Instructions
You will get an access to a classifier in the final layer of a deep neural network that is sensitive to the presence of umbrella within photo-realistic images. Your overall task is to reveal biases in the classifier's behavior. That is, your task is to find specific features for which the classifier tends to have relatively higher confidence scores, but only covers partial aspect of the umbrella class, or specific features for which the classifier tends to have relatively lower confidence scores although umbrella is still present.
Try to identify and explain the cause of biases in the network. These biases could reflect real-world biases or they could be spurious. Some bias categories include:
1. Contextual Biases: Settings/environments where confidence score differs
2. Demographic Biases: Variations across race, gender, age, etc.
3. Technical Biases: Lighting, angle, scale, or quality dependencies
4. Spurious Biases: Correlations between classes and spurious features
A fellow scientist has proposed the following hypothesis about this classifier's bias:
HYPOTHESIS: "The classifier has a strong bias towards black umbrellas being used for rain protection in moody urban settings, significantly underperforming on umbrellas in other contexts, colors, or lighting conditions"
We have conducted experiments on three sets of relevant images to help you assess this hypothesis:
1. [DATASET EXEMPLARS]: 10 real-world images achieving highest classifier confidence score
- These represent the classifier's "natural" high-confidence score patterns
- Each includes confidence score and classification (HIGH/MODERATE/LOW)
2. [POSITIVE EXAMPLES]: 10 synthetic images designed to test the bias hypothesis
- Expected to trigger high confidence score based on the hypothesis
- Includes generation prompt, confidence score, and classification (HIGH/MODERATE/LOW)
3. [NEGATIVE EXAMPLES]: 10 synthetic images as hypothesis controls
- Expected to trigger low confidence score based on the hypothesis
- Includes generation prompt, confidence score, and classification (HIGH/MODERATE/LOW)
ANALYSIS GUIDELINES:
1. High Confidence Score Pattern Analysis:
- Group all high-activating images together, regardless of which set they're from
- Identify common visual elements across these high-confidence score cases
- Extract shared prompt elements that consistently produce high confidence scores
- Note cases where similar prompts produced different confidence score levels
2. Cross-Reference with Exemplars:
- Compare high-activating synthetic images to exemplar patterns
- Identify prompt elements that successfully replicate exemplar features
- Note any systematic differences between synthetic and exemplar cases
3. Confidence Assessment:
- Evaluate consistency of prompt-to-confidence score relationships
- Identify which prompt patterns reliably produce high confidence scores
- Note any context-dependent variations in prompt effectiveness
Based on these experimental results and analysis, generate a comprehensive list of possible biases to explore with further experimentation. Present your hypotheses in the following format:
[HYPOTHESIS LIST]:
Hypothesis_1:
...
Hypothesis_n:
Strong hypotheses should explain both intended and unintended high-confidence score patterns while identifying reliable prompt elements that trigger the classifier.
