The Future of Work by Harvard Business Review

Author:Harvard Business Review
Language: eng
Format: epub
Publisher: Harvard Business Review Press
Published: 2021-08-17T00:00:00+00:00

Phase 4: The Teammate

Edwin Hutchins, a cognitive anthropologist, developed what is known as the theory of distributed cognition. It is based on his study of ship navigation, which, he showed, involved a combination of sailors, charts, rulers, compasses, and a plotting tool. The theory broadly relates to the concept of extended mind, which posits that cognitive processing, and associated mental acts such as belief and intention, are not necessarily limited to the brain, or even the body. External tools and instruments can, under the right conditions, play a role in cognitive processing and create what is known as a coupled system.

In line with this thinking, in the final phase of the AI implementation journey (which to our knowledge no organization has yet adopted) companies would develop a coupled network of humans and machines in which both contribute expertise. We believe that as AI improves through its interactions with individual users, analyzing and even modeling expert users by drawing on data about their past decisions and behaviors, a community of experts (humans and machines) will naturally emerge in organizations that have fully integrated AI coachbots. For example, a purchasing manager who—with one click at the moment of decision—could see what price someone else would give could benefit from a customized collective of experts.

Although the technology to create this kind of collective intelligence now exists, this phase is fraught with challenges. For example, any such integration of AI must avoid building in old or new biases and must respect human privacy concerns so that people can trust the AI as much as they would a human partner. That in itself is a pretty big challenge, given the volume of research demonstrating how hard it is to build trust among humans.

The best approaches to building trust in the workplace rely on the relationship between trust and understanding—a subject of study by David Danks and colleagues at Carnegie Mellon. According to this model, I trust someone because I understand that person’s values, desires, and intentions, and they demonstrate that they have my best interests at heart. Although understanding has historically been a basis for building trust in human relationships, it is potentially well suited to cultivating human-AI partnerships as well, because employees’ fear of artificial intelligence is usually grounded in a lack of understanding of how AI works.

In building understanding, a particular challenge is defining what “explanation” means—let alone “good explanation.” This challenge is the focus of a lot of research. For example, one of us (Evgeniou) is working to open up machine-learning “black boxes” by means of so-called counterfactual explanations. A counterfactual explanation illuminates a particular decision of an AI system (for example, to approve credit for a given transaction) by identifying a short list of transaction characteristics that drove the decision one way or another. Had any of the characteristics been different (or counter to the fact), the system would have made a different decision (credit would have been denied).

Evgeniou is also exploring what people perceive as good explanations for AI decisions.

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