Why the ethics of big data matters for business leaders

Depending on who you ask, the phrase “big data” evokes a sense of opportunity, fear, or confusion.

It’s clear that data collection is an invaluable tool for learning about customer preferences and developing groundbreaking products. But businesses must also balance these advantages with formidable challenges: maintaining user trust and upholding company values.

A survey from KPMG revealed that 91% of respondents believe corporations should take the lead on establishing data responsibility measures. That means it’s not enough for businesses to leverage data however they see fit, then ask for forgiveness later on. Industry leaders are now in the position to pave the way for the future of ethical data use. 

So what are the most important aspects of the debate surrounding ethics in big data? 

Here’s what you need to know and how innovative organizations can keep growing while using data responsibly.

Imagine if your business could increase its hiring volume by 40% over the course of a single year. Amid today’s ultra-competitive talent market, many growing companies might jump at the chance to build up their teams without spending huge sums of money or losing time.

With the help of big data-powered hiring systems featuring artificial intelligence (AI), this is already a reality. AI tools analyze huge swaths of candidate data and can cut down the time it takes to pre-screen, comb through CVs, interview, and onboard new employees. 

This advancement, however, has also been linked to a serious issue: increased bias. 

Without proper oversight and continuously-updated data sets, new technologies may perpetuate the biases of the past. When Amazon tested an AI-powered hiring tool, they found it downgraded the applications of women. Meanwhile, facial recognition software from Microsoft and Face++ discriminated against candidates of color by assigning them negative emotions in behavioral assessments. 

Though big data-powered insights lift convenience and operational efficiency, they don’t always work in sync with a company’s mission—or with the progression of societal norms. That’s why even the most cutting-edge AI interfaces aren’t steering Fortune 500 boardrooms just yet. Delphi, a technology designed specifically to make moral judgments, comes with a caveat to put things in perspective: “Model outputs should not be used for advice for humans, and could be potentially offensive, problematic or harmful.”

In the fast-moving world of data science, legislation often lags behind the day-to-day realities of how data is really being used. 

2019 Pew polling found that nearly 80% of respondents were at least somewhat concerned about how companies were using the data collected about them. Still, the US doesn’t have a standardized national law to regulate how companies can collect, store, and share personal data. And while Europe has made strides to reign in how personal information is used online through the General Data Protection Regulation (GDPR), good data governance starts internally.

If a large-scale breach compromises your users’ data, simply stating, “We adhered to the current rules” might not be enough to maintain a good reputation. Instead, a proactive approach offers transparency and keeps you from playing catch-up

• Creating a data-ethics board made up of cross-organizational stakeholders

• Holding regular discussions about ethical scenarios related to big data. Gartner recommends using different mental modes—such as universalist, consequentialist, and care ethics—in order to consider multiple sides of complex data issues

• Using adaptive governance to determine policies based on a given context rather than one-size-fits-all solutions. A helpful starting point comes from reviewing company values to assess how they fit into shifting data concerns.