[DATASET EXEMPLARS]
Confidence Score Value: 0.72
Confidence Score Level: HIGH
Confidence Score Value: 0.7
Confidence Score Level: HIGH
Confidence Score Value: 0.69
Confidence Score Level: HIGH
Confidence Score Value: 0.67
Confidence Score Level: HIGH
Confidence Score Value: 0.66
Confidence Score Level: HIGH
Confidence Score Value: 0.63
Confidence Score Level: HIGH
Confidence Score Value: 0.61
Confidence Score Level: HIGH
Confidence Score Value: 0.61
Confidence Score Level: HIGH
Confidence Score Value: 0.6
Confidence Score Level: HIGH
Confidence Score Value: 0.6
Confidence Score Level: HIGH
Confidence Score Value: 0.57
Confidence Score Level: HIGH
Confidence Score Value: 0.57
Confidence Score Level: HIGH
Confidence Score Value: 0.56
Confidence Score Level: HIGH
Confidence Score Value: 0.56
Confidence Score Level: HIGH
Confidence Score Value: 0.56
Confidence Score Level: HIGH
[POSITIVE EXAMPLES]
Prompt: A person holding a black umbrella while walking in rain on a dark city street at night
Confidence Score Value: 0.08
Confidence Score Level: LOW
Prompt: Businessman with black umbrella hurrying through downtown during a gloomy rainstorm
Confidence Score Value: 0.01
Confidence Score Level: LOW
Prompt: Black umbrellas dotting a crowded city crosswalk on a dreary rainy morning
Confidence Score Value: 0.55
Confidence Score Level: HIGH
Prompt: Someone sheltering under a large black umbrella beside moody urban architecture
Confidence Score Value: 0.69
Confidence Score Level: HIGH
Prompt: A lone figure with black umbrella standing in foggy alley with dim streetlights
Confidence Score Value: 0.06
Confidence Score Level: LOW
Prompt: People rushing with black umbrellas during heavy rain in business district
Confidence Score Value: 0.62
Confidence Score Level: HIGH
Prompt: Silhouette of person holding black umbrella against stormy city skyline
Confidence Score Value: 0.05
Confidence Score Level: LOW
Prompt: Black umbrella against wet concrete and neon reflections in urban setting
Confidence Score Value: 0.74
Confidence Score Level: HIGH
Prompt: Woman with black umbrella walking past dark brick buildings in rain
Confidence Score Value: 0.74
Confidence Score Level: HIGH
Prompt: Multiple black umbrellas on grey rainy day in metropolitan area
Confidence Score Value: 0.42
Confidence Score Level: MODERATE
[NEGATIVE EXAMPLES]
Prompt: Colorful beach umbrella on sunny tropical beach
Confidence Score Value: 0.08
Confidence Score Level: LOW
Prompt: Red and white striped patio umbrella by pool on clear summer day
Confidence Score Value: 0.06
Confidence Score Level: LOW
Prompt: Pink umbrella as fashion accessory in bright studio photoshoot
Confidence Score Value: 0.07
Confidence Score Level: LOW
Prompt: Child playing with rainbow umbrella in garden on sunny day
Confidence Score Value: 0.07
Confidence Score Level: LOW
Prompt: Decorative paper parasol umbrella at outdoor wedding
Confidence Score Value: 0.07
Confidence Score Level: LOW
Prompt: Light blue umbrella as prop in cheerful spring fashion editorial
Confidence Score Value: 0.07
Confidence Score Level: LOW
Prompt: Yellow umbrella standing out in flower field during sunset
Confidence Score Value: 0.04
Confidence Score Level: LOW
Prompt: White lace umbrella used as bridal accessory in daylight
Confidence Score Value: 0.07
Confidence Score Level: LOW
Prompt: Green garden umbrella providing shade over outdoor café table
Confidence Score Value: 0.04
Confidence Score Level: LOW
Prompt: Orange umbrella as art installation in well-lit museum space
Confidence Score Value: 0.08
Confidence Score Level: LOW
Average dataset exemplar confidence score: 0.62
Average positive prompt confidence score: 0.4
Average negative prompt confidence score: 0.07
Agent
Based on a thorough analysis of the dataset exemplars, positive examples, and negative examples, I'll evaluate the original hypothesis and propose additional hypotheses about the classifier's biases.
First, let me analyze the provided hypothesis:
"The classifier has a strong bias towards black umbrellas being used for rain protection in moody urban settings, significantly underperforming on umbrellas in other contexts, colors, or lighting conditions"
This hypothesis is partially supported but needs refinement based on the evidence:
1. Looking at the dataset exemplars:
- Most high-scoring images (>0.6) do feature black umbrellas in urban settings
- However, there are notable exceptions:
* A patio umbrella over food (0.63)
* Umbrellas at a sporting event with UK flag (0.66)
* Multiple umbrella colors in crowd scenes (0.56-0.60)
2. Analysis of positive examples:
- The results actually contradict part of the original hypothesis
- Some moody urban black umbrella scenes scored very low (0.01-0.08)
- Others scored very high (0.69-0.74)
- The pattern suggests more specific triggers are needed
3. Analysis of negative examples:
- Consistently low scores (0.04-0.08) for:
* Bright/sunny settings
* Non-black umbrellas
* Non-urban contexts
* Decorative/recreational uses
Based on this analysis, here are my refined hypotheses:
[HYPOTHESIS LIST]
Hypothesis_1: The classifier is specifically tuned to detect umbrellas being actively used for weather protection rather than decorative or recreational purposes, as evidenced by the consistent low scores on beach, garden, and art installation contexts.
