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Artificial Intelligence

A Practical Guide to Building Ethical AI

3 Mins read

Companies are leveraging data and artificial intelligence to create scalable solutions — but they’re also scaling their reputational, regulatory, and legal risks. For instance, Los Angeles is suing IBM for allegedly misappropriating data it collected with its ubiquitous weather app. Optum is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients. Goldman Sachs is being investigated by regulators for using an AI algorithm that allegedly discriminated against women by granting larger credit limits to men than women on their Apple cards. Facebook infamously granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million users.

Just a few years ago discussions of “data ethics” and “AI ethics” were reserved for nonprofit organizations and academics. Today the biggest tech companies in the world — Microsoft, Facebook, Twitter, Google, and more — are putting together fast-growing teams to tackle the ethical problems that arise from the widespread collection, analysis, and use of massive troves of data, particularly when that data is used to train machine learning models, aka AI.

These companies are investing in answers to once esoteric ethical questions because they’ve realized one simple truth: failing to operationalize data and AI ethics is a threat to the bottom line. Missing the mark can expose companies to reputational, regulatory, and legal risks, but that’s not the half of it. Failing to operationalize data and AI ethics leads to wasted resources, inefficiencies in product development and deployment, and even an inability to use data to train AI models at all. For example, Amazon engineers reportedly spent years working on AI hiring software, but eventually scrapped the program because they couldn’t figure out how to create a model that doesn’t systematically discriminate against women. Sidewalk Labs, a subsidiary of Google, faced massive backlash by citizens and local government officials over their plans to build an IoT-fueled “smart city” within Toronto due to a lack of clear ethical standards for the project’s data handling. The company ultimately scrapped the project at a loss of two years of work and USD $50 million.

Despite the costs of getting it wrong, most companies grapple with data and AI ethics through ad-hoc discussions on a per-product basis. With no clear protocol in place on how to identify, evaluate, and mitigate the risks, teams end up either overlooking risks, scrambling to solve issues as they come up, or crossing their fingers in the hope that the problem will resolve itself. When companies have attempted to tackle the issue at scale, they’ve tended to implement strict, imprecise, and overly broad policies that lead to false positives in risk identification and stymied production. These problems grow by orders of magnitude when you introduce third-party vendors, who may or may not be thinking about these questions at all.

Companies need a plan for mitigating risk — how to use data and develop AI products without falling into ethical pitfalls along the way. Just like other risk-management strategies, an operationalized approach to data and AI ethics must systematically and exhaustively identify ethical risks throughout the organization, from IT to HR to marketing to product and beyond.

What Not to Do

Putting the larger tech companies to the side, there are three standard approaches to data and AI ethical risk mitigation, none of which bear fruit.

First, there is the academic approach. Academics — and I speak from 15 years of experience as a former professor of philosophy — are fantastic at rigorous and systematic inquiry. Those academics who are ethicists (typically found in philosophy departments) are adept at spotting ethical problems, their sources, and how to think through them. But while academic ethicists might seem like a perfect match, given the need for systematic identification and mitigation of ethical risks, they unfortunately tend to ask different questions than businesses. For the most part, academics ask, “Should we do this? Would it be good for society overall? Does it conduce to human flourishing?” Businesses, on the other hand, tend to ask, “Given that we are going to do this, how can we do it without making ourselves vulnerable to ethical risks?”

The result is academic treatments that do not speak to the highly particular, concrete uses of data and AI. This translates to the absence of clear directives to the developers on the ground and the senior leaders who need to identify and choose among a set of risk mitigation strategies.

Next, is the “on-the-ground” approach. Within businesses those asking the questions are standardly enthusiastic engineers, data scientists, and product managers. They know to ask the business-relevant risk-related questions precisely because they are the ones making the products to achieve particular business goals. What they lack, however, is the kind of training that academics receive. As a result, they do not have the skill, knowledge, and experience to answer ethical questions systematically, exhaustively, and efficiently. They also lack a critical ingredient: institutional support.

Finally, there are companies (not to mention countries) rolling out…

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