Giving credit where it’s due

Bosses want you to use AI. Then they credit the AI

THE RISE OF AI agents in the workplace has started posing a new question: when work goes well, who — or what — should get the credit?

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A story in Business Insider plumbed this quandary, and found that in many corners of the professional world this is a growing problem. “White-collar workers are caught in a nasty pickle,” wrote Shubham Agarwal. “Many employees have begun hiding their AI usage and wonder how much credit, if any, they should give it for their efforts. To credit or not to credit AI can feel like as vital a question as to be or not to be.”

There’s hardly one way to use AI, and work styles using AI tools differ from person to person, yet research from Christoph Riedl cited by Business Insider points out there are trends in how work is evaluated when humans work alongside AI.

“Managers consistently devalued workers’ contributions to projects when workers revealed AI had assisted them,” they said. It seems many managers assumed, often incorrectly, that human input was minimal.

Some workers view the question differently — you don’t credit Excel for good accounting, for instance, or credit Microsoft Word for writing a good paragraph. Others still wonder if that’s simplifying it a bit too much.

“The issue becomes whether the apparent intellectual contribution can still be attributed to the human author or agency was largely outsourced to the machine,” said management professor Oliver Schilke.

Others still think it’s the wrong question entirely, and that what matters isn’t how the work was produced but “whether the person responsible for it can defend it, improve it and be held accountable when it fails.”

Some experts in the field of human-tech interaction believe that marketing AI agents as personified ‘employees’ was always a mistake, and created ambiguity about credit and agency. “It inverts our sense of who’s in charge. When an AI tool was framed as an employee, participants in the study saw themselves as less responsible for its output,” wrote James O’Donnell in the MIT Technology Review.

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Instead, noted MIT economist Daron Acemoglu, “they should be optimized so that they can improve human capabilities, which is not what they have been at the moment.”

Understanding this dynamic will go a long way to unravel a knot that exists around trust in the world of AI and work, one cited in Canada’s national AI strategy and one likely to be a persistent theme over the next couple years: how do you build trust alongside exploiting the technology?

It is, as yet, an open question, and one that Alessia Artuffo, CEO of education tech firm Docebo, thinks can be a harmful one.

“If employees are producing more output but feeling less ownership of the work, that’s not a win,” he told Business Insider. “That’s capability regression dressed up as efficiency.” Kieran Delamont