The Houston Astros And The Ethical Use Of Data And Analytics

I’ve been thinking a lot about the Houston Astros. They won the 2017 World Series with an impressive performance, and were American League champions in 2019 (but lost to the Nationals in the World Series). Their General Manager, Jeff Luhnow, was a former McKinsey consultant who—as a McKinsey Quarterly article describes—made extensive use of advanced analytics to improve the team’s performance. However, we have learned over the past several months that the Astros’ appetite for data crossed the ethical line. Specifically, the team’s staff and players developed an illegal approach to stealing pitch signs from catchers that helped hitters anticipate what pitch was coming. Luhnow and manager A.J. Hinch were fired by the Astros in January and suspended from all major league teams for a year.

The Astros probably weren’t the only team that stole pitch signals; there is considerable speculation that the Boston Red Sox, managed by former Houston bench coach Alex Cora in 2018 (when they won the World Series) and 2019, did something similar, and they are currently under investigation. The Red Sox, who are my home team, were also accused by their archenemy New York Yankees of using Apple Watches to transmit stolen pitch signs in 2017, which would have been an illegal use of technology. The Red Sox then accused the Yankees of using their cable network cameras to steal pitch signs. Alex Cora was implicated in the Houston sign stealing and was fired by the Red Sox in January. The Sox, like the Astros, are aggressive users of data and analytics to improve team performance.

There are other examples of borderline and/or overt inappropriate collection or use of data in both sports and business. The New England Patriots, another successful and data-driven team I follow, were accused of illegal filming of opponents—a form of data collection—in both 2007 and 2019.

In business, some industries use data and analytics to market their products to customers who can’t afford them. This isn’t illegal, but many consider it somewhat unethical. Two offenders are the subprime credit card industry and the gaming industry.  Capital One, for example, is great at analytics but allows some customers to take on more debt than they can afford, this New Republic article and this Salon article allege. Capital One isn’t just a subprime lender, of course. Banks that restrict themselves to that market, such as the late Providian, are often both analytical and somewhat predatory.

In the gaming industry, there has long been criticism that casinos encourage irresponsible gaming behaviors, as this Wall Street Journal article describes. That may be facilitated by sophisticated analytics on customer behavior, which most large gaming firms have. On the other hand, under CEO Gary Loveman’s tenure, Caesars Entertainment was not only an analytics powerhouse, they adopted the industry’s strongest focus on responsible gaming.

Perhaps the most analytical firms today are the online businesses like Google and Facebook, who know about virtually everything we do online.  Shoshana Zuboff reports in her book The Age of Surveillance Capitalism the less-than-surprising information that these online firms are gathering information on what we search for and like, and using it to make analytical predictions about what ads we might respond to. Such predictions have been going on for at least fifty years or so by firms that practiced database marketing, and their predictions are still not very accurate. However, these firms do sometimes cross the ethical line in data collection and analysis, such as when Google’s Street View data collection program gathered wifi activity, or when Facebook allowed Cambridge Analytica to gather data under false pretenses about 50 million users and their friends in order to aid the 2016 Trump campaign. And Zuboff is certainly correct that these online businesses, as well as other types of data brokers, need more regulation.

Pursuing Data and Analytics Too Far

So here are the key questions: does an orientation to data and analytics make it likely that ethical violations in the collection and use of data will be committed? Does it lead organizations to gather and use data “because we can,” even when they shouldn’t? Is there something intrinsic in a data-driven culture that leads firms in an ethically dicey direction?

I don’t think the answers to these questions are clear at all. “Maybe” is perhaps the best single response. The examples I’ve described certainly should raise concern about the relationship between being data-driven and crossing ethical lines in the use of data and analytics, but they are hardly perfectly correlated. In fact, there are plenty of examples to the contrary. Many individual companies exist that rely heavily on analytics and data, but have a clean record in terms of ethical infractions. In fact, I’d argue that the majority of such firms have had no ethical problems. One could also argue that if you want your products or services not to cause serious problems for customers, you can use data and analytics to confirm that.

In addition, purely human decisions that aren’t based on data can create plenty of ethical problems too. Maybe not so much in baseball, where it’s legal for humans to capture pitch sign data without the aid of technology. Then again, pitchers have long made decisions to throw spitballs, which are illegal. The New England Patriots appear to have decided to deflate footballs in 2014, and no data collection was involved.

Perhaps the real issue is that companies and teams that seek a competitive edge have now gravitated to data and analytics as a tool. If they are overly aggressive with their approach to winning, they may cross the line to gather or use data in a questionable or unethical way.

How to Avoid Crossing the Line

Given that data collection and use can sometimes cross an ethical line, it’s important to have strategies and structures in place to prevent that from happening. The best way to do that is to establish some sort of “data ethics board” to discuss the ethical implications of new data and analytics-related initiatives. Such a body will become increasingly necessary as organizations embrace AI, which is more likely to face issues of lack of transparency and data/algorithmic bias than traditional analytics.

Committed organizations may also want to establish permanent positions to advocate for and adjudicate about ethical data usage. I’ve written about what Microsoft’s AI ethicist does, and a few other companies have similar roles.

Companies may also want to encourage governmental bodies to regulate them. Responsible regulation could prevent a consumer backlash, as well as the emergence of poorly-designed regulation. Google, for example, is now advocating “sensible” regulation of AI, as are companies in enterprise data and analytics like Salesforce.com. My guess is that we will see many more companies begin to either advocate for more data-oriented regulation, or to, at least, not object to it.

I don’t think we can proclaim with certainty that the ardent pursuit of data and analytics leads to ethical problems. But there is evidence emerging that there may be a connection and a problem. Leaders of companies and organizations that adopt this competitive focus should be particularly careful that they don’t fall into the murky ethical lapses swamp. It could be hard to climb out.