 Machines make decisions that have moral impacts. Wendell Wallach and Colin Allen tell an anecdote in their book “Moral Machines” (2008). One of the authors left on a vacation and when he arrived overseas his credit card stopped working, perplexed, he called the bank and learned that an automatic anti-theft program had decided that there was a high probability that the charges he was trying to make were from someone stealing his card and that in order to protect him the machine had denied his credit card transactions. Here we have a situation where a piece of information technology was making decisions about the probability of nefarious activity happening that resulted in a small amount of harm to the person that it was trying to help. Increasingly, machines make important life changing financial decisions about people without much oversight from human agents. Whether or not you will be given a credit card, mortgage loan, the price you will have to pay for insurance, etc., is very often determined by a machine. For instance if you apply for a credit card, the machine will look for certain data points, like your salary, your credit record, the economic condition of the area you reside in, etc., and then calculate the probability that you will default on your credit card. That probability will either pass a threshold of acceptance or not and determine whether or not you are given the card. The machine can typically learn to make better judgments given the results of earlier decisions it has made. This kind of machine learning and prediction is based on complex logic and mathematics (see for example, Russell and Norvig 2010), this complexity may result in slightly humorous examples of mistaken predictions as told in the anecdote above, or it might be more eventful. For example, the program may interpret the data regarding the identity of one’s friends and acquaintances, his or her recent purchases, and other readily available social data, which might result in the mistaken classification of that person as a potential terrorist, thus altering that person’s life in a powerfully negative way (Sullins 2010). It all depends on the design of the learning and prediction algorithm, something that is typically kept secret, so that it is hard to justify the veracity of the prediction.