SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In our experiment setup, given the sender's goal is to have the receiver purchase the products regardless of the product quality, when the sender is more successful, it leads to a lower receiver utility in general and implies the potential negative social impacts.
C.1 Demographic of Workers
We have recruited 300 workers from Amazon Mechanical Turk in total for our experiments. Table 12 contains the demographic information of the 300 workers.
Table 12: Demographic information of the participants in our experiment.
Group	Category	Number
Age	20 to 29	88
30 to 39	111
40 to 49	65
50 to 59	25
60 or older	11
Gender	Female	131
Male	168
Other	1
Race / Ethnicity	Caucasian	240
Black or African-American	18
American Indian/Alaskan Native	5
Asian or Asian-American	22
Spanish/Hispanic	6
Other	9
Education	High school degree	12
Some college credit, no degree	9
Associate's degree	24
Bachelor's degree	223
Graduate's degree	29
Other	3
C.2 Task Interface and Description
In our human-subject experiments, we simulate the setting with binary actions and binary states. In particular, we present the product purchasing example as we discussed in Section 2. The task interface about our human-subject experiments is shown in Figure 6.
Each human participant is asked to make multiple rounds of purchase decisions. In each round, the participant is presented a product with unknown binary quality (either good product or bad product). The participant is told that a (noisy) inspection has been performed on the product, and is given the conditional distribution associated with the inspection (i.e., the probability to receive a good/bad signal given the product is good/bad). Finally, the participant is given a realization of the inspection signal and is asked to make a binary decision of purchasing or not. The participant's reward depends on both their purchasing decisions and the true product quality. When collecting human response in the first phase, random policy are presented to all participants. In the second phase, different policies are presented: {Random, BR-policy, TH-policy, HAIDNet policy}. The policies are designed with the assumption that the sender is persuading human receivers to purchase the product, and we calculate the probability of participants choosing to purchase and report it as the sender utility to evaluate performance of different policies.
In this round, consider the following scenario:
1. You are presented with a product . The product could be Good or Bad , and you need to decide whether to purchase it. You bonus will depend on your purchase decision and the product quality as shown below:
If you "Buy" a Good product, you will get a bonus of 3 cents.
$\star$  If you "Don't Buy" a Bad product, you will get a bonus of 2 cents.
$\star$  Otherwise, you won't get any bonus for this round.
2. Below is the information about the product for you to make the decision.
The product is randomly selected from a pool of products. For all products,  $70\%$  are Good and  $30\%$  are Bad.
$\star$  After given the product, you will perform a noisy inspection.
If the product is good, you will receive a good signal with  $20\%$  chance or a bad signal with  $80\%$  chance.
If the product is bad, you will receive a good signal with  $80\%$  chance or a bad signal with  $20\%$  chance.
Figure 6: Human experiment interface.
3. Which action will you take when the inspection result is
Your Decision Is:
Don't Buy