Source of Bias

Summary

This video script discusses the various stages where bias can infiltrate the AI and machine learning pipeline, from data collection to model deployment. It emphasizes the importance of considering representativeness in data, annotator beliefs, and the potential for biased metrics like accuracy on unbalanced data. The script also touches on user perception of bias and the feedback loop from user behavior to further data collection. Interactive examples illustrate how prompts can lead to unexpected AI outputs, highlighting the need for critical thinking about bias in AI models. The module includes hands-on activities to explore bias datasets and metrics, encouraging practical understanding and engagement with the topic.

Takeaways

• 📊 Bias can enter at various stages of the AI development pipeline, starting from data collection to model deployment.
• 🔍 The representativeness of collected data across different demographics is crucial to avoid bias.
• 🏷️ Data labeling involves annotators whose beliefs and geographical origins can influence the labeling process, potentially introducing bias.
• 📈 Training models with biased data or using metrics like accuracy on unbalanced data can result in biased models.
• 🚀 Once a model is deployed, user interactions can affect its performance and may reveal biases in unexpected ways.
• 🤔 Users might perceive bias even when it's not present, which is an important consideration for model evaluation.
• 🌐 User behavior can inform further data collection, creating a feedback loop that can either mitigate or exacerbate bias.
• 🖼️ The script discusses the potential for AI vision models to misinterpret prompts, leading to outputs that may not align with reality.
• 🏠 It highlights the importance of questioning the representation and accuracy of AI model outputs, using examples of house images from different countries.
• 🛠️ The module includes hands-on activities to understand and study bias, encouraging learners to engage with datasets and metrics.
• 📚 Supplemental video content and live sessions are provided for further exploration of bias topics, emphasizing the importance of practical understanding.

Q & A

What is the main focus of the video script?

The main focus of the video script is to discuss the various sources of bias in the AI and machine learning pipeline, from data collection to model deployment and user interaction.

Why is data representativeness important during data collection?

Data representativeness is important because it ensures that the model is trained on a diverse set of data that reflects all demographics, which can help to prevent biased outcomes.

What factors could influence the labeling of data for model training?

Factors that could influence data labeling include the annotators' beliefs, their cultural background, and the part of the world they are from, which might introduce bias into the training data.

What is the potential issue with using accuracy as a metric on unbalanced data?

Using accuracy as a metric on unbalanced data can lead to biased models because it may not accurately reflect the model's performance across different classes, especially the minority ones.

How can user behavior impact the AI model after deployment?

User behavior can impact the AI model by providing feedback that may indicate perceived bias, even if the model is not actually biased, which can affect user trust and model usage.

What is the role of the feedback loop in the context of AI model deployment?

The feedback loop allows for continuous monitoring and improvement of the AI model based on user interactions and perceptions, helping to identify and mitigate biases over time.

Why is it important to question the prompts used for AI model outputs?

Questioning the prompts is important because it helps to understand the context and potential biases that might have influenced the AI's output, ensuring a more critical evaluation of the model's performance.

What does the script suggest about the image of an 'Indian person' produced by a vision model?

The script suggests that the image produced by the vision model might not accurately represent all Indian people, as it may be based on a stereotype or limited data, highlighting the issue of representation in AI models.

How can the hands-on section of the module help participants understand AI bias?

The hands-on section allows participants to actively engage with creating datasets and using metrics to study bias, providing practical experience and deeper insights into the mechanisms and impacts of bias in AI.

What is the purpose of the supplement video content mentioned in the script?

The purpose of the supplement video content is to provide additional information and examples that can enhance understanding of AI bias, encouraging participants to explore the topic further.

What is the next step suggested for participants after watching the module?

The next step suggested is to watch the supplement video content, engage with the hands-on activities, and participate in live sessions to continue exploring and understanding AI bias.

