McKinsey reported that total potential “annual value of AI and analytics across industries to be be worth $9.5 to $15.4 trillion” in its report entitled “The executive’s AI playbook” which includes 3 different perspectives:

Value & Assess – Size the opportunity and determine data needs

Execute – Learn best practices to realize value

Beware – Know the warning signed of AI program failure

The 3rd perspective to Beware includes 10 “warning signs of AI program failure” which are part of the May 2018 report “Ten red flags signaling your analytics program will fail” which includes #10 that “No one is hyperfocused on identifying potential ethical, social, and regulatory implications of analytics initiatives”:

It is important to be able to anticipate how digital use cases will acquire and consume data and to understand whether there are any compromises to the regulatory requirements or any ethical issues.

One large industrial manufacturer ran afoul of regulators when it developed an algorithm to predict absenteeism. The company meant well; it sought to understand the correlation between job conditions and absenteeism so it could rethink the work processes that were apt to lead to injuries or illnesses. Unfortunately, the algorithms were able to cluster employees based on their ethnicity, region, and gender, even though such data fields were switched off, and it flagged correlations between race and absenteeism.

Luckily, the company was able to pinpoint and preempt the problem before it affected employee relations and led to a significant regulatory fine. The takeaway: working with data, particularly personnel data, introduces a host of risks from algorithmic bias. Significant supervision, risk management, and mitigation efforts are required to apply the appropriate human judgment to the analytics realm.

First response: As part of a well-run broader risk-management program, the CDO should take the lead, working with the CHRO and the company’s business-ethics experts and legal counsel to set up resiliency testing services that can quickly expose and interpret the secondary effects of the company’s analytics programs. Translators will also be crucial to this effort.

Here are all Ten Red Flags:

  1. The executive team doesn’t have a clear vision for its advanced-analytics programs
  2. No one has determined the value that the initial use cases can deliver in the first year
  3. There’s no analytics strategy beyond a few use cases
  4. Analytics roles—present and future—are poorly defined
  5. The organization lacks analytics translators
  6. Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure
  7. Costly data-cleansing efforts are started en masse
  8. Analytics platforms aren’t built to purpose
  9. Nobody knows the quantitative impact that analytics is providing
  10. No one is hyperfocused on identifying potential ethical, social, and regulatory implications of analytics initiatives

Time will tell about the real value of AI!