The Need for Fair and Ethical AI in Insurance

AI is revolutionizing the insurance industry today by enhancing underwriting, claims processing, customer service, and product development. AI has already proven its value in automating manual tasks, improving risk assessment practices, detecting fraud, personalizing customer interactions, and enabling predictive pricing.

Thanks to these new capabilities, AI now delivers a wide range of significant benefits for insurers: increased efficiency, cost savings, enhanced productivity, and higher levels of customer satisfaction, engagement, and retention.

Yet the rapid advancement and widespread of adoption of AI in the insurance industry leads to new questions about the potential for biases and discrimination – as well as AI’s overall implications for insurers.  

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Negative Ethical Implications of AI in Insurance

Insurers’ concerns are well-founded. The use of AI raises new questions where carriers are looking to use AI to produce fair and equitable outcomes that represents their customers’ best interests.

According to KPMG’s 2023 CEO Outlook Survey, 57% of business leaders expressed concerns about the ethical challenges created by implementing AI.

Algorithmic Unfairness and Bias in Underwriting and Pricing

For example, consider the case where an insurance carrier offers auto insurance where an AI-driven pricing model uses a dataset that includes many different factors such as driving history, vehicle type, mileage, geographical location, and other demographic information.  

While this model doesn’t use race, gender, or income as variables, it may use proxy factors that are highly correlated with these characteristics. It’s possible that the resulting pricing model could unfairly penalize individuals based on characteristics that are proxies for race, socio-economic status, or other protected details. Without full visibility into the AI algorithm and how it’s using data, there’s a risk that biases in the training data could lead to discriminatory outcomes.

As insurers look to incorporate new data sources and apply new ML modeling techniques, it is critical to provide complete transparency and traceability to regulators (or anyone else looking to understand pricing outcomes). The faster that insurance companies can document various pricing decisions and processes, the faster they’ll be able to gain approval.

Potential Impacts on Customer Privacy and Data Protection

AI-driven decision-making in insurance relies heavily on analyzing vast amounts of personal data, raising significant concerns about customer privacy and data protection. The extensive data collection and processing required can lead to potential breaches and even the misuse of sensitive information. All of this puts more emphasis on the need for comprehensive data security practices to minimize these risks and maintain customers’ trust.

Collecting, storing, and using personally identifiable information (PII) data in AI-driven insurance processes pose risks such as data breaches, unauthorized access, and unauthorized access by internal or external parties who might misuse the data for malicious purposes. There is also the risk of data being used beyond its intended scope, leading to potential privacy violations and discrimination.

Transparency and Explainability of AI Decisions

For insurers to trust and adopt the use of AI, there needs to be a mechanism that gives stakeholders and business professionals the ability to interpret complex AI decision-making processes and ensure they abide by regulatory demands.

Industry experts are now developing tools that enable us to peek inside the AI “black box” and understand some of the magic. Explainability is the bridge that makes AI more understandable and transparent and leads to equal opportunities and outcomes across different groups of individuals, regardless of their race, gender, age, or other sensitive attributes.

Social and Economic Implications of AI-Driven Insurance Practices

There is concern that the use of AI could have an adverse effect on the workforce. For example, the view that AI could lead to job displacement in roles traditionally performed by human employees, especially those in underwriting, claims processing, and customer service.

Yet to look at it as a glass-half-full opportunity, AI can help employees become more productive and even more competitive in their role or field. AI will continue to automate repetitive, time-consuming tasks, yet it will also deliver faster access to vital information and provide new alternatives to conventional insurance processes in key lines of business such as rating, pricing, and risk management. As a result, AI will free workers to focus on more complex, strategic, and more rewarding work that requires critical thinking, creativity, and interpersonal skills.

As the use of AI continues to develop, employees will have the opportunity to reskill, upskill, and gain new competencies in important areas such as data analysis, AI management, and the use of other advanced technologies. All of this can lead to better job satisfaction and new career prospects.

Best Practices for Ethical AI Use in Insurance

All of this points to the need for clear guidelines and policies when developing and implementing AI models in insurance as well as the need to incorporate diverse perspectives in AI decision-making processes.