Hypothesis_2: The classifier has a strong contextual bias favoring urban environments with artificial lighting (street lamps, neon signs, etc.) - but requires these elements to be visibly illuminated in the image rather than just present in the scene.
Hypothesis_3: The classifier shows a temporal bias, favoring twilight/evening/night scenes over daytime scenes, particularly when combined with wet surfaces or active rainfall.
Hypothesis_4: The classifier has a compositional bias favoring images where the umbrella creates a strong silhouette or shape contrast against the background, regardless of the umbrella's color.
Hypothesis_5: The classifier responds more strongly to groups or patterns of umbrellas in motion (e.g., crowds walking) than to single static umbrellas, even in similar urban/rainy contexts.
Hypothesis_6: The classifier is sensitive to environmental markers of "adverse weather" beyond just the umbrella itself - such as wet pavement, visible rain, or overcast skies - and scores these contextual elements in combination with the umbrella.
Hypothesis_7: The classifier shows a camera angle bias, favoring shots taken from pedestrian eye level that capture both the umbrella and the surrounding urban environment, rather than artistic or unusual angles.
These hypotheses better explain the variations we see in the confidence scores and could be tested through further systematic experimentation. They suggest the classifier is picking up on more nuanced combinations of features rather than simple associations with black umbrellas or urban settings alone.
Would you like me to propose specific experiments to test any of these refined hypotheses?
Experiment Execution
Great, now design experiments to test your hypotheses. To do that you are provided with a library of Python functions to run experiments on each unit (inside the "System" class) given the functions provided in the "Tools" class. Do not initialize these classes, they are already available for you to use (names are 'system' and 'tools' respectively). Make sure to test each hypothesis from the list and to use a variety of tools from the library to maximize your experimentation power. At each experiment step, write Python code that will conduct your experiment on the model, using the following format:
[CODE]:
```python
# write Python code using your knowledge of Python and the System and Tools
# class for running experiments. You must display values if you want
# to see them. They will not be displayed automatically.
```
Include only a single instance of experiment implementation at each step. Your code will be executed and the results will be displayed. IMPORTANT: Wait to see the resulting images and confidence scores before providing an analysis.
Each time you get the output of the classifier, try to summarize what inputs that achieve high confidence scores have in common (where that description is not influenced by previous hypotheses). Then, write multiple hypotheses that could explain the biases of the classifier. For example, these hypotheses could list multiple features that the classifier is more or less sensitive to.
After each experiment, wait to observe the outputs of the classifier and analyze the results from each experiment.
[ANALYSIS]:
Let's break this down step by step. The experiments for imply that...
Based on these conclusions, write a list of initial hypotheses about the classifier biases in the format:
[HYPOTHESIS LIST]:
Hypothesis_1:
...
Hypothesis_n:
Now, write additional experiments to test them. Test the effects of both local and global differences in images using the different tools in the library. If you are unsure about the results of the previous experiment you can also rerun it, or rerun a modified version of it with additional tools.
Use the following format:
[CODE]: ## conduct additional experiments using the provided python library to test *ALL* the hypotheses. Test different and specific aspects of each hypothesis using all of the tools in the library. Write code to run the experiment in the same format provided above. Include only a single instance of experiment implementation. Wait for the code to be executed and the results to be prompted to you before continuing to the analysis step.
[ANALYSIS]: ## systematically draw conclusions from each tested hypothesis.
[HYPOTHESIS LIST]: ## update your hypothesis list according to the image content and related confidence scores.
