SlowGuess/NeurIPS_2023_OCR huggingface dataset:
ChatGPT obtains an accuracy of  $69\%$  during our evaluation and we restrict our attention to specific subjects within the MMLU dataset. Specifically, we sample 5 subjects (out of the 57 in MMLU) where ChatGPT has significantly better perform performance than average, 5 where it's significantly worse, and 4 subjects where performance is similar to average performance. These subjects are listed here:
high school government and politics, marketing, high school psychology, logical fallacies, sociology, public relations, high school computer science, anatomy, business ethics, elementary mathematics, high school statistics, machine learning, moral scenarios, global facts
We sample 150 questions from each subject and additionally sample 150 questions from OpenBookQA dataset [54] to use as attention checks as human performance on OpenBookQA is  $91\%$ .
We run IntegrAI on both the dataset embeddings, metadata (subject name), and an embedding of ChatGPT explanations separately. We find 10 regions based on the metadata and 2 regions based on the ChatGPT explanations. The regions and their descriptions found by our algorithm are reported in Table 9.
Table 9: Region descriptions found by IntegrAI for the MMLU user study.
Region ID	Description
1	Related to marketing, including pricing strategies, branding, communication, product classification, market segmentation, advertising, and supply chain management.
2	Questions related to psychology and neuroscience.
3	Questions related to global statistics and trends, ranging from military spending to mental health disorders.
4	Questions inside the region cover a variety of topics in the field of public relations, including ethical frameworks, evaluation models, common tactics, and regulations.
5	Mathematical and quantitative questions, involving calculations and problem-solving.
6	Questions related to sociology, including topics such as social class, symbolic interactionism, bureaucracy, and globalization.
7	Questions and descriptions of logical fallacies and syllogisms.
8	Questions focus on US politics, government, and history.
9	Contains questions related to anatomy and physiology.
10	Various topics including ethics, regulation, consumer rights, and corporate transparency.
11	Various questions on different topics such as algebra, biology, public relations, and statistics with multiple options to choose from.
F.3 Screenshot of User Study Interface for BDD
5https://github.com/hendrycks/test
Classify Images of Road Scenarios
You are invited to participate in a research study how people classify images. You will be presented with a series of images and have to make judgements. You may additionally be asked to interact with an AI model and write a few sentences of text.
This study will include participants over 18 years of age, who feel comfortable using an online interface to read and answer questions in English and investigate images.
To reiterate, you are eligible for this study if and only if:
- Are able to distinguish images of different types.
- You are comfortable reading/writing in English.
- You have not already completed this survey.
You are over 18 years of age.
- You have JavaScript enabled in your browser.
What Will My Participation Involve?
If you decide to participate in this research you will be asked to classify different images, write answers to questions in English and interact with AI models.
Are There Any Risks To Me?
We don't anticipate any risks from participation in this study greater than normal activity.
Are There Any Benefits To Me?
There are no direct benefits to you other than monetary compensation.
How Will My Confidentiality Be Protected?
While there will probably be publications as a result of this study, your name will not be used. Only group characteristics will be published
Welcome to our experiment! (please read carefully)
After you fill out the information on this page please click the Start Experiment button to proceed. The experiment may contain multiple steps that you will be guided through.
Please do not refresh or press back the webpage as you may lose a fraction of
The experiment will contain attention checks that will not modify the question you are asked, but they will be easier questions of the same format.
There is a potential bonus of up to 2$ for good performance on the tasks.
If you have previously completed this study, you are auto-ineligible to complete it again!
Your answers to the below questions have no impact on your eligibility to perform the task.
Your Prolific IDs will NOT be shared with anyone or used in the study or tied to the information below, they are only collected for matching with the recorded study.
By completing this task, you consent to having your responses (excluding age, gender, eyesight and educational ability) be made publicly available for research and educational purposes.
Prolific ID:
#
What is your age? Select most appropriate age range.
35-50
What is your gender?
Prefer not to answer
What is the highest degree or level of school you have completed?
Some college credit, no degree
How would you rate your knowledge about Artificial Intelligence (AI) or Machine Learning?
I know how AI works at a basic level
How would you rate your eyesight? Poor indicates any major visual impairments, Excellent denotes 20/20 vision.
Fair
How would you rate your driving ability? Answer this question in terms of safety of driving.
I very rarely drive or I'm not confident in my ability.
Figure 7: Consent Form
Figure 8: User Information Collection
- This experiment will be in 3 stages.
- Task: You will be shown images of road scenarios from the point of view of inside a car, you will be asked to respond to check whether in the image there is any traffic light (red or green or yellow)
Your goal in is to be as accurate as you can be.
- Interface: you will have to first select an answer by choosing the corresponding button "Yes" or "No" and then pressing the "Submit" button.
You can track how many examples remain by looking at the progress bar on the top of the webpage after you press next. You will keep trying each example until you get the answer correct
Example (no need to click, only for viewing):
Is there a traffic light in the picture?
Figure 9: Practice Task instructions
Figure 10: Prediction without AI interface
Figure 11: Instructions for the onboarding phase
Figure 12: Model card information shown during onboarding.
Is there a traffic light in the picture?
Instructions (scroll down for task)
- AI MODEL: Images are now blurred and you have access to an Artificial Intelligence (AI) model that provides their prediction
- AI Button: A new button now appears called "Use AI Answer" in blue and allows you to select the AI's answer which you can then submit.
- Please press the button if the AI helped with your answer and you want to use it's answer, if you agree with the answer of the AI but got the answer without it's help then don't press the button.
