Bias in AI models is a serious concern for the insurance industry. When companies use artificial intelligence to make decisions, they might not realize the hidden dangers. This article explains how these intelligent systems can accidentally break the law and lead to expensive problems for insurance providers. We will look at real examples and explore the legal, technical, and ethical perspectives on this modern challenge.
What is AI Bias in Insurance?
AI bias in insurance happens when a computer program makes unfair decisions against certain groups of people. This occurs because the AI learns from historical data that may contain old prejudices.
For example, an AI used for underwriting might learn from past data that people from a specific neighborhood file more claims. Daniel Lewis, CEO of LegalOn, explains that AI models may “discriminate when they texture on proxies such as ZIP codes or credit history,” as these factors can stand in for racial or socioeconomic factors. An individual homeowner in a certain neighborhood may be marked as a higher risk despite no personal claims history.
How Does Bias Get into AI Systems?
Bias gets into AI systems primarily through the data they are trained on. An algorithm is like a student; it can only learn from the textbook it is given. If the textbook (the data) is flawed, the student’s knowledge will be flawed too.
Mircea Dima, a CEO and software engineer with expertise in AI systems, notes that results “could be discriminative… when historical data, which is biased towards systemic inequality… is used during the learning process.” Insurance data collected over decades can reflect these past inequalities. The AI then copies these patterns and makes them seem official and scientific.
How Can AI Bias Lead to Legal Trouble for Insurers?
Bias in AI models can lead to legal trouble by causing discrimination. Insurers must follow strict laws that promise fair treatment for all customers. When an AI system breaks these rules, the company can be held responsible.
A biased algorithm could result in discrimination lawsuits from customers who were treated unfairly. Government agencies might also investigate and fine the company. Even if the bias was accidental, the insurer is still accountable. As Mircea Dima states, “The insurers are liable under the laws… liabilities fall under discrimination, although this might not have been their purpose.”
The Two Main Legal Threats: Lawsuits and Regulations
The legal threats from bias in AI models generally come from two directions. The first is individual or class-action lawsuits from policyholders. The second is enforcement action from government regulators.
1. Discrimination Lawsuits
Customers can sue an insurance company for unfair treatment. Jimmy Fuentes, a Consultant with experience in risk assessment, points out that when a model “systematically disfavors a protected class, the underwriter may be sued in civil lawsuits.” If the AI’s decision-making process is opaque, courts may rule against the insurer.
2. Regulatory Penalties
Government bodies have the power to investigate insurers. Daniel Lewis highlights that AI use is “coming under more significant scrutiny from regulators under the unfair-and-deceptive-practices authority wielded by the FTC.” André Disselkamp, CEO of Insurancy, adds that failures can “result in regulatory fines and civil liability,” especially with new proposed laws mandating risk assessments.
What are Some Examples of Biased AI in Insurance?
Real-world examples show how bias in AI models can appear. André Disselkamp provides a clear case: “In an audit we reviewed, an insurer’s auto‑claim system revealed a 12% higher denial rate in coastal ZIP codes, a baked‑in artifact of historical data, not an accurate reflection of risk.”
Jimmy Fuentes shares another example: “A predictive model by an insurer which applies zip code as an approximation of the risk can over-price communities which have already suffered due to the red-lining. One of the cases involved an insurer in California that charged premiums higher to a small number of neighborhoods and a lawsuit ensued.”
What Laws and Rules Govern AI Fairness?
Several legal frameworks aim to ensure AI fairness and accountability. In the United States, laws like the Fair Housing Act and the Equal Credit Opportunity Act forbid discrimination. Jimmy Fuentes also notes the role of state laws like the “California Privacy and Consumer Protection Act [which] brings in an element of transparency.”
Globally, the landscape is changing. Daniel Lewis mentions that “in the EU, once it comes into force, the AI Act is going to place insurers under strict accountability obligations.” These laws apply to insurance decisions, whether made by a person or a machine.
The Principle of “Disparate Impact”
A key legal idea is “disparate impact.” This means a policy or practice is illegal if it has a disproportionately negative effect on a protected group, even if the company did not intend to discriminate. Bias in AI models often creates a disparate impact.
For example, an insurer might not ask for race information. But if their AI uses zip code as a factor, and certain zip codes are predominantly Black, the algorithm could have a disparate impact on Black applicants. The law focuses on the outcome, not the intent.
How Can Insurers Reduce the Risk of AI Bias?
Insurers can adopt several best practices to make their AI systems safer. The experts provide a clear consensus on the necessary steps. André Disselkamp recommends a proactive approach: “Audit data sources for demographic skew… Guarantee model explainability… Conduct independent bias testing before deployment… Set up continuous monitoring.”
Jimmy Fuentes adds that insurers should “carry out frequent bias audits by third-party auditors” and “keep records that indicate how data were sampled, cleansed and utilized.”
A Step-by-Step Plan for Mitigating Bias
A good plan to manage bias in AI models involves multiple steps.
Step 1: Audit Your Data
Before building a model, carefully examine the training data. Look for historical patterns that might reflect bias. Work to clean the data and make it more representative.
Step 2: Choose Transparent Algorithms
Where possible, use AI models that are easier to understand. Daniel Lewis advises that insurers should “document explainability for each decision pathway.” This helps your team figure out why the AI made a decision.
Step 3: Implement Human Oversight
Always keep a human in the loop for final decisions. Daniel Lewis recommends a “human-in-the-loop review when working on higher-impact decisions, such as fraud flags or coverage denials.” Mircea Dima agrees, stating, “You cannot give responsibility to a program.”
Step 4: Create a Bias Response Plan
Have a plan for what to do if someone alleges your AI is biased. This plan should include how to investigate the claim quickly and fairly.
What is the Future of AI Liability in Court?
Predicting how courts will handle bias in AI models cases is difficult, but trends are emerging. The experts agree that courts will not accept ignorance as a defense. Daniel Lewis believes that “courts will treat AI models as an extension of policies defined by the respective companies and not as a ‘black box.’ Hence, no insurer can get away with the defense, ‘the algorithm made the decision.'”
André Disselkamp predicts courts are “moving toward a ‘reasonable foreseeability’ standard. If an insurer can’t demonstrate proactive bias mitigation, liability is highly likely.” Jimmy Fuentes concludes that insurers will be pressured “to prove that disparate impact models have been put to the test,” or they may face penalties.
The message is clear: using AI requires a strong sense of responsibility. Proactive measures are the best defense against future liability.
Conclusion
The integration of AI into insurance offers great potential for efficiency, but it also introduces significant legal risks through bias in AI models. The key takeaway is that insurers are fully responsible for the decisions made by their algorithms, whether those decisions are intentional or not. As the experts have shown, the path forward requires a commitment to transparency, continuous auditing, and meaningful human oversight. By treating fairness as a core requirement, not an afterthought, insurers can harness the power of AI while building trust and staying on the right side of the law. The future of insurance depends not just on smarter technology, but on fairer and more accountable practices.