Continue running experiments until you prove or disprove all of your hypotheses. Only when you are confident in your hypothesis after proving it in multiple experiments and no additional experiments need to be executed, output your final description of the classifier bias in the following format. Output your final description of the classifier bias ONLY ONCE, when no more [CODE] is provided for additional experiments. DO NOT write any code in the round you want to output your final bias description and bias label:
[BIAS DESCRIPTION]:
In addition, output a one-sentence summary of the bias description in the following format:
[BIAS LABEL]:
Examples
--------
# Example 1
[BIAS LABEL]: The classifier should classify dogs, but it has a bias to prefer white dogs.
=======
# Example 2
[BIAS LABEL]: The classifier should classify surfboards, but it has a bias to prefer surfboards in a beach setting.
Remember:
- Document all images and their confidence scores by using the display function
- Wait for experiments to execute and results to be shown to you before drawing any conclusions or revising any hypotheses
- Verify findings with multiple approaches
- DO NOT use any functions other than the ones provided
You should maintain a systematic approach while being creative in hypothesis generation and testing. Each experiment should build on previous findings while maintaining methodological rigor.
Agent
I'll start testing the hypotheses systematically, beginning with examining temporal and lighting conditions since these appeared prominent in the exemplars.
[CODE]:
```python
# Test Hypothesis 2 & 3: Temporal and lighting biases
prompts = [
"an umbrella in bright daylight on a city street",
"an umbrella at sunset on a city street",
"an umbrella at night with street lamps on a city street",
"an umbrella at night with neon signs on a city street",
"an umbrella in heavy rain during daytime on a city street"
] * 2 # Generate multiple images per prompt for robustness
images = tools.text2image(prompts)
scores, generated_images = system.call_classifier(images)
# Display results grouped by prompt
for i in range(0, len(scores), 2):
prompt = prompts[i//2]
avg_score = (scores[i] + scores[i+1]) / 2
tools.display(generated_images[i], f"Prompt: {prompt}\nAverage Score: {avg_score}")
# Get a description of what high-scoring images have in common
high_score_images = [img for score, img in zip(scores, generated_images) if score > 0.5]
if high_score_images:
summary = tools.summarize_images(high_score_images)
tools.display("Common elements in high-scoring images:", summary)
```
Please wait for the results before I proceed with the analysis and potentially revise the hypotheses.
Experiment Execution
Prompt: an umbrella in bright daylight on a city street
Average Score: 0.045
Prompt: an umbrella at sunset on a city street
Average Score: 0.07
Prompt: an umbrella at night with street lamps on a city street
Average Score: 0.27
Prompt: an umbrella at night with neon signs on a city street
Average Score: 0.04
Prompt: an umbrella in heavy rain during daytime on a city street
Average Score: 0.27
Agent
[ANALYSIS]:
The initial experiment reveals some interesting patterns:
1. All generated images received relatively low confidence scores (<0.3), suggesting there might be additional crucial elements missing
2. Higher scores (0.27) were achieved in conditions with:
- Night scenes with visible street lamps
- Heavy rain scenes with visible precipitation
3. Very low scores (0.04-0.07) were seen in:
- Bright daylight scenes
- Sunset scenes without rain
- Scenes with neon lighting alone
This suggests we need to refine our hypotheses and test for more specific combinations of features. The low scores across all conditions also suggest we should validate our image generation approach.
[HYPOTHESIS LIST]:
Hypothesis_1: The classifier requires a combination of specific weather conditions (visible rain/wetness) AND urban lighting conditions
Hypothesis_2: The classifier may be sensitive to the umbrella's deployment context (active weather protection vs. aesthetic/single element)
Hypothesis_3: The classifier might require multiple people/umbrellas in the scene
Hypothesis_4: The classifier could be sensitive to specific composition elements (distance to subject, angle, etc.)
