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How to Put a Stop to AI Bias

Imagine you were suddenly refused insurance coverage, or your premium increased 50% just because of your skin color. Imagine you were charged more just because of your gender. It can happen, because of biased algorithms.

While technology improves our lives in so many ways, can we entirely rely on it for insurance policy?

Algorithmic Bias

Algorithms will most likely have flaws. Algorithms are made by humans, after all. And they learn only from the data we feed them. So, we have to struggle to avoid algorithmic bias — an unfair outcome based on factors such as race, gender and religious views.

It is highly unethical (and even illegal) to make decisions based on these factors in real life. So why allow algorithms to do so? 

Algorithmic Bias and Insurance Problems

In 2019, a bias problem surfaced in healthcare. An algorithm gave more attention and better treatment to white patients when there were black patients with the same illness. This is because the algorithm was using insurance data and predictions about which patients are more expensive to treat. If algorithms use biased data, we can expect the results to be biased.

It doesn’t mean we need to stop using AI — but, rather, that we must make an effort to improve it.

How Does Algorithmic Bias Affect People?

Millions of people of color were already affected by algorithmic bias. This bias mostly occurred in algorithms used by healthcare facilities. Algorithmic bias has also influenced social media.   

It is essential to keep working on this problem. In the U.S. alone, algorithms manage care for about 200 million people. It is difficult to work on this issue because health data is private and thus hard to access. But it’s simply unacceptable that Black people had to be sicker than white people to get more serious help and would be charged more for the same treatment. 

How to Stop This AI Bias?

We have to find factors beyond insurance costs to use in calculating someone’s medical fees. It’s also imperative to continually test the model and to offer those affected a way of providing feedback. By acknowledging feedback every once in a while, we ensure that the model is working as it should. 

See also: How to Evaluate AI Solutions

We have to use data that reflects a broader population and not just one group of people — if there is more data collected on white people, other races may be discriminated against.

One approach is “synthetic data,” which is artificially generated and which a lot of data scientists believe is far less biased. There are three main types: data that has been fully generated, data that has partially been generated and data that was corrected from real data. Using synthetic data makes it much easier to analyze the given problem and come to a solution.  

Here is a comparison: 

If the database isn’t big enough, the AI should be able to input more data into it and make it more diverse. And if the database does contain a large number of inputs, synthetic data can make it diverse and make sure that no one was excluded or mistreated. 

The good news is that generating data is less expensive. Real-life data requires a lot more work, such as collecting or measuring data, while synthetic data can rely on machine learning. Besides saving a lot of money, synthetic data also saves a lot of time. Collecting data can be a really long process.

For example, let’s say we are operating with a facial recognition algorithm. If we show the algorithm more examples of white people than any other race, then the algorithm will work best with Caucasian samples. So we should make sure that enough data has been produced that all races are equally represented.

Synthetic data does have its limitations. There isn’t a mechanism to verify if the data is accurate.

AI is obviously having a significant role in the insurance sector. By the end of 2021, hospitals will invest $6.6 billion in AI. But it’s still essential to have human involvement to make sure the algorithmic bias doesn’t have the last say. People are the ones that can focus on making algorithms work better and overcoming bias.

See also: How AI Can Vanquish Bias

Explainable AI

Because we can’t entirely rely on synthetic data, a better solution may be something called “explainable AI.” It is one of the most exciting topics in the world of machine learning right now.

Usually, when we have a certain algorithm doing something for us, we can’t really see what’s going on in the work with the data. So can we trust the process fully?

Wouldn’t it be better if we understood what the model is doing? This is where explainable AI comes in. Not only do we get a prediction of what the outcome will be, but we also get an explanation of that prediction. With problems such as algorithmic bias, there is a need for transparency so we can see why we’re getting a specific outcome. 

Suppose a company makes a model that decides which applications warrant an in-person interview. That model is trained to make decisions based on prior experiences. If, in the past, many women got rejected for the in-person interview, the model will most likely reject women in the future just because of that information.

Explainable AI could help. If a person could check the reasons for some of these decisions, the person might spot and fix the bias. 

Final words

We need to remember that humans make these algorithms and that, unfortunately, our society is still battling issues such as racism. So, we humans must put a lot of effort into making these algorithms unbiased.

The good news is that algorithms and data are easier to change than people.

How ‘Explainable AI’ Changes the Game

Artificial intelligence (AI) drives a growing share of decisions that touch every aspect of our lives, from where to take a vacation to healthcare recommendations that could affect our life expectancy. As AI’s influence grows, market research firm IDC expects spending on it to reach $98 billion in 2023, up from $38 billion in 2019. But in most applications, AI performs its magic with very little explanation for how it reached its recommendations. It’s like a student who displays an answer to a school math problem, but, when asked to show the work, simply shrugs.