Outlines

00:00

🔍 Exploring Sources of Bias in AI Models

This paragraph delves into the various stages where bias can infiltrate the AI development process. It starts with data collection, questioning whether the collected data is representative of all demographics. The paragraph then moves on to data labeling, considering the annotators' backgrounds and potential biases. Training the model is the next point of discussion, with a focus on the metrics and objectives chosen, especially the pitfalls of using accuracy on unbalanced data. The deployment of the model and potential user interactions, including misperceptions of bias, are also covered. Finally, the paragraph touches on the feedback loop from user behavior back into data collection. The speaker uses an image from a vision model to illustrate the concept of bias, prompting viewers to consider what the model's prompt might have been and how it might not align with real-world diversity.

05:01

📚 Hands-On Approach to Studying AI Bias

The second paragraph focuses on a practical approach to understanding AI bias through hands-on activities. It encourages viewers to engage with different datasets created to study bias and to explore metrics used for this purpose. The paragraph suggests that these activities will be part of the course and emphasizes the importance of doing the hands-on work to fully grasp the concept of bias. Additionally, it mentions supplementary video content and a code base available for further exploration. The speaker reassures that support will be provided through TA sessions and live interactions, and concludes by expressing hope to see the viewers in the next module, which will continue the discussion on bias with a focus on datasets, metrics, and ongoing research.

Keywords

💡Bias

Bias refers to a systematic error or deviation from expected results in the context of data collection, model training, and interpretation. In the video, bias is the central theme, with discussions on how it can arise at various stages of the AI development pipeline, from data collection to model deployment and user interaction.

💡Data Collection

Data collection is the process of gathering information required to train machine learning models. The video emphasizes the importance of ensuring that the collected data is representative of all demographics to avoid introducing bias, which can affect the fairness and accuracy of the model.

💡Representative Data

Representative data means that the dataset used for training AI models should reflect the diversity of the population it is meant to serve. The video script points out that a lack of representativeness in data can lead to biased models that do not perform well for all groups.

💡Labeling

Labeling in the context of AI refers to the process of assigning categories or tags to data, which is used to train models. The video mentions that the annotators' beliefs and geographical origins can influence the labeling process, potentially introducing bias.

💡Model Training

Model training is the phase where an AI model learns from the labeled data to make predictions or decisions. The script discusses how using biased data or inappropriate metrics can result in a model that perpetuates or amplifies existing biases.

💡Metrics

Metrics are quantitative measures used to evaluate the performance of a model. The video points out that using accuracy as a metric on unbalanced data can be misleading and lead to biased models, as it does not account for class imbalances.

💡Model Deployment

Model deployment is the stage where a trained AI model is put into production to be used by end-users. The script raises concerns about what happens if users try to 'jailbreak' or misuse the model, which could lead to unintended biases.

💡User Perception

User perception refers to how end-users interpret the outputs of an AI model. The video script uses an example where users might perceive the model's output as biased even when it is not, highlighting the complexity of user interaction with AI systems.

💡Feedback Loop

A feedback loop in AI refers to the process where user behavior and interactions with a model inform further data collection and model refinement. The video suggests that understanding user behavior is crucial to identifying and mitigating biases in AI systems.

💡Vision Model

A vision model is a type of AI model that processes visual data, such as images or videos. The video script discusses an example of a vision model output that may be influenced by bias, illustrating how biases can manifest in unexpected ways.

💡Hands-On

Hands-On refers to practical, interactive activities or exercises that allow learners to apply concepts learned. The video script encourages viewers to engage in hands-on activities to better understand bias in AI, such as exploring different datasets and metrics for studying bias.

Highlights

The importance of considering bias in the data collection process and its impact on model development.

Questioning the representativeness of collected data across different demographics to identify potential bias.

The role of annotators' beliefs and geographical background in data labeling and its influence on model bias.

The potential bias introduced when using accuracy as a metric on unbalanced data during model training.

The challenges of deploying models in production and the unpredictability of user interactions.

The concept of 'jailbreaking' and its implications on how users might misuse or misunderstand model outputs.

The phenomenon of users perceiving bias where there may be none, influenced by personal experiences and preconceptions.

The feedback loop between user behavior and further data collection, and its role in perpetuating or correcting bias.

An example of a vision model output that raises questions about the prompt and the model's understanding of 'Indian person'.

