizations use AI say their CEO is responsible for overseeing AI governance, although the proportion is lower in larger organizations with annual revenue of $500 million or more, and 17% say AI governance is overseen by their board of directors.”

This finding confirms that, in practice, AI governance is distributed among different leaders, reflecting the need for a shared, cross-functional vision.

In the insurance sector, where AI models directly impact policy approvals, risk calculations, and claims payment decisions, governance becomes a factor of competitiveness and reputation.

Global governance and the sociotechnological dimension of AI

Beyond the business sphere, international organizations underscore the importance of governance as a global phenomenon.

During the UN Global Dialogue on AI Governance, António Guterres stated that this space is "the main global forum for collective reflection on this transformative technology."

He added that “AI is a sociotechnological phenomenon: to govern it, we must understand how people and societies experience and respond to it,” they quoted on the TechPolicy portal.

This implies that insurers, in addition to complying with regulations, must integrate a diversity of voices into their processes: clients, communities, and civil organizations that can ensure that algorithmic decisions do not reproduce biases or generate exclusions. AI governance, then, is not only technical, but also social and democratic.

From principles to practice: The challenge of implementation

UNESCO warns that there is a gap between abstract principles of AI ethics and their actual implementation in both the public and private sectors.

An example of how to overcome this challenge is the AI ​​Governance Clinic in Thailand, promoted by the Electronic Transactions Development Agency (ETDA). This program connects public officials, international experts, civil society, and businesses to transform principles into concrete practices.

Applied to the insurance sector, this means that it's not enough to have written AI ethics policies: practical mechanisms are required, such as independent model audits, data validation, and regular training for technical and business teams.

Key principles of AI Governance in insurance

IBM proposes a robust framework of responsible governance principles that are highly applicable to the insurance sector:

• Empathy: Understanding the social implications of AI. In insurance, this translates to assessing how an algorithmic decision may affect vulnerable customers, such as older adults or low-income populations.
• Bias Control: Ensuring that training data does not reproduce historical discrimination. For example, ensuring that an algorithm does not penalize health insurance applicants based on patterns associated with gender or geographic location.
• Transparency: Opening the “black box” of AI. Insurers must be able to clearly explain to a customer how the decision to approve or reject a policy was made.
• Accountability: Establishing strict standards of governance and acknowledging the impact of AI. This means insurers do not delegate all responsibility to algorithms, but rather leaders maintain final oversight.

These principles, far from being just ethical guidelines, are also strategic assets that strengthen the trust of customers and regulators, reducing legal and reputational risks.

Best practices for ethical and trustworthy AI in insurance

To implement the above principles, insurers can adopt a set of AI governance best practices:

• Internal AI Ethics Committees: Multidisciplinary teams including technical, legal, and business experts to oversee critical decisions.
• Algorithmic Impact Assessments: Systematic analyses that anticipate risks of discrimination or bias in models.
• External Algorithm Audits: Independent verification that ensures the transparency and fairness of systems.
• Ongoing Training: Training leaders and collaborators in digital ethics and governance.
• Customer and Stakeholder Engagement: Creating channels where policyholders can understand and challenge AI decisions.

These practices not only raise the industry's ethical standard, but also enhance brand reputation and foster customer loyalty who value transparency and fair treatment.

Responsible AI as a competitive differentiator

In a market as competitive as insurance, AI governance should not be viewed as simple regulatory compliance. Adopting a proactive and ethical approach becomes a competitive differentiator. Insurers that manage to implement trustworthy and transparent models will be better positioned to:

• Gain the trust of customers who demand clarity in decision-making.
• Comply with emerging regulations without late adaptation costs.
• Attract investment and strategic partners by demonstrating a commitment to technological ethics.
• Innovate with legitimacy, ensuring that new digital solutions are perceived as fair and secure.

Conclusion: Towards a future of trustworthy insurance

AI in insurance represents an extraordinary opportunity to optimize operations and generate value. However, this potential can only be fully realized if accompanied by a firm commitment to ethical governance.

As McKinsey, the UN, UNESCO, and IBM point out, the future of AI will not depend solely on technical advances, but on organizations' ability to responsibly manage their social impact. For insurers, adopting these practices is not only a moral obligation, but also a strategy for sustainable growth and long-term trust.

The challenge for business leaders is clear: it's not enough to have AI; we must have responsible, trustworthy, and humane AI. At Rootstack, we implement AI ethically and responsibly. Contact us!

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