Source of Bias
Summary
TLDRThis 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
🔍 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.
📚 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.
Mindmap
Keywords
💡Bias
💡Data Collection
💡Representative Data
💡Labeling
💡Model Training
💡Metrics
💡Model Deployment
💡User Perception
💡Feedback Loop
💡Vision Model
💡Hands-On
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]
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