Ibrahim Diallo was fired from his job by the company's AI system, as managers scrambled to figure out the problem and watched him be escorted from the building. In what many are considering a grave warning of the potential consequences of AI in the workplace, a man was fired by his company's AI system. Worst of all, his human managers "stood helpless" throughout the firing process. Nobody had any real answers at first, and no human was able to keep the man from being escorted out of the building by security. Ibrahim Diallo, a programmer for a company in Los Angeles, described his "automated" firing in a viral blog post, described the New Zealand Herald.

In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given data set is not a simple problem, because Markov networks rigorously represent Markov properties, and this rigor imposes complex constraints on the design of the networks. Our proposed model removes these constraints, acquiring important aspects from the information geometry. The proposed parameter- and structure-learning algorithms are simple to execute as they are based solely on local computation at each node. Experiments demonstrate that our algorithms work appropriately.