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Applications of Analytics

Decision Making

Category: Prescriptivec Analytics

According to David Parkes (2019), “Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals.” He argues, “Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view. Decision making requires leadership in advocating and explaining a path forward. Decision making requires dialogue.” The use of algorithmic systems to make decisions is not new. Drew (2016) describes “allowing data-led decisions by non-technical analysts/specialists,” for example, the government’s emergency planning committee “now has a dynamic, interactive visualization tool that allows non-specialists to help respond to emergencies.” Parkes and Vohra (2019) point to its use in cases involving recidivism prediction, credit scoring, applicant screening, setting bail and sentencing, lending and insurance, and the allocation of public services. Banks and financial institutions “rely heavily on quantitative analysis and models in most aspects of financial decision making. They routinely use models for a broad range of activities, including underwriting credits; valuing exposures, instruments, and positions; measuring risk; managing and safeguarding client assets; determining capital and reserve adequacy; and many other activities” (FRS, 2011:1).

What’s new are scale, ubiquity and accountability. AI enables a human decision-maker to make many more decisions and to use the same process for multiple types of decisions, but raises questions about who is ultimately accountable for a decision that has been made and how new information could be added to better inform the decision.

Examples and Articles

What AI-Driven Decision Making Looks Like
"AI can be trained to find segments in the population that best explain variance at fine-grain levels even if they are unintuitive to our human perceptions. AI has no problem dealing with thousands or even millions of groupings. And AI is more than comfortable working with nonlinear relationships, be they exponential, power laws, geometric series, binomial distributions, or otherwise." HBR. Eric Colson, July 08, 2019. Direct Link

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