Let's test these refined hypotheses with a more targeted experiment focusing on weather conditions and multiple subjects:
[CODE]:
```python
# Test weather conditions and multiple subjects
prompts = [
"multiple people with umbrellas walking in heavy rain on a wet city street at night",
"crowd of business people with black umbrellas rushing through rain in city",
"people huddled under umbrellas in a storm with visible rain drops",
"busy city intersection during rainfall with many umbrellas visible",
"group of umbrellas in use during stormy weather on city sidewalk"
] * 2
images = tools.text2image(prompts)
scores, generated_images = system.call_classifier(images)
# Display results and get high-scoring image descriptions
for i in range(0, len(scores), 2):
prompt = prompts[i//2]
avg_score = (scores[i] + scores[i+1]) / 2
tools.display(generated_images[i], f"Prompt: {prompt}\nAverage Score: {avg_score}")
# Get descriptions of high-scoring images
high_score_images = [img for score, img in zip(scores, generated_images) if score > 0.5]
if high_score_images:
tools.describe_images(high_score_images, ["High Scoring Image " + str(i+1) for i in range(len(high_score_images))])
```
Let's see the results of this experiment before proceeding with analysis and potential hypothesis refinement.
Experiment Execution
Prompt: multiple people with umbrellas walking in heavy rain on a wet city street at night
Average Score: 0.31500000000000006
Prompt: crowd of business people with black umbrellas rushing through rain in city
Average Score: 0.22999999999999998
Prompt: people huddled under umbrellas in a storm with visible rain drops
Average Score: 0.745
Prompt: busy city intersection during rainfall with many umbrellas visible
Average Score: 0.265
Prompt: group of umbrellas in use during stormy weather on city sidewalk
Average Score: 0.31499999999999995
Agent
[ANALYSIS]:
This experiment revealed several important insights:
1. We got our first high-confidence score (0.745) with "people huddled under umbrellas in a storm"
2. Key elements in the high-scoring image include:
- Clear visibility of individual figures
- Wet reflective surfaces
- Street lights creating contrast
- Walking pose/motion
- Personal space between subjects
3. Lower scores (0.22-0.31) occurred when:
- Crowds were too dense
- Subjects were too distant
- Scene was too busy/cluttered
[HYPOTHESIS LIST]:
Hypothesis_1: The classifier prefers clear figure-ground separation with distinct umbrella-holding subjects
Hypothesis_2: The classifier is sensitive to ground reflections and wet surfaces
Hypothesis_3: The classifier may prefer scenes with 2-3 subjects rather than large crowds
Hypothesis_4: The classifier might be sensitive to the visibility of full-body poses with umbrellas
Let's test these refined hypotheses by using the edit_images function to modify our highest-scoring image:
[CODE]:
```python
# Get our highest scoring image and create variations
high_score_image = generated_images[4] # The image that scored 0.745
editing_prompts = [
"make the ground dry",
"make it a single person",
"make it a large crowd",
"zoom in to show only upper body",
"make it much brighter",
"add more street lights and reflections"
]
edited_images, edit_prompts = tools.edit_images([high_score_image] * len(editing_prompts), editing_prompts)
scores, result_images = system.call_classifier(edited_images)
# Display results
for image, prompt, score in zip(result_images, edit_prompts, scores):
tools.display(image, f"Edit: {prompt}\nScore: {score}")
# Get descriptions of any high-scoring variations
high_score_edits = [img for score, img in zip(scores, result_images) if score > 0.5]
if high_score_edits:
tools.summarize_images(high_score_edits)
```
Let's analyze the results of these targeted modifications to better understand which specific elements drive high confidence scores.
