3 types of bias in AI | Machine learning
Summary
TLDRThe video discusses the role of bias in machine learning, explaining how human biases can influence technology. It compares traditional programming, where solutions are hand-coded, to machine learning, where computers learn from patterns in data. Despite the data-driven approach, human biases can still seep in through interaction, latent, and selection bias. Examples include drawing shoes or recognizing physicists based on historical data. The video emphasizes the importance of addressing these biases and ensuring technology, such as search algorithms, works fairly for everyone.
Takeaways
- 👟 Machine learning can inherit human bias, even if it's unintentional.
- 🤖 Machine learning powers many technologies like navigation, suggestions, translation, and speech recognition.
- 👨💻 Traditional programming involves hand-coding solutions step-by-step, whereas machine learning allows computers to learn from patterns in data.
- 🧠 Data-based systems are not automatically neutral—biases can exist in the data used for training.
- 👀 Human biases, such as what we think a shoe looks like, can influence the machine learning models we create.
- 🎮 Interaction bias occurs when a machine learning model is trained based on a biased set of interactions, like people drawing a specific kind of shoe.
- 👩🔬 Latent bias can arise if the data used for training reflects past biases, such as training a model on physicists that skew heavily male.
- 📸 Selection bias happens if the data selected for training, such as face images, is not representative of the full population.
- 🚫 Companies are working to prevent machine learning from perpetuating negative biases, such as filtering offensive content or biased autocomplete suggestions.
- 💡 Solving bias in technology is a complex issue that requires awareness and input from everyone to ensure technology works for all.
Q & A
What is the game described in the script about?
-The game described in the script involves closing one's eyes and picturing a shoe, followed by the speaker showing different shoes to see if anyone had pictured them. It's used to illustrate how everyone has a bias towards one shoe over others.
How is this game related to machine learning?
-The game is related to machine learning because it demonstrates how our own biases can influence the way we teach computers to recognize objects, like shoes, which can lead to biased machine learning models.
What is machine learning?
-Machine learning is a subset of artificial intelligence that enables computers to learn from and make decisions based on data without being explicitly programmed to perform the task.
How does machine learning work?
-Machine learning works by allowing computers to find patterns in data and learn from them, as opposed to traditional programming where solutions are hand-coded step by step.
Why can't data be considered neutral?
-Data cannot be considered neutral because it can reflect the biases of the people who collected it or the biases inherent in the way it was collected.
What are some examples of biases that can occur in machine learning?
-Some biases that can occur in machine learning include interaction bias, latent bias, and selection bias. These biases can result from the way people interact with technology, the data used to train the models, and the selection of data used for training.
What is interaction bias?
-Interaction bias occurs when a machine learning model is trained based on the interactions of users with a system, which may not represent a diverse or unbiased sample.
Can you give an example of latent bias from the script?
-An example of latent bias from the script is training a computer on what a physicist looks like using pictures of past physicists, which would likely result in a bias towards men.
What is selection bias in the context of machine learning?
-Selection bias in machine learning refers to the bias that can occur when the data selected for training a model does not represent the entire population it is meant to serve.
What steps are being taken to prevent machine learning technology from perpetuating negative human biases?
-Steps to prevent machine learning technology from perpetuating negative human biases include tackling offensive or misleading information in search results, adding feedback tools for users to flag inappropriate suggestions, and raising awareness about the issue.
Why is it important for everyone to be aware of bias in machine learning?
-It's important for everyone to be aware of bias in machine learning because it helps ensure that technology works for everyone and does not unfairly disadvantage certain groups.
What is the role of feedback tools in addressing bias in machine learning?
-Feedback tools play a role in addressing bias in machine learning by allowing users to report and flag inappropriate or biased content, which can then be reviewed and corrected by developers.
Outlines
🎮 Let's Play a Game: Visualizing Bias
The speaker invites the audience to play a mental game, asking them to close their eyes and picture a shoe. They then challenge the audience by revealing different shoes, prompting a reflection on personal biases. The key point here is that everyone, without even realizing it, tends to favor one type of shoe over another. This exercise serves as an analogy for how biases can unknowingly influence machine learning when humans teach computers to recognize objects, introducing their own biases in the process.
🤖 What is Machine Learning?
The speaker introduces the concept of machine learning, explaining how it is embedded in various everyday technologies, such as navigation apps, recommendation systems, translation services, and voice recognition. They contrast traditional programming, where humans explicitly write the code, with machine learning, where computers learn by identifying patterns in data. The speaker begins to explore how biases can still arise in machine learning, even though it is based on data, because the data itself can reflect human biases.
