This man was fired by a computer. Real AI could have saved him


Ibrahim Diallo was allegedly fired by a machine. Recent news reports relayed the escalating frustration he felt as his security pass stopped working, his computer system login was disabled, and finally he was frogmarched from the building by security personnel. His managers were unable to offer an explanation and powerless to overrule the system.

Some might think this was a taste of things to come as artificial intelligence is given more power over our lives. Personally, I drew the opposite conclusion. Diallo was sacked because a previous manager hadn’t renewed his contract on the new computer system and various automated systems then clicked into action. The problems were not caused by AI, but by its absence.

The systems displayed no knowledge-based intelligence, meaning they didn’t have a model designed to encapsulate knowledge (such as human resources expertise) in the form of rules, text and logical links. Equally, the systems showed no computational intelligence – the ability to learn from datasets – such as recognizing the factors that might lead to dismissal. In fact, it seems that Diallo was fired as a result of an old-fashioned and poorly designed system triggered by a human error. AI is certainly not to blame – and it may be the solution.

The conclusion I would draw from this experience is that some human resources functions are ripe for automation by AI, especially as, in this case, dumb automation has shown itself to be so inflexible and ineffective. Most large organizations will have a personnel handbook that can be coded up as an automated, expert system with explicit rules and models. Many companies have created such systems in a range of domains that involve specialist knowledge, not just in human resources.

But a more practical AI system could use a mix of techniques to make it smarter. The way the rules should be applied to the nuances of real situations might be learned from the company’s HR records, in the same way, common law legal systems like England’s use precedents set by previous cases. The system could revise its reasoning as more evidence became available in any given case using what’s known as “Bayesian updating”. An AI concept called “fuzzy logic” could interpret situations that aren’t black and white, applying evidence and conclusions in varying degrees to avoid the kind of stark decision-making that led to Diallo’s dismissal.