AI Ethics Challenge: Understanding Passive versus Proactive Ethics

Several of my friends have challenged me to get involved in the AI ethics discussion. I certainly do not have any special ethics training. But then again, maybe I do. I’ve been going to church most Sundays (and not just on Christmas Eve) since I was a kid and have been taught a multitude of “ethics” lessons from the Bible. So, respectively, let me take my best shot at sharing my thoughts about the critical importance of the AI Ethics topic.

Ethics is defined as the moral principles that govern a person’s behavior or actions, where moral principlesare the principles of “right and wrong” that are generally accepted by an individual or a social group. “Right or wrong” behaviors …not exactly something that is easily codified in a simple mathematical equation.  And this is what makes the AI ethics discussion so challenging and so important.

To understand the AI ethics quandary, one must first understand how an AI model makes decisions:

1.     The AI model relies upon the creation of “AI rational agents” that interact with the environment to learn, where learning is guided by the definition of the rewards and penalties associated with actions.

2.     The rewards and penalties against which the “AI rational agents” seek to make the “right” decisions are framed by the definition of value as represented in the AI Utility Function.

3.     In order to create an “AI rational agent” that makes the “right” decision, the AI Utility Function must be comprised of a holistic definition of “value” including financial/economic, operational, customer, society, environmental and spiritual.

Bottom-line: the “AI rational agent” determines “right and wrong” based upon the definition of value as articulated in the AI Utility Function (see Figure 1). 

It isn’t the AI models that scare me. I’m not afraid that the AI models won’t work as designed.  My experience-to-date is that AI models work great. But the thing is AI models will strive to optimize exactly what they have been programmed to optimize by humans via the AI Utility Function. 

And that’s where we should focus the AI ethics conversation because humans tend to make poor decisions.Just visit Las Vegas if you doubt that statement (or read “Data Analytics and Human Heuristics: How to Avoid Making Poor Decisions”). The effort by humans to define the rules against which actions will be measured sometimes results in unintended consequences.

Shortcutting the process to define the measures against which to monitor any complicated business initiative is naïve…and ultimately dangerous. The article “10 Fascinating Examples of Unintended Consequences” details actions “believed to be good” that ultimately led to disastrous outcomes, including:

See “Unintended Consequences of the Wrong Measures” for more insights into the challenges of properly defining the criteria against which progress and success will be measured. And if you want to know just had bad it can get, check out “Real-World Data Science Challenge: When Is ‘Good Enough’ Actually ‘Good Enough’” to understand the costs of false positives and false negatives.