The variability in prompts and outputs from vision models when different countries are specified, highlighting cultural bias.

The need for critical thinking when using AI models to understand and question the outputs and their implications.

The encouragement for hands-on practice to explore bias in AI through provided codebases and datasets.

The availability of supplementary video content and resources for a deeper understanding of bias in AI.

The upcoming module's focus on datasets and metrics for studying bias, and the ongoing research in this area.

The importance of engaging with TA and live sessions for support and discussion on bias in AI models.

A call to action for participants to continue exploring bias in the next module, emphasizing its significance in AI development.

Transcripts

[Music]

now let's look at some source of

bias uh this is an interesting

interesting diagram to see where all the

biases could come in how the biases

could come in Also let's go from left to

right first is uh collecting data right

that's a process meaning if you go back

to our module one the way we do is that

look we have to collect data we have to

take data to build this model so while

collecting this data is the data

representative of all

demographics that's a question that we

should

ask and the way these questions are

framed also please keep in mind it's

framed around the question of bias next

you are actually labeling the data to

build the model

while labeling who are the annotators

what about their beliefs which part of

the world are they

from next training using chosen metrics

and

objectives So you you're building a

model right using the using The

annotation that we that you got uh

training on bias data what if accuracy

is used as a metric on unbalanced data

it could be biased again

right model deployed in production so

we're going from here to here model

built uh collected data labeled and then

training model and now it's put in

production so what about what would

happen if users try to jailbreak

chbt next users see on effect what if

users perceive something as biased when

it is

not so this example I already told you

also about uh School uh kids looking at

this and then probably they perceive

that the world is that way some times

they're going to actually perceive and

think that it is biased when it is

not and then the last one is user

Behavior informs forther data collection

so understanding from uh how the users

are then there's a feedback loop that

can be given but you can see right every

part of the uh pipeline or aim ml

pipeline that you can think of there

could be bias that is creeping in which

is the uh aim of having this uh slide

here

here's another task for you uh here is a

image that came out of a vision model

I'm going to request you to think of

what could have been the prompt uh for

this pause the video and think of what

could be the prompt for this particular

uh output interestingly many many uh

sessions that I've done people would say

that uh

sadu uh

[Music]

Indian uh with

beard

men

turban uh right so all of that I've seen

people say uh but just to highlight also

there's a women here uh and

uh yeah so this is this is what these

are the kinds of prompts that people

have uh suggested before but

interestingly the The Prompt that was

given was an Indian person

and I'm not too sure whether any of you

are uh like this as you see the video or

any of you uh live with people like this

maybe I don't actually uh look like this

or wear these turban and all so it's not

clear to me what Indian person is this

model referring to some of the people uh

I know some of faculty and researcher

actually are working on these Vision

model bias itself here's another

interesting one uh which says that

prompt is a photo of a house in if you

do us you you get this China you get

this India you get

this and again the question I would ask

generally is that look to to any of us

live in such a house at least I

don't uh so so what is it representing

is not

clear and of course these are questions

that we should ask I don't think so the

goal is uh that these models are wrong

and we should not be using it I think

the go goal intent here is for you to

understand that when you use this models

and when you get these outputs you can

think of these questions uh that will

help you uh think about these bias

questions uh here is uh Hands-On so this

this part of the uh module has handson

also so please look at the YouTube

description or the course website which

will give you a link to the collab uh

code uh or some code base you will

get please try it out in this case

you're going to look at

uh bias uh this handson will walk you

through the way that people have done uh

created different data sets for studying

bias and some metrics for studying bias

also we'll also see some of them as part

of the course

itself but uh take a make sure that you

actually uh do this handson as part of

this uh bias module please watch the

supplement video content uh please do

watch the video take the code and do it

yourself uh there are these are simple

ones if you have any trouble reach out

uh but we'll also do a TA and a live

session for the same uh thank you for

watching this module hope to see you in

the uh next module uh which will be also

in bias we'll continue looking at bias

uh but as I said we look at uh data

sets uh we look at

metrics and a lot of research work that

is going on on this part yeah see you

soon

[Music]