The role of C-suite executives is clear. They need to have skin in the game and enable diverse and inclusive teams to keep algorithms in check.

Representation from leadership teams to the shop floor keeps retailers learning from the ever-evolving customer, empowering retailers to build authentic relationships with their shoppers. Deeper engagement with customers will keep leaders learning about who their customer is and will help to eliminate and address bias and discrimination in data inputs.

There is a difference between being “negligent” and not knowing there is a bug in the algorithm logic. I trust that most retailers use AI “making best efforts” to ensure that content is not racially biased and contact is not offensive or intrusive. Yet the possibility of a huge problem always looms over software. It’s the nature of the beast. In our litigious society, this could lead to lawsuits by ambulance chasers and overzealous citizens. Are the users of AI always exposed? If a horrible problem was found and fixed in a day, would retailers be liable for having crossed the line? I should think not.

This is a three-fold issue. The first is the people responsible for the implementing the algorithms in their companies do not understand the algorithm. The second is that the algorithm is written by people and people, no matter how hard they try not to, have biases that creep into the basic assumptions. The third is that the company can’t and doesn’t react until a serious issue surfaces.

How should retailers address it? Management should be keenly aware that it WILL happen. Fix it immediately. But the real challenge is that the fix may bring other unanticipated issues.

If C-suite executives and, even more importantly, their boards are hyper-focused on growth and profitability regardless of the means, then the AI bias problem is guaranteed to exist in their algorithms. Ensuring diversity in the AI team is a start. Reviewing key assumptions and a variety of training data is another step. But paramount is putting in place directives that guide the building of algorithms and actively eliminate embedded bias.

As an academically trained philosopher I find this a very complicated set of questions since the “right” answer depends on one’s much larger ethical view of the world. Let’s start with a pragmatic look. How many C-suite executives know enough about AI/ML, coding, or algorithm architecture to have an informed position of ethical algorithm development? Hint: Not too many. So the best that most C-suite denizens can do is set a general ethical position and pray the CIO/CTO can find a way to effectively embed it in their programming. As to what retailers can do today, AI systems can only do what they are told, so the first step to preventing discrimination is to screen for bias, bigotry, prejudice, and even ethical positions among programmers, software architects, and coders, and that is very dicey ground requiring input from legal, HR, and — if I can be so bold — somebody trained in ethics and meta-ethical analysis.

The introduction of artificial intelligence into customer interactions has tremendous benefits. At the same time, it is subject to the law of unintended consequences. There are always unintended consequences. In many cases the designers of algorithms do not have ill intentions; rather, the design and the data can lead artificial intelligence to unwittingly reinforce bias and prejudice.

The C-suite is not responsible for monitoring the algorithms, but are clearly responsible for ensuring that company values are transmitted and maintained throughout the organization. In many areas, executives receive reports that monitor compliance with values and HR standards; recommendation engines and artificial intelligence needs to be monitored and tracked just like any other area.

The C-level executive still bears responsibility for this problem – even if it is developed by machines. Execs need to put in place a system of checks and balances to ensure that discrimination is minimal and inherent biases are weeded out. Not an easy task, a team should be assigned responsibility for validating and testing the recommendation system. This would include data cleaning, metadata management, and initial training of their AI tools. As with most solutions, there are different levels of engagement and the C-suite will need to treat this just as they would preventative security measures in IT. The fallout could be significant. CIOs and CSOs (security or data privacy) should own this task as part of their responsibility.

This is an excellent article and important topic. I’d go one caution further, though. Any use of metrics in this process has a fundamental problem that for something to be a metric, it must reject important real world truth. While humans might be able to keep this in mind, algorithms can’t. Programming any metrics to algorithms risks shorting too far from the real world — hence the increase in application of prejudice we sometimes see in machine learning or AI output.