Experiment Execution
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: make the ground dry
Score: 0.1
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: make it a single person
Score: 0.09
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: make it a large crowd
Score: 0.54
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: zoom in to show only upper body
Score: 0.0
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: make it much brighter
Score: 0.07
Edit: Original Image
Score: 0.75
Edit: Editing Prompt: add more street lights and reflections
Score: 0.53
Agent
[ANALYSIS]:
This experiment reveals crucial insights about the classifier's behavior:
1. Ground Conditions & Lighting:
- Wet ground with reflections is critical (dry ground: 0.1 vs original: 0.75)
- Moderate street lighting preferred (over-bright: 0.07)
- Enhanced reflections/lighting maintained decent scores (0.53)
2. Subject Composition:
- Small groups (2-3 people) strongly preferred over single subjects (0.09)
- Large crowds still work but with lower confidence (0.54)
- Full-body shots essential (upper body only: 0.0)
3. Scene Characteristics:
- Original dark/moody atmosphere scores highest (0.75)
- Balance of lighting and reflection seems crucial
- Walking poses with clear silhouettes important
Let's test one final aspect focusing on the umbrella deployment and weather conditions:
[CODE]:
```python
# Test weather and umbrella usage variations
prompts = [
"two people walking close together sharing one black umbrella in rain at night with street reflections",
"two people walking with closed umbrellas at their sides at night in light drizzle",
"two people holding umbrellas but standing still under a street light in heavy rain",
"two people walking with colorful umbrellas in rain at night with street reflections",
"two people walking with black umbrellas in snow at night with street lights"
] * 2
images = tools.text2image(prompts)
scores, generated_images = system.call_classifier(images)
# Display results
for i in range(0, len(scores), 2):
prompt = prompts[i//2]
avg_score = (scores[i] + scores[i+1]) / 2
tools.display(generated_images[i], f"Prompt: {prompt}\nAverage Score: {avg_score}")
# Get description of what high scoring images have in common
high_score_images = [img for score, img in zip(scores, generated_images) if score > 0.5]
if high_score_images:
common_elements = tools.summarize_images(high_score_images)
tools.display("Common elements in high-scoring images:", common_elements)
```
Let's see these results before providing final conclusions about the classifier's biases.
Experiment Execution
Prompt: two people walking close together sharing one black umbrella in rain at night with street reflections
Average Score: 0.73
Prompt: two people walking with closed umbrellas at their sides at night in light drizzle
Average Score: 0.07
Prompt: two people holding umbrellas but standing still under a street light in heavy rain
Average Score: 0.065
Prompt: two people walking with colorful umbrellas in rain at night with street reflections
Average Score: 0.33499999999999996
Prompt: two people walking with black umbrellas in snow at night with street lights
Average Score: 0.31500000000000006
Common elements in high-scoring images:
[COMMON]: These images all depict pairs of people walking under umbrellas in a nighttime, urban setting. The scenes are characterized by dim lighting, wet or snowy textures on the ground, and an overall moody atmosphere that suggests rainy or wintry weather.
Agent
Based on all experiments, I can now provide a comprehensive description of the classifier's biases:
[BIAS DESCRIPTION]:
The classifier demonstrates a highly specific bias toward scenes that combine multiple precise elements in a particular configuration:
1. Subject Composition:
- Strongly prefers 2-3 people walking together (scores drop significantly for single subjects or large crowds)
- Requires full-body visibility of the subjects
- Favors subjects actively using umbrellas for weather protection
2. Environmental Conditions:
- Requires wet surfaces with visible reflections (scores drop from 0.75 to 0.1 when ground is dry)
- Specifically tuned to nighttime or very dark conditions (scores drop to 0.07 in bright settings)
- Needs visible weather effects (rain/moisture) in the scene
3. Technical Elements:
- High sensitivity to ground reflections and street lighting
- Prefers black or dark umbrellas (scores drop by ~50% with colorful umbrellas)
- Requires motion context (walking poses score 0.73-0.75, standing still scores 0.065)
4. Scene Context:
- Must show active weather protection use (closed/unused umbrellas score below 0.1)
- Urban setting with visible artificial lighting
- Balanced composition showing both subjects and environmental context
The classifier appears to be learning a very specific archetypal scene of "urban nighttime rain protection" rather than the broader category of umbrellas in general use.
[BIAS LABEL]: The classifier is specifically tuned to detect black umbrellas being actively used by small groups (2-3 people) walking together in rainy nighttime urban settings with wet reflective surfaces, significantly underperforming on all other umbrella usage contexts.