According to one recent study, in 2022 alone, about 53% of all global IT companies reported some level of "acceleration" of AI adoption over the previous two years.
But this is hardly a new trend.
Gartner estimated that between 2015 and 2019, the number of businesses in all industries taking advantage of AI services grew by an incredible 270%.
In a broad sense, AI-powered systems can create, ingest, share, and analyze massive volumes of data, essentially in real-time.
It doesn't matter what type of business you're running or even the industry you're operating in - the implications of this are massive.
Even if you take something as "straightforward" as ChatGPT, the potential applications are fascinating.
People are already using it for everything from debugging countless lines of code to explaining complex topics in easily understandable ways to full-on content creation.
Keep in mind, this is just something that is available to the public.
But using AI to help you study for your upcoming exam in college is one thing.
A doctor using it to help provide a treatment plan for a patient is something else entirely.
This isn't a situation we still have years left to spend contemplating, either.
AI in healthcare is here.
There are solutions aimed at making AI for community banks more accessible than ever.
Microsoft Copilot has brought an artificial intelligence assistant to nearly every computer running the Windows operating system.
As AI begins to ingrain itself in industries like healthcare and finance more and more, it demands the question: what ethical considerations are there and what values do we have a collective obligation to uphold?
The answer to that question, unfortunately, isn't quite as easy to answer as one might hope.
Especially when it comes to the sophisticated, technology-driven world that we're now living in, it's important to remember the age-old concept of "just because we can do something doesn't mean we should."
Artificial intelligence is a primary example of that, especially when it comes to both the healthcare and the financial sectors.
One of the most important benefits that AI brings with it involves giving professionals the ability to free up as much of their time as possible to focus on more important matters.
Regardless of how AI is deployed in these two sectors, it's likely going to help accomplish these goals.
But we must all be mindful to do so in a way that mitigates as much of the ethical risk as possible.
One of the biggest ethical considerations of artificial intelligence in any environment has to do with bias.
Remember that AI systems and algorithms are only as effective as the data they've been trained on.
But at the same time, those same systems can inherit biases from that training data - or, in a larger sense, the biases of their creators.
To address this, we need to do whatever it takes to make sure that training datasets are as diverse as the audiences they reflect.
There needs to be a fairness metric that is implemented to guarantee equitable outcomes for all demographic groups whenever possible.
The issue with data bias is that it tends to creep up slowly - you may not even realize it's happening at first.
Another major ethical consideration when it comes to AI has to do with the potential for privacy breaches.
The types of AI systems that we're talking about don't just process vast amounts of data - much of it is personal in nature.
What happens if that privacy is violated?
Consider the fact that, according to one recent study, the average cost of a data breach across industries was about $4.45 million per incident.
That's already a staggering number that you likely want to do whatever it would take to avoid.
This will become especially urgent when you realize that the healthcare industry's cost actually comes in much higher, at a stunning $10.93 million per incident.
This is why data collection needs to be executed with the consent of those impacted (the patients in a healthcare setting, for example) and they must follow the law.
Robust security measures and safeguards need to be put in place to protect sensitive information.
Data should be made anonymous whenever possible to help avoid these types of issues in the first place.
Finally, decision-making transparency is a massive ethical consideration when it comes to AI and it's one that, again, is unfortunately difficult to solve.
Part of the point of AI is to help provide organizational leaders with the insight they need to make the right decisions at the right time.
This is true both in terms of healthcare and finance especially.
But what types of data are being used to make those decisions?
It's a bit more complicated than just letting a computer solve your math problem for you - the system needs to show its work.
In this context, that's called interpretability - AI systems should provide explanations for their recommendations or decisions, much in the same way a human would.
There also must be clear lines of responsibility for AI-generated outcomes and humans should control crucial decisions, having the ability to override whatever the AI system says to do when the need arises.
Is living in a world where healthcare and financial professionals have more time to devote to individual patients and clients worth the potential ethical consequences of artificial intelligence in this context?
It's difficult to say - like the technology itself, there's no "one size fits all" answer.
