Data ethics is the new strategic imperative for leading corporations. In NewVantage Partners 2019 Executive Survey, more than half of executives — 55.7% — pointed to data ethics as a top business priority.
In a recent Harvard Business Review article, Tom Davenport and I discussed the “seven key types of Chief Data Officer (CDO) jobs”, noting that one of the emerging roles of the Chief Data Officer is as Data Ethicist. We noted that CDO as Data Ethicist is “growing in popularity, is [focused] on the ethics of data management, specifically on how it’s collected, safeguarded and shared and who controls it”. We confirmed that, “there is no doubt that consumers, regulators, and legislators are becoming more concerned about the misuse of data”.
I spoke recently with Dennis Hirsch, Professor of Law and Director of the Program on Data and Governance at The Ohio State University Moritz College of Law, and research fellow at The Risk Institute, about data ethics and corporate responsibility in the context of today’s emerging algorithmic economy. Hirsch’s research is focused on how data analytics can pose ethical risks, and how leading companies are responding. He is currently spearheading an interdisciplinary Ohio State research study on corporate data ethics that incorporates interviews and surveys with these firms.
Hirsch is interested in the question of what leading companies are doing in terms of data ethics management, as well as what are the motivating factors that are driving these corporate data ethics initiatives. One of the questions that he asks study participants is what constitutes ethical use of data analytics. While it is well understood that data analytics and AI offer the potential for tremendous economic and social benefits, there is less understanding of the potential risks of misuse and ways in which these risks can be mitigated.
The 4 Principle Risks of Data Misuse
Hirsch observes, “Much of the discussion around big data analytics focuses on the promising benefits, but ignores the equally significant risks”. Through his research, Hirsch has identified 4 common risks that consumers experience as a result of the misuse of data analytics:
2. Exploitation/Manipulation Risk – This leads to the potential risk of exploitation, based on inferred vulnerabilities that are used for manipulative purposes, including political persuasion. Hirsch cites the now infamous example of the Facebook-Cambridge Analytica scandal of 2018, where millions of Facebook user’s data was harvested without their consent. Cambridge Analytica used this data to infer these users’ personality types and target them with manipulative political ads that they would find hard to resist.
3. Algorithmic Bias – This has been much discussed in recent years, notably by Cathy O’Neil in her 2016 book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. The idea here is that there are built in biases within algorithms that can be traced to race, ethnicity, gender, or place of residence;
4. Opacity – This refers to the black-box nature of advanced machine learning, resulting in inherent procedural unfairness, with an inability to diagnose the algorithmic flaws. It is difficult to detect algorithmic bias when the logic of the algorithm is not accessible.
The 3 Motivating Factors for Ethical Data Use
In addition to asking what leading companies are doing to address data ethics and prevent data misuse, The Ohio State team’s research seeks to understand why companies should be motivated to develop strong data ethics principles, and what companies see as being at risk for the business if they do not develop robust data ethics principles. As Hirsch sees it, the 3 primary motivating factors are:
1. Reputational Risk – Good business is predicated on trust. This entails maintaining the trust of customers, business partners, regulators, and the general public. The costs of reputational damage are harmful, and sometimes fatal. Cambridge Analytica is no longer in operation. This underscores the necessity and importance of ethical data stewardship to ensure corporate responsibility and reputational integrity;
2. Employee Retention and Recruitment – Companies must compete for the best talent. Those companies that demonstrate social responsibility and high standards of ethical practices will be more attractive to a growing pool of socially conscious information economy talent;
3. Regulation – Firms do not want to be the subject of intense regulatory scrutiny based on their business practices. In light of GDPR, CCPA, and future anticipated legislation that will guide the ethical use of data analytics, those firms that establish best practices for stewarding their data will prevent unnecessary regulatory costs and adverse regulatory publicity.
Hirsch concludes, “Companies must do more to protect their customers and, in doing so, their own reputations. They need to go beyond what the law requires to ensure that their data analytics activities do not invade privacy, institutionalize bias or manipulate people”.
Laying a Foundation for the Data Ethics Officer
A decade ago, leading corporations made the decision to establish the Chief Data Office function, and appoint executives to the new Chief Data Officer (CDO) role. While only 12% of firms had appointed a CDO in 2012, by 2019 this percentage had grown to a peak of 67.9%.
Successfully establishing a new corporate function requires more than a commitment to “check the box” however. Today, only 27.9% of firms report that the CDO role is “successful and established”. This underscores the need for companies to go beyond well-intentioned lip service, to understand the complexities and cultural challenges that result from establishing new C-Suite roles.
Data ethics has emerged as a strategic corporate imperative. Its implementation requires the establishment of data ethics policies and practices. Whether this requires establishment of a new leadership function or whether this belongs under the mandate of the Chief Data Officer remains for each organization to determine.
What is clear is that those companies that proactively address their data risks will maximize corporate value and mitigate business risk. These firms will combine responsible use of data with the successful application of advanced analytics to realize positive economic and social outcomes, while avoiding the harms and risks resulting from the irresponsible and nefarious use of data. Ethical data use is good business.