Paper: Does Calibration Affect Human Actions? (ICLR-2024)

Reviewer comment (Experiments):
The paper's experimentation is lacking, and the current experimental setup is much too simplistic: just asking the users how much they trust the system can result in a lot of noise especially as users have no reason to be faithful. Prior works usually measure some proxy for trust, or simulate an environment where participant's trust is linked to some monetary risk/reward.

Author response:
We agree with the reviewer that the self reported trust by the participants is noisy. Indeed, our results clearly support this claim. Therefore, our results are based on the participants' report on how likely they are to cancel their outdoor activity based on the model’s prediction. Recall that we show that there is a statistical significant difference between the different treatment groups, thus, it is quite unlikely that these differences are due only to noise.

Reviewer comment (Experiments):
The authors show experiments on a single task and ignore the temporal effects of changing trust as the participant interacts with the system.

Author response:
We plan to add results for another task.

Reviewer comment (Experiments):
A lot of experimental design choices (e.g., Likert scale to quantify trust) seem to differ from prior works that examine human trust. Perhaps the authors could spend more time justifying them.

Author response:
We used different Likert scales during the experiment (1-5, and 1-7) to ensure that the participants think and answer each question separately (so that the answer to one question will have a lesser effect on the other question).

Reviewer comment (Experiments):
How much time did each participant take to complete the survey on average?

Author response:
The average work time of the participants is 9.5 minutes.

Reviewer comment (Experiments):
How do the authors ensure that the participant responses were faithful?

Author response:
Recall that we asked the participants to report the values that the model predicted and the actual outcome. This encourages the participants to answer seriously; we will emphasize this in the paper. Furthermore, all participants in the survey were required to have at least 99% approval rate (on other tasks on Mechanical Turk).

Reviewer comment (Experiments):
Did the participants receive $1 per question or for 30 questions?

Author response:
$1 for all questions, but note that there were 20 questions, not 30 questions.

Reviewer comment (Experiments):
One of the crucial limitations of the paper is lack of thorough description of human study conducted. The paper claims that "there is no reported difference in the level of trust reported by the participants". However, without further information on the nature of instructions / guidelines provided to the human subjects, it could very well be the case that the subjects of this study behaved randomly (which is not an uncommon phenomenon, and is usually controlled for in user studies by designing good incentive mechanisms). The paper (in the current form) does not delve much deeper whether the measures were taken to control random behaviour. My opinion is also informed by correlation in Figure 7, where the difference between random and calibrated / uncalibrated is not that different.

Author response:
- The instructions to the participants appear at the beginning of section 4.2 and in Figure 5. However, we notice that the text in the figure is hard to read; this will be corrected.

- Recall that we asked the participants to report the values that the model predicted and the actual outcome. This encourages the participants to answer seriously; we will emphasize this in the paper. Furthermore, all participants in the survey were required to have at least 99% approval rate (on other tasks on Mechanical Turk).

- The random model is not arbitrary in its predictions. It provides probabilities like the PT-calibrated model, with the distinction that the determination of whether it rained on a specific day was randomly generated. As a consequence, the probabilities it produces tend to be extreme, i.e., either close to 0 or 1. We believe that it is more convenient for people to listen to a model that provides predictions at the extremes, as this diminishes uncertainty. However, when the level of trust also take into account, the "random" model falls significantly short compared to other models. We intend to change the name of the model since "random" does not accurately reflect how it works.

Reviewer comment (Novelty):
The paper misses some of the relevant literature on the implications of calibration to decision making [1,2].

[1] Benz et al. Human-Aligned Calibration for AI-Assisted Decision Making.  
[2] Rothblum et al. Decision-Making under Miscalibration.

Author response:
Answers:
We thank the reviewer for the pointers and will add them to the paper.

Reviewer comment (Experiments):
The study setting is somewhat limited in that the participants have to make decisions based on the predictions of the model only and have no other information available. This doesn't seem to be realistic in most assisted decision making scenarios, where the individual could ignore the model if they do not trust it and base the decision on their own knowledge (e.g., the tasks in Vodrahalli et al. 2022). It would be interesting to know if we can expect that calibration+prospect theory to also lead to higher correlation in such tasks where the individual has the same (or other/additional) information available as the model.

Author response:
In the current version of the paper, we attempted to use the “cleanest” environment for testing our hypothesis. However, we are planning to run additional experiments in a new domain, in which we will allow the participants to gather additional information.

Reviewer comment (Experiments):
For some parts of the evaluation it is unclear which data was used: Are the results of Table 1 and Figure 2 from the data in the validation set? Was the test set used in the survey with the human participants?

Author response:
Table 1 and Figure 2 were both calculated on the validation set. In the current manuscript, the test set results are not reported. We will add them to the next version.

Reviewer comment (Presentation):
It would be nice if the authors could point out earlier that the $\gamma$ value chosen is not specific for this task. This was unclear when described in page 6 and only discussed much later in the conclusion.

Author response:
We will point this out earlier, as the reviewer suggested.

Reviewer comment (Evaluation):
It is interesting that, even though individuals reported to trust the random model less, the correlation of the random model's prediction with the individuals' decisions is higher than the calibrated and uncalibrated model's correlation (Figure 6a and 7). Do the authors have an intuition why this is the case?

Author response:
The random model is not arbitrary in its predictions. It provides probabilities like the PT-calibrated model, with the distinction that the determination of whether it rained on a specific day was randomly generated. Consequently, the probabilities it produces tend to be extreme, either close to 0 or 1. We believe that it is more convenient for people to listen to a model that delivers predictions at the extremes, as this diminishes uncertainty. We intend to change the name of the model since "Random" does not accurately reflect how it works.