Climate change is humanity’s greatest challenge right now.

Recognizing this fact, many large investors have started to pull their funds away from fossil fuel companies.

Instead of joining the movement and making efforts to save the planet, Texas has enacted a law seeking to punish financial firms that don’t invest in fossil fuels.

In poker, we call that move a raise. You don’t need a better hand than your opponent to raise. Sometimes, you are simply trying to get them out of the hand.

The danger with a raise is that you might get called by a stronger hand, or even face a re-raise. Texas Representative Phil King said “if you boycott Texas energy (the bet), then Texas will boycott you (the raise).” OK. But what if people start boycotting not just Texas energy but Texas everything (re-raise)?

Personally, I would go all-in with saving the planet.

Good poker players fold their starting hand three to four times more often than bad players. They know to wait for better cards in a better position. While they wait, they observe and accumulate information about the other players.

Likewise, good ethics officers rarely provide answers without asking many questions of their own. They resist the temptation of appearing smart based on incomplete information. They deploy modesty and seek to understand all facets of a problem. They consider the facts and the people involved before offering advice.

Providing ethical advice is betting that our prediction of the future will manifest. Let’s make sure we are placing good bets.

For Part 1 of this series, click here.

I recently joined a small group of E&C professionals who decided to complete the free online course on AI Ethics for Business, offered by Seattle University.

We agreed to complete a module or two every week and to share our insights. Here are some of my insights from Module 2:

• In these early days of AI, it might be best to create technology-specific regulations rather than impose a general regulatory framework. Let’s think specifically about the regulations required for self-driving cars, face recognition and nurse robots, and then extract general concepts.
• Most data-collection efforts start with good intentions. A magazine needs your home address for delivery, and later they ask for your age and your income to attract the right advertisers to place adds in the magazine. But then, the advertisers offer to buy that information from the magazine to send direct-mail marketing to subscribers, without their consent. What’s a magazine to do? How transparent must they be with their subscribers? How much agency should the subscriber have? Similar scenarios (and questions) are now being played with data collected by our phones about where we’ve been, by our watches about our resting heart-rate, by our cars about how fast we are driving, and this data is being being fed to AI engines.
• Machines learn to make decisions based on datasets that humans provide. These datasets almost always contain biases. Let’s say I want a machine to learn how to identify a good poker player. I will feed this machine with all the data that we have about the players who reached the final table of the Main Event at the World Series of Poker since its founding in 1970. The machine will see that only one woman ever made it, back in 1995. What will the machine learn from this?
• The concerns that humans have about AI and machine learning revolve around agency: we want to have control over the types of decisions machines make; we want to understand how those decisions are made; and we want to be able to override those decisions.

A side-note: I find the end-of module quizzes very poor. If all you remembered of these modules was the information included in the quizzes, you would have a dismal understanding of AI ethics.