Delivering financial ethics in the age of AI

Ethics should never be an afterthought when deploying artificial intelligence (AI) systems in finance. To the contrary, embracing a culture of ethical conduct among AI development teams contributes to systems that are more effective in the long run, keeping customers and shareholders comfortable.

The AI gold rush has been underway in the financial services industry for the past few years. According to the UK Financial Conduct Authority and the Bank of England, two-thirds of Britain’s financial services firms use some form of machine learning. Just over half have an R&D strategy to add even more to their AI capabilities. That strategy needs a well-developed ethical component.

Financial regulators can hardly be expected to keep up with an industry that by the day devises novel techniques to exploit the power of automation, such as using AI to optimise risk modeling in insurance, authenticating transactions or automating pre-trade analysis. The U.S. Securities and Exchange Commission (SEC) uses an in-house natural language processing system to flag potential misconduct in filings, which the agency says makes its human auditors five times more effective at their job. That’s a huge advance, but it falls short of rethinking the agency’s broader mission in light of what’s possible with AI. As the agency itself notes, it will take time to replace all the old-fashioned requirements like paper filing of some reports.

Regulation happens at the speed of the Administrative Procedures Act, which was intended to slow the pace of governance so that the public – and sometimes the courts – would have an opportunity to weigh in. Innovation, on the other hand, doesn’t wait on the schedule of the printing press at the Federal Register.

That means it’s up to fintech companies to do what’s necessary to implement common sense ethical guidelines when deploying automated solutions. Fortunately, most of the hard work on that front has already been done. The Institute of Electrical and Electronics Engineers (IEEE) created principles for what it calls “Ethically Aligned Design” which are generally applicable across most industries. These are eight concepts that, if followed, ought to keep financial companies that implement AI solutions out of trouble.

Briefly, the Ethically Aligned Design principles are:

– Create systems that respect human rights.
– Make increased human well-being the measure of success in AI development.
– Give individuals the right to access and share their own data.
– Prove the AI system is effective.
– Be sure you can document and explain the reason behind each of the autonomous systems’ decisions.
– Make sure accountability is built into the system so that people can be held responsible for mistakes.
– Build safeguards to prevent misuse.
– Make sure operators of AI systems are properly trained to use the systems properly.

They’re not supposed to be deep insights. Rather, they serve as critical reminders about what must be done to keep AI projects from going down a dark path. And these ideas take on particular significance for an industry that holds the life savings of millions of people in its hands, with all of the privacy and security implications that entails.

The good news is that there’s plenty of time for the industry to get this right. Despite widespread use of various forms of AI technology in finance, we’re very much in the early days of reaching the full potential of what autonomous systems can do. If an early system happened to be designed without ethics in mind, it’s probably time for a change anyway. Systems that today might be considered “old” should probably be replaced. In terms of hardware capabilities, the speed of processing AI algorithms has quadrupled in less than two years, while software capabilities are expanding just as fast. The best time to validate that ethical concerns have been fully addressed is before any autonomous system is deployed. That’s how problems are avoided.

Shedding light on the black box

The classic ethical dilemma applicable to most forms of machine learning is known as the “Black Box” problem. An algorithm “learns” by scanning datasets and self-adjusting its internal search parameters until it can detect patterns with the required level of accuracy. Such algorithms can be used by humans who have no idea how the machine arrived at its answer. It just works.

But in finance, such a lack of transparency can create big problems. Why did that algorithm flag this credit card purchase as suspicious? Customers don’t appreciate the inconvenience of having legitimate transactions declined. Or, far worse, an unchecked algorithm could inadvertently develop race-based patterns for mortgages, raising not just ethical but also serious legal issues.

One of the most effective ways to minimise the chance of this happening is to prefer augmented intelligence solutions over simple machine learning options. These are solutions that automate the collection of information and provide expert system analysis with recommendations presented to human operators who are entrusted with the judgment call about what to do. This arrangement addresses the IEEE concerns about transparency and agency because the human remains in control and can explain the rationale behind each decision. Humans can be held to account for their conduct.

The ethical landmines with AI

While augmented intelligence represents the most effective approach to ensuring ethics in AI, that’s just one of the tools in the toolbox. Others will still be needed.

Perhaps the industry’s most widespread form of automation is algorithmic trading. These systems are so advanced that high-frequency traders found value in the minuscule difference between the time it takes for a beam of light to travel down a fibre optic data cable and for an electromagnetic wave to bounce from one microwave tower to the next. A few additional milliseconds means beating the competition, yet no human could possibly act on such an imperceptible difference.