What is algorithmic aversion?
A working definition from MIT Sloan
algorithmic aversion (noun)
The conscious or unconscious reluctance of human decision-makers to accept algorithmic recommendations.
Algorithms can help us make better decisions. But to follow their advice, humans must trust them. The way humans view algorithmic recommendations varies based on what they know about how the artificial intelligence model works and how it was created, according to research co-authored by MIT Sloan professor Kate Kellogg. Prior research assumed people are more likely to trust interpretable AI models, in which they can see how the models make their recommendations. But Kellogg found that this isn’t always true.
In an experiment at Tapestry, a New York-based house of lifestyle brands, product allocators were charged with maximizing sales. That involved placing the right number of items in the right stores at the right time. Product allocators received recommendations from either an interpretable algorithm or an uninterpretable machine learning algorithm.
Overall, the human allocators experienced less algorithmic aversion with the uninterpretable model than with the one they could more easily understand. Why? The researchers found that being able to troubleshoot the interpretable algorithm by reviewing its inner workings led allocators to sometimes overrule the recommendations. Meanwhile, knowing peers had developed and tested the uninterpretable algorithm made them more likely to accept its recommendations.
Why employees are more likely to second-guess interpretable algorithms
Working Definitions: Artificial Intelligence
MIT Sloan's Working Definitions explore the words and phrases behind emerging management ideas.
Leading the AI-Driven Organization
In person at MIT Sloan
Register Now
What 3 new MIT Sloan professors have learned about AI
New faculty members are thinking about how AI can help decision-making, the value of adversarial AI agents, and the skills workers need in the AI era.
Generative AI as a tool for market research
Market research is essentially a prediction problem. MIT Sloan’s John Horton says organizations should consider using generative AI to simulate the effects of their decisions before they make them.
10 levers for shaping generative AI that improves worker performance
A report from the MIT Working Group on Generative AI & the Work of the Future identifies three principles and seven outcomes that ensure that jobs are better, not just faster, with AI.
AI boosts productivity — but does that translate to final outputs?
A new study found that developers using AI tools can write much more code than those working without AI, but they don’t release as much new software.
Judgment-free AI appeals to embarrassed clients
Professional service advisers are seen as more capable than AI — but that advantage fades when clients feel ashamed. An MIT Sloan study shows when people prefer AI’s nonjudgmental assistance.
‘No One Works Here’ urges firms to tackle human bottlenecks with AI
In his new book, MIT Sloan School of Management’s Paul Cheek argues that companies lose competitive ground when they keep humans in the loop on decisions that AI can make more efficiently.
4 ways to navigate entrepreneurship in the age of AI
MIT Sloan’s Bill Aulet and Jenny Larios Berlin explore how founders can combine artificial intelligence with the principles of Disciplined Entrepreneurship to move from concept to market more quickly.
An MIT expert on which companies will succeed in the AI era
MIT Sloan’s Andrew McAfee said the future of tech-driven companies is young firms on the West Coast — and that Europe will continue to fall behind.
AI ethics and governance: Where will you draw the line?
Enterprises must address complex ethical issues surrounding AI or risk exposure to myriad financial, legal, and reputational risks. Here’s a framework to protect your organization.
Can AI’s climate benefits outweigh its costs?
The rising energy demands of AI and data centers threaten corporate net zero commitments and worsen climate change. But can AI also help develop climate solutions? MIT Sloan researchers investigate.