How AI Can Help Companies Set Prices More Ethically

3 min read

Using AI and data-driven tools, companies can change the price of a good or service based on who is buying, when they’re shopping, and myriad other factors. This power raises a question: Should ethics and societal values factor into these prices — and how? Companies should consider what they’re selling, who they’re selling to, and how they’re selling their products, and consider the range of possible impacts. Then, they should 1) get creative about reducing negative externalities, 2) reach out to stakeholders, and 3) build proactive filters into their pricing deliberations.

More than ever, companies are able to tailor prices across people, places, and time. They do this to maximize profit, and sometimes simply to survive. We’re in a new era of supercharged price discrimination, made possible by two major scientific and technological trends. First, AI algorithms — often trained on highly detailed behavioral data — enable organizations to infer what people are willing to pay with unprecedented precision. Second, recent developments in behavioral science — often invoked with the tagline “nudge” — provide organizations greater ability to influence their customers’ behaviors.

Not long ago, such tactics might have been taken in stride. But no longer. Today’s overlapping crises and changing attitudes highlight a truth that’s often been hidden in plain sight: Such supercharged pricing strategies can cause real harm to individuals, organizations, and societies. When companies deploy such strategies without considering their potential societal impacts, they risk harming people, inviting customer outcry and public outrage, and even reduced prospects for long-term survival.

Given all of this, organizations must consider how to use their supercharged pricing capabilities in ways that reflect important societal values and avoid causing harm and amplifying inequities. Going further, farsighted organizations would do well to seek profitable data-driven pricing strategies that not only avoid harm but also help advance societal wellbeing.

To do this, companies need to embed societal considerations and ethical deliberations in their pricing calculus. The fields of medicine, data science, and AI have undergone similar evolutions, embracing the core ethical concepts of avoiding harm, promoting wellbeing and fairness, and not manipulating people’s behaviors and decisions. In a similar spirit, we offer a framework comprising three easy-to-ask, easy-to-answer, and easy-to-apply questions that together reduce the chances that your pricing strategy will have negative social impacts. Think of each of these three questions as layers of a “filter” that can reduce potential harms to your customers, your company, and society.

Think about your products and services. Are they essentials, such as food, shelter, medicine, transportation, and internet access? During the pandemic, high prices blocked access to personal safety equipment like N95 masks and hand sanitizer, as well as to must-have products such as toilet paper. In each case, not being able to get these items exposed people to risk or harm.

Of course this question remains relevant beyond times of crisis. High pricing by pharma companies has historically blocked access to drugs for chronically ill patients — harming people they could help and making these companies targets of social outrage. Similarly, AI algorithms enable surge pricing for taxi rides. Surge pricing can be a useful tool – for example it can both increase the availability of transportation and enable drivers to earn more. But context is important. For example, significant price increases during emergencies such as severe snowstorms or hurricanes can prevent vulnerable consumers from moving to safety.

Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.