Check your odds

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.

AI ethics for business – Part 2

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.

Put the odds in your favor

People tease me about my interest in poker. Is an ethics professional allowed to gamble?

My favorite poker game is Texas Hold’em. I like it because it’s mostly a game of skill, with the element of chance greatly reduced.

When you play enough poker, you start thinking about odds in other areas of your life, like when I see an employee misbehave at work.  Let me explain and give you a simple example.

You are playing Texas Hold’em and your hole cards are of the same suit, say hearts. The flop reveals one more heart, and the turn is a heart as well. All you need is one more heart on the river to make your flush, which we’ll assume would be the winning hand.

What are your odds of getting that heart? Well, you know that there are 9 hearts left somewhere in the 46 cards that are still face down (either in the deck or face down in front of the other players). So you have about a 1 in 5 chance that the next card will be a heart.

Should you bet? Well, that depends on the size of the bet and the size of the pot. If you must bet $1 and the pot has $10 in it, it’s a good bet. Why? Because if you played that hand 5 times, you would lose $1 four times and win $10 one time. Over the long run, this is a winning move.

Conversely, if you had to bet $10 into a $40 pot, it would be a bad bet.

So what does this have to do with bad behavior at work?

Well, take the employee who steals $50 from the petty cash box at the reception desk. In some way, he is betting his $50,000 salary that he won’t get caught stealing $50. What he is essentially doing is betting $50,000 into a $50 pot. As we’ve seen, this is a good bet only if his odds of getting caught are lower than 1000/1. Given the financial controls around petty cash, the workplace cameras, other employees walking by, it’s clearly not a good bet.

We’ve all heard of the small-time thieves making this common mistake, impulsively robbing a convenience store with a sign on the door warning them of camera surveillance and that the cash register never contains more than $200. On the other hand, professional criminals make much better bets, going for big rewards after careful planning.

We see employees (and politicians, and celebrities, etc.) make these bad bets all the time. Harassment, discrimination, conflicts of interest – you name it. They bet their reputation and future earnings on small pots when the odds are against them.

Don’t get me wrong. I’m not suggesting that employees should carefully plan bigger scams. What I’m saying is that an employee’s odds of getting away with wrongdoing in the modern corporation are very low. Too low to bet their future earnings and reputation.

We all have so much good to give, why not stack the odds in our favor?