It turns out that people actually have different expectations of how difficult certain tasks are for algorithmic systems vs. how difficult those same tasks are for humans. This difference in expectations, combined with the fact that people inferred effort from the response time it took the systems (humans) to make a prediction, explains our findings. For algorithms, slower responses were incongruent with expectations; the prediction task was presumably easy so slower speeds, and more effort, were unrelated to prediction quality. For humans, slower responses were congruent with expectations; the prediction task was presumably difficult so slower responses, and more effort, led people to conclude that the predictions were high quality.
This hints at a highly nuanced effect of response time on prediction quality evaluations made by algorithmic systems. It seems that people’s judgment of quality for a prediction made by an algorithm can be meaningfully impacted by how quickly or slowly it is provided.
What can we do now with this information? Response time is a feature that is easily adaptable to existing algorithmic systems. As managers, we can implement these findings into our organisations. Since algorithmic predictions tend to be more accurate, why not calibrate the response time so that your human officers are more trusting of their predictions. As users of algorithmic systems, we can be aware of how simple cues (that are often outside of our control) can impact our judgment, even of non-human systems.
We hope our findings stimulate future research on this fascinating topic. Identifying cues that can change how we interact with algorithmic systems is a promising and valuable avenue for understanding human-algorithm interaction.