Cyborganisation: Machines and Humans Make Optimal Decisions Together
Business rules evolved from expert systems, a concept whereby expertise could be encoded and leveraged across an organisation. In the same way, artificial intelligence is now being used to replace human decision-makers. In both cases, the idea is to emulate and replace the decision-maker with a machine. But this approach could be considered as misguided for two reasons. Firstly, it is not possible to emulate a human with complete accuracy, and secondly, human decision-makers are fallible. Another approach is to consider how business rules and human experts can work together to maximise the expected profit of an organisation, creating a cyborganisation. There are several elements to this problem—the need to quantify the impact of different rules on the performance of the organisation, the accuracy of the human decision-maker on a case-by-case basis, and determine whether the machine and/or the human decision-maker makes the final decision. This paper considers a real example from a well-understood problem that of loan approval, but from the perspective of machine augmenting, rather than replacing, the human decision-maker. The results suggest that there are potential savings and increases in profit from this approach.
KeywordsDecision support Business process Business rules Human experts Optimisation
This research did not receive any specific grant from funding agencies in the public, commercial or no-for-profit sectors.
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