Establishing Clear Guidelines for Ethical AI Development and Deployment

Developing clear, comprehensive guidelines and policies for ethical AI is crucial to ensure fairness, transparency, and accountability in AI-driven decisions. These guidelines help protect customer privacy and data security, mitigate biases, and prevent discriminatory practices. By establishing ethical standards, insurers can maintain public trust, comply with regulations, and promote the responsible use of AI technologies in their operations.

When establishing guidelines for ethical AI in insurance, insurers should do all they can to maximize data privacy and security to protect sensitive customer information. Additionally, promoting transparency in AI decision-making processes and complete accountability are essential to maintain customer trust and regulatory compliance.

Insurers would also be wise to engage a diverse range of stakeholders in the development and testing phases to do all they can to make AI systems fair, transparent, and unbiased. Ideally, this should include customers, employees, and even various regulatory groups.

Insurers should also clearly communicate how AI systems make decisions to demonstrate how these processes work and that they’re understandable to non-technical users and stakeholders to build further trust and accountability.

Incorporating Diverse Perspectives and Expertise in AI Decision-Making Processes

Incorporating diverse perspectives in AI decision-making processes is essential to ensure fairness, transparency, and effectiveness. Different stakeholders, including customers, employees, regulators, and community representatives, offer unique insights and experiences that can uncover biases, identify blind spots, and mitigate unintended consequences in AI systems.

By involving diverse voices, organizations can better understand the ethical implications of their AI applications, anticipate potential risks or harms, and design solutions that align with societal values and expectations. Moreover, inclusive decision-making fosters trust and legitimacy, enhancing the acceptance and adoption of AI technologies while promoting equitable outcomes for all stakeholders involved.

Such an approach can pay off by reducing overall bias and discrimination, improving problem-solving and innovation, and increasing stakeholder trust and acceptance.

Ensuring Transparency and Accountability in AI-Driven Decision-Making

Transparency and accountability in AI-driven decision-making are an important way to make sure that AI systems operate fairly, ethically, and without bias. These steps allow stakeholders to understand how decisions are made, which goes a long way to fostering trust and confidence in the technology. Clear accountability protocols ensure that any errors or harms caused by AI can be promptly addressed and rectified.

Insurers can promote transparency with AI models by providing detailed documentation and explanations of how these models work, including the data sources, algorithms used, and decision-making criteria. They can give their customers access to simplified summaries or visualizations that illustrate how their data influences outcomes, such as premium calculations or claim approvals.

We are already seeing examples of this today. Consider the case of a usage-based insurance (UBI) program that uses telematics to collect data related to mileage, speeding, acceleration, hard braking, and other variables as part of its AI model. We’re also seeing examples where AI can estimate the risk of potential fire incidents by analyzing various data sources such as weather patterns, past wildfire occurrences, vegetation density, and human activity.

These AI-driven models and insights help customers understand what’s incorporated into the price of their policy and determine what actions they may need to take to remain insurable in the future.  

Insurers can also implement a system for regular audits and third-party reviews of their AI models to ensure accuracy and fairness. These efforts can help them become even more compliant, especially as they continue to iterate on business models more frequently than they may have in the past. It’s a valuable step to make sure they have the right tools, processes, and people in place to prepare – and meet – evolving regulatory demands.

It is important to note that there are controls in place to hold insurers accountable, with many more to come. For example, in the U.S., the National Institute of Standards and Technology (NIST) is currently developing a framework to improve the management and governance of AI systems, with a specific focus on transparency and accuracy. Additionally, the proposed Algorithmic Accountability Act is intended to require companies to evaluate the impact of their AI systems, especially in terms of bias.

Additionally, industry standards and guidelines from organizations like the National Association of Insurance Commissioners (NAIC) provide oversight and best practices for ethical AI use in insurance. These regulations and standards ensure that insurers adhere to ethical practices, protect consumer rights, and face penalties for non-compliance. 

Conclusion

Interested in learning more about fair and ethical AI in insurance? Watch our on-demand webinar, “Responsible AI: Privacy, Transparency, and Fairness in Insurance” or visit www.earnix.com today.