Online field experiment · 2016
How Much Information? Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec RF
What it found
When an algorithm's result was worse than people expected, explaining how it worked kept their trust, but explaining too much eroded it again. People whose expectations were met were not affected.
Where the evidence stops
Peer grading in online courses; one setting.
How it shapes Pursue
Pursue
Pursue explains itself in a sentence, not a page.
A design input, not evidence that Pursue works.
Cited in
- 01Should I trust an AI's advice about how to spend my day?Trust it as far as it has earned, and no further. Across six experiments, people followed advice more readily when they believed it came from an algorithm rather than a person (Logg et al., 2019), yet watching an automated aid make mistakes made them distrust even a reliable one (Dzindolet et al., 2003).Trust and uncertainty
- 02How should an AI say it isn't sure?Plainly, and without inventing a confidence number. Modern neural networks are often over-confident (Guo et al., 2017), and a system allowed to decline uncertain cases makes far fewer errors on the ones it answers (Geifman and El-Yaniv, 2017); a 2019 review led from the University of Cambridge found that admitting uncertainty does not necessarily cost trust.Trust and uncertainty