- If the AI predicts there to be a traffic light, it will put a box around it in the image and a score (you can hide or show it with a button below the image)
- Teaching Phase: The next of examples will help teach you more about the AI through examples where you will receive feedback, after that, there will be a testing phase where the 2$ bonus applies.
- We give you the following information about the Al model to help you understand when you should use it's answer and when not to:
Average AI Accuracy:	78%
Average Human Accuracy:	72%
AI Model Input:	Blurry Image
AI Model Output: Prediction of traffic light, bounding box on image showing it's location and a score indicating it's confidence
Source of Training Data: Dataset of road images from New York and San Francisco and Bay Area.
AI Training Objective: Detect Traffic Lights and other objects in image
- Now a deeper dive into the AI error/accuracy:
Category: Accuracy
more than 5 traffic lights in image	90%
If there is 1 to 4 traffic lights	62%
If there is no traffic lights	86%
No Cars	83%
Daytime or overcast weather	75%
few pedestrians	76%
Figure 13: Prediction with AI interface (AI predicts no traffic light)
Figure 14: Feedback shown during onboarding phase after human predicts.
Figure 15: Feedback shown during onboarding phase after human predicts (sets of examples from region)
Figure 16: Prediction with AI interface (AI predicts there is a traffic light)
Hide AI answer Show Instructions
AI Predicts: YES with score: 0.92
Use AI Answer
AI Predicts: YES with score: 0.65
Correct: You predicted correctly!, the correct answer was yes.
AI was Correct: The AI predicted correctly.
This example is part of a larger region with the following recommendation:
Recommendation: Rely on AI Answer
Region: depict various scenes of city streets and highways with varying weather conditions, traffic, and signage.
Region Info
AL Accuracy: 91.0
Human Accuracy: 78.0
Figure 17: Feedback shown during onboarding phase after human predicts (correct feedback)
Figure 18: Testing phase instructions
Figure 19: Testing phase instructions that include AI-integration recommendation
- Testing Phase: You will be now tested on a series of examples with the help of the AI model.
- AI MODEL: Images are now blurred and you have access to an Artificial Intelligence (AI) model that provides their prediction
- AI Button: A new button now appears called "Use AI Answer" in blue and allows you to select the AI's answer which you can then submit.
Please press the button if the AI helped with your answer and you want to use it's answer, if you agree with the answer of the AI but got the answer without it's help then don't press the button.
- If the AI predicts there to be a traffic light, it will put a box around it in the image and a score (you can hide or show it with a button below the image)
Try to do your best to get things correctly as there is a potential bonus of up to 2\$.
Please read instructions (25 secs
- AI MODEL: Images are now blurred and you have access to an Artificial Intelligence (AI) model that provides their prediction
- AI Button: A new button now appears called "Use AI Answer" in blue and allows you to select the AI's answer which you can then submit.
- Our Recommendation: Additionally, above the button and the Al's answer, we give you a recommendation whether you should use the Al answer or on your own. This recommendation is based on comparing your estimated personal ability compared to the Al's ability, this recommendation is not always correct.
Please read instructions (14 secs
Is there a traffic light in the picture?
Figure 20: Prediction interface with AI and with AI-integration recommendations.
F.4 Screenshot of User Study Interface for MMLU
- We give you the following information about the AI model to help you understand when you should use it's answer and when not to:
Average AI Accuracy:	69%
Average Human Accuracy:	50% (varies between people and subjects)
AI Model Input:	Question and the answers
AI Model Output:	An answer and a text explanation
Source of Training Data:	Trained on all the internet
AI Training Objective:	Trained to mimic language and respond to instructions
- Now a deeper dive into the AI error/accuracy:
Category:	Accuracy
Machine Learning	43%
Management/Marketing	85%
Elementary Math	60%
Psychology/Sociology	86%
Global Facts	42%
Moral Scenarios	44%
Logical Fallacies	78%
High School Statistics	59%
Figure 21: Model Card for MMLU study
Question: is an assessment of a company's mix of products, services, investments, and other assets in order to optimize the use of resources and to assess its suitability, level of risk, and expected financial return.
A: SWOT analysis.
B: Political analysis.
C: Portfolio analysis
D: Competitive analysis.
AI Predicts: C
Answer: C. Portfolio analysis. It involves evaluating a company's product portfolio to determine its strengths and weaknesses, as well as its suitability, level of risk, and expected financial return. This allows the company to allocate its resources more effectively and efficiently.
Figure 22: Prediction Interface for MMLU study
Wrong: You predicted incorrectly., the correct answer was C.
Figure 23: Feedback shown to user during teaching phase
Figure 24: Prediction Interface for MMLU study with AI-integration recommendations.
This example is part of a larger set of examples with the following recommendation:
related to marketing, including pricing strategies, branding, communication, product
classification, market segmentation, advertising, and supply chain management..
Region Information:
AI Accuracy: 92.0
Human Accuracy: 40.0
Here are more examples from this region for you to generalize from
Question: Which of the following is an example of costbased pricing?
A: Basing the price on what the customer is willing to pay
B: Basing the price on the psychological expectations of the customer
C: Basing the price on the features and benefits of the product
D: Basing the price on the full cost of production plus the required profit
Question: If a child inherits a physical trait from his father he will likely receive
A: tooth gap
B: favorite color
C: sense of fun
D: liking dogs
Recommendation: Ignore AI Answer
Al often wrong when various questions on different topics such as algebra, biology,
public relations, and statistics with multiple options to choose from.
AI Predicts: A
The answer is A. tooth gap because physical traits, such as the gaps
between teeth, are primarily determined by genetics and can be passed down from parents to children.