This “black box” approach is one thing on fifth-grade math homework but quite another when it comes to the high-impact world of commercial insurance claims, where adjusters are often making weighty decisions affecting millions of dollars in claims each year. The stakes involved make it critical for adjusters and the carriers they work for to see AI’s reasoning both before big decisions are made and afterward so they can audit their performance and optimize business operations.

Concerns over increasingly complex AI models have fired up interest in “explainable AI” (sometimes referred to as XAI,) a growing field of AI that asks for AI to show its work. There are a lot of definitions of explainable AI, and it’s a rapidly growing niche — and a frequent subject of conversation with our clients. 

At a basic level, explainable AI describes how the algorithm arrived at the recommendation, often in the form of a list of factors that it considered and percentages that describe the degree that each factor contributed to the decision. The user can then evaluate the inputs that drive the output and decide on the degree to which it trusts the output.

Transparency and Accountability

This “show your work” approach has three basic benefits. For starters, it creates accountability for those managing the model. Transparency encourages the model’s creators to consider how users will react to its recommendation, think more deeply about them and prepare for eventual feedback. The result is often a better model.

Greater Follow-Through

The second benefit is that the AI recommendation is acted on more often. Explained results tend to give the user confidence to follow through on the model’s recommendation. Greater follow-through drives higher impact, which can lead to increased investment in new models.

Encourages Human Input

The third positive outcome is that explainable AI welcomes human engagement. Operators who understand the factors leading to the recommendation can contribute their own expertise to the final decision — for example, upweighting a factor that their own experience indicates is critical in the particular case.

How Explainable AI Works in Workers’ Comp Claims

Now let’s take a look at how explainable AI can dramatically change the game in workers’ compensation claims.

Workers comp injuries and the resulting medical, legal and administrative expenses cost insurers over $70 billion each year and employers well over $100 billion — and affect the lives of millions of workers who file claims. Yet a dedicated crew of fewer than 40,000 adjusters across the industry is handling upward of 3 million workers’ comp claims in the U.S., often armed with surprisingly basic workflow software.

Enter AI, which can take the growing sea of data in workers’ comp claims and generate increasingly accurate predictions about things such as the likely cost of the claim, the effectiveness of providers treating the injury and the likelihood of litigation.

See also: Stop Being Scared of Artificial Intelligence

Critical to the application of AI to any claim is that the adjuster managing the claim see it, believe it and act on it — and do so early enough in the claim to have an impact on its trajectory.

Adjusters can now monitor claim dashboards that show them the projected cost and medical severity of a claim, and the weighted factors that drive those predictions, based on:

  • the attributes of the claimant,
  • the injury, and
  • the path of similar claims in the past

Adjusters can also see the likelihood of whether the claimant will engage an attorney — an event that can increase the cost of the claim by 4x or more in catastrophic claims.

Let’s say a claimant injured a knee but also suffers from rheumatoid arthritis, which merits a specific regimen of medication and physical therapy.

If adjusters viewed an overall cost estimate that took the arthritis into account but didn’t call it out specifically, they may think the score is too high and simply discount it or spend time generating their own estimates.

But by looking at the score components, they can now see this complicating factor clearly, know to focus more time on this case and potentially engage a trained nurse to advise them. Adjusters can also use AI to help locate a specific healthcare provider with expertise in rheumatoid arthritis, where the claimant can get more targeted treatment for a condition.

The result is likely to be:

  • more effective care,
  • a faster recovery time, and
  • cost savings for the insurer, the claimant and the employer

Explainable AI can also show what might be missing from a prediction. One score may indicate that the risk of attorney involvement is low. Based on the listed factors, including location, age and injury type, this could be a reasonable conclusion.

But the adjuster might see something missing. They adjuster might have picked up a concern from the claimant that he may be let go at work. Knowing that fear of termination can lead to attorney engagement, the adjuster can know to invest more time with this particular claimant, allay some concerns and thus lower the risk the claimant will engage an attorney.

Driving Outcomes Across the Company

Beyond enhancing outcomes on a specific case, these examples show how explainable AI can help the organization optimize outcomes across all claims. Risk managers, for example, can evaluate how the team generally follows up on cases where risk of attorney engagement is high and put in place new practices and training to address the risk more effectively. Care network managers can ensure they bring in new providers that help address emerging trends in care.

By monitoring follow-up actions and enabling adjusters to provide feedback on specific scores and recommendations, companies can create a cycle of improvement that leads to better models, more feedback and still more fine-tuning — creating a conversation between AI and adjusters that ultimately transforms workers’ compensation.

See also: The Future Isn’t Just for Insurtech

Workers’ comp, though, is just one area poised to benefit from explainable AI. Models that show their work are being adopted across finance, health, technology sectors and beyond.

Explainable AI can be the next step that increases user confidence, accelerates adoption and helps turn the vision of AI into real breakthroughs for businesses, consumers and society.

As first published in Techopedia.