The Balancing Act Between AI Ethics and Insurance Innovation
Machines thinking like humans are not like Agent Smith, in the Matrix trilogy. The sentient bad guy assumes the form of a white male in a black suit, frameless dark glasses, and an earpiece. He belongs to a class of AI programs known as “Agents” who are tasked with protecting the Matrix and are superhumanly strong with contempt for humanity and think they need to be erased like a virus. That sounds exciting and terrifying but in truth, AI in everyday life is much more commonplace. It could be as simple as unlocking your phone with your face. There is a common factor though between Agent Smith and present-day AI programs — Is AI governed by any ethics and are there systemic AI biases that can creep in that could discriminate against people? And even more distressing, could AI come to conclusions without the need to explain the process because after all, they learn from experience?
As AI becomes the fuel that is powering innovation, particularly in highly regulated industries such as insurance, AI ethics can no longer be glossed over.
Why AI bias can have far-reaching implications for stakeholder trust
Insurance companies are using AI and machine learning to automate parts of the claim handling process and to improve customer service. AI is also displaying its power to revolutionize the way we buy auto insurance because of telematics and smartphones. Newer technologies such as those deployed in usage-based insurance bring up the inevitable discussions on AI bias and data privacy.
AI ethics is relatively nascent compared to the evolution of artificial intelligence. We all know that artificial intelligence is the simulation of human intelligence in machines by training them to think by feeding data into computer algorithms. When humans develop new technologies it normally starts simple and as it gets more advanced that is when questions of ethical usage start arising. Let’s put this in perspective with two examples. The first goes back to a time before AI and the second shows how bias can be unconscious when building AI algorithms.
Until 2011, women were 47% more likely to be injured in a car crash. Further, fetal death was the highest cause of maternal trauma from a car crash. Digging deeper into this, it was found that manufacturers did not take into account the impact that the seat belt design would have on pregnant women. Even more surprising was that since auto manufacturers were using male bodies as test dummies for test crashes, the design effect on women was never studied. There was no malice in this oversight, just that new technology was being tested by the people who built it and they were males.
The next example is more recent from the financial sector where a large regional organization used a fraud detection algorithm to identify potential cases of fraudulent transactions. The algorithm was trained on a set of data to understand what a fraudulent transaction would look like compared to a normal one. Since it was dealing with big data, the fact that it contained a larger sampling of fraudulent applicants over 45 years old flew under the radar. This oversampling continued over months without being detected and the model began to consider that older folk were more likely to commit fraud. Only when regulators started asking questions because customers were raising complaints was the AI bias detected,
AI in everyday applications also shows such bias. For instance, voice recognition software has difficulty interpreting female voices. Again the reason is that R&D teams testing this software are normally dominated by men.
These examples underline the fact that by the time businesses come to a realization of ethical risk the effects are never small. Of course, AI will scale up operational efficiencies, risk mitigation and fraud detection. The other side of the coin is that if there are no checks and balances then AI ethical risk can have a massive negative impact on brand perception, a legal and financial impact, and also a loss of customer trust that is often painstakingly built through the years.
The checks and balances involved in responsible AI
It is important to make the distinction that data models used to train machine learning algorithms are not biased, rather it is the training data that can render them biased. So how does insurance move forward with innovative technologies when customers are looking for proof of fairness? The solution will probably involve both external factors such as progressive regulatory standards, and internally through a corporate cross-functional lifecycle process for AI model governance.
When it comes to America, there are significant AI regulations in the pipeline. The Algorithmic Accountability Act drafted in 2019 did not pass the US senate. Now though, the White House is proposing an AI Bill of Rights to make AI systems more accountable and give consumers the right to transparency. As we wait for the outcomes on that, it is important to note that while the federal government has still to legislate on this, many states in the US have enacted AI ethical laws.
Internal AI model governance
For the most part, insurance has been relatively transparent when it came to consumers and businesses buying insurance. If you live in a flood zone, for example, the relationship between risk and premiums is easy to understand. However, as big data and artificial intelligence take the burden off the shoulders of underwriters and claim adjudicators, more attention
However, even with the best intentions, humans are at the center of development and errors can still occur even with the best cross-functional governance in place. Business owners and other stakeholders should have technical dashboards that can evaluate system performance and know what course correction was made to identify any bias and rectify it. What comes across is that transparency is vital for all players involved in implementing new technologies, the customer being a vital part of it. Balancing commercial interests, and protecting proprietary technology while at the same time maintaining customer trust, appears a conundrum but one that needs to be solved.
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Originally published at https://www.simplesolve.com on October 19, 2022.