This is why we must all be mindful - from patients to healthcare providers to consumers and financial institutions, to understand how we will be impacted by AI-powered decisions and how we can collectively work to enjoy all the benefits of this process with as few of the potential downsides as we can.
If nothing else, success in the AI era for industries like healthcare and finance will depend upon balance.
Healthcare professionals need to carefully consider all the AI ethics considerations outlined above and address them, but not at the expense of some advancement that might literally change the way we provide healthcare to countless people for the better.
Both of these two concepts can co-exist - we just need to find a way to make that possible.
As organizations continue to pursue these groundbreaking advancements - and they will - AI ethics standards must be enacted that also comply with all regulatory frameworks that apply.
This includes but is certainly not limited to the GLBA in finance and HIPAA in the healthcare industry.
But it's also essential not to limit your line of thinking to the present tense.
Yes, we need to act ethically and responsibly when utilizing the AI tools that are already out there.
We must also apply those same standards to the ones that do not yet exist.
In 10 years, the type of solution aimed at AI for credit unions may have capabilities that are literally unthinkable right now.
This is especially likely in terms of using things like predictive analytics, risk assessment models, and more - all to optimize the decision-making process.
This means that those same concerns pertaining to data privacy, transparency, and bias will become more pressing, not less.
Organizations in healthcare, finance, and in any other industry will need to regularly audit their system to be on the lookout for issues like bias.
There must never come a time when key decision-making is turned over to the artificial intelligence system 100% of the time.
Humans must always play an important and active role in the proceedings.
Again, it's all about striking that balance between innovation, ethics, and compliance.
None of these elements can afford to become an afterthought, but if any one of them is given too much weight over the others, it runs the risk of introducing potentially catastrophic consequences for every AI-driven interaction.
In an effort to create more robust internal policies that govern the use of AI in a healthcare or finance environment, you need to begin by creating as much clarity as possible.
Internal policies should be created that go into great detail about:
Much of this will require organizations to lean upon the regulatory frameworks that are already there.
HIPAA, for example, is fairly clear about what steps healthcare organizations need to take to protect patient confidentiality and to guarantee that medical data is handled with care.
In finance, the GLBA does largely the same thing.
Create policies that are in line with these documents, not at odds with them.
It's also imperative that organizations implement an ethics review process whenever possible.
Regulator audits and assessments need to be conducted to guarantee that the AI system is still operating in the way that it's supposed to be, free from bias or discrimination.
Any organization should operate in the best interests of its customers and clients at all times.
In a lot of ways, this is one of the primary reasons why any entrepreneur opens a business in the first place.
You want to do something of value for people that they might not otherwise be able to get on their own.
When you open that concept up to areas like healthcare and finance, you add in the additional element of serving the public trust.
That's why AI ethics are of paramount importance.
Once you start talking about a system that can theoretically do just about anything, you are forced to ask difficult questions about the best and most responsible ways to wield that power.
AI ethics serve as the rock-solid foundation upon which everything else is built.
They dictate how AI systems should be deployed.
It makes sure that progress can still come without harming societal values or running afoul of legal requirements.
The list goes on and on.
In healthcare, the types of powerful AI systems being discussed can not operate without making patient confidentiality a priority.
Treatment recommendations need to be fair.
In finance, steps must be taken to guarantee that unintended biases never make their way into the types of decisions that impact people's lives.
This is why developers and organizational leaders need to be educated about relevant industry standards and protocols.
Bringing ethics into AI education and training programs ensures that future generations of AI developers are equipped to navigate ethical challenges responsibly in ways that their contemporaries only had to think about after the fact.
By emphasizing ethics alongside technical expertise, organizations can create a culture of ethical awareness and responsibility throughout their AI initiatives.
Remember: "just because we can do something doesn't mean we should."
At the very least, opening a larger discussion around AI ethics today means that we won't wind up in a scenario where we realize something is an issue only after we've already done it.
If you'd like to find out more information about the ethics of AI in healthcare and finance organizations, or if you want to continue to learn about the tech-driven "Brave New World" that we consistently find ourselves in, please don't delay - contact us today.
