Modern AI is great at optimizing--finding the shortest route, the perfect pricing sweet spot, or the best distribution of a company's resources. But it's also blind to a lot of the context that a human making similar decisions would be cognizant of, particularly when it comes to ethics. As an example, most people realize that while jacking the price of a medicine up during a health crisis would boost profits, it would also be morally indefensible. But AI has no sense of ethics, so if put in charge of pricing strategy this might seem like a promising approach. The key is to focus on the strategies likely to provide the biggest returns, as these are the ones the optimization process is likely to settle on. The authors recommend ranking strategies by their returns and then manually inspecting the highest-ranked ones to determine if they're ethical or not.

We all know the old saying: garbage in, garbage out. This has been especially true with early trails of artificial intelligence. Humans building the algorithms are inherently flawed and have deeply ingrained biases in their thought processes, and this translates to bias in the output of many artificial intelligence algorithms. We've seen one algorithm learn that male job candidates are preferred to female job candidates and automatically kick out not only the resumes of women, but also those that listed women as references. Building ethical AI is tricky, but it can, and must, be done.