⚖️ Bias in Machine Learning: It's Inevitable
The speaker delves deeper into how biases in machine learning emerge. Despite the perception that data-driven systems are neutral, human biases inevitably influence the data and the systems created. The speaker emphasizes that even with good intentions, human biases shape technology in multiple ways. They introduce three types of bias: interaction bias, latent bias, and selection bias, each contributing to how machine learning systems can inherit skewed perspectives from the humans who create and interact with them.
🎨 Interaction Bias: Drawing Shoes
The speaker uses a recent game where people were asked to draw shoes as an example of interaction bias. Most people drew conventional shoe designs, which caused the computer to recognize only those and disregard less common shoe types. This type of bias arises from how people interact with and feed data into the system, ultimately influencing what the computer learns to recognize based on the most frequent or typical inputs.
📚 Latent Bias: Physicists and Gender
Latent bias is explained with the example of training a machine learning algorithm to recognize physicists. If the training dataset consists primarily of images of male physicists from history, the system will develop a skewed perception, associating physicists predominantly with men. This kind of bias reflects deeper societal patterns that can become embedded in the algorithms when historical or imbalanced data is used.
📸 Selection Bias: Recognizing Faces
The speaker highlights selection bias using the example of training a model to recognize faces. If the dataset used to train the model is not representative of all demographic groups, the algorithm will not accurately recognize or serve everyone equally. Whether using images from the internet or personal photo libraries, it’s crucial to ensure diverse and representative data selection to avoid biased outcomes.
🛡️ Fighting Bias in Advanced Products
Since machine learning powers some of the most advanced technology products, the speaker explains how efforts are being made to minimize the perpetuation of negative human biases. Examples include curating search results to remove offensive or misleading content and adding feedback tools that allow users to flag inappropriate suggestions. The issue is complex, and while there is no single solution, awareness and proactive steps are key to addressing bias in technology.
💡 Awareness is the First Step
The speaker concludes by encouraging everyone to be part of the conversation about bias in technology. They emphasize that the first step toward solving the issue is becoming aware of how human biases influence machine learning. By actively discussing and addressing these biases, the technology we create can work more equitably for everyone.
Mindmap
Keywords
💡Bias
💡Machine Learning
💡Interaction Bias
💡Latent Bias
💡Selection Bias
💡Traditional Programming
💡Patterns in Data
💡Feedback Tool
💡Search Results
💡Technology for Everyone
Highlights
Introduction to a game to illustrate bias in recognizing a shoe.
Discussion on how our inherent biases can influence machine learning.
Definition of machine learning and its prevalence in modern technology.
Explanation of how traditional programming differs from machine learning.
The process of computers learning solutions by identifying patterns in data.
The inherent issue of human bias in data-driven technologies.
Examples of interaction bias in a shoe drawing game.
The concept of latent bias in machine learning models.
The impact of selection bias on the diversity of data used for training.
The importance of preventing machine learning from perpetuating negative human biases.
Efforts to tackle offensive or misleading information in search results.
Introduction of a feedback tool to flag inappropriate autocomplete suggestions.
Acknowledgment of the complexity of addressing bias in machine learning.
The call for awareness and collective conversation to improve technology.
The goal of technology to serve everyone without bias.
Transcripts
SPEAKER: Let's play a game.
Close your eyes and picture a shoe.
OK.
Did anyone picture this?
This?
How about this?
We may not even know why, but each of us
is biased toward one shoe over the others.
Now, imagine that you're trying to teach a computer
to recognize a shoe.
You may end up exposing it to your own bias.
That's how bias happens in machine learning.
But first, what is machine learning?
Well, it's used in a lot of technology we use today.
Machine learning helps us get from place to place,
gives us suggestions, translates stuff, even
understands what you say to it.
How does it work?
With traditional programming, people
hand code the solution to a problem, step by step.
With machine learning, computers learn the solution
by finding patterns in data, so it's
easy to think there's no human bias in that.
But just because something is based on data
doesn't automatically make it neutral.
Even with good intentions, it's impossible to separate
ourselves from our own human biases,
so our human biases become part of the technology
we create in many different ways.
There's interaction bias, like this recent game
where people were asked to draw shoes for the computer.
Most people drew ones like this.
So as more people interacted with the game,
the computer didn't even recognize these.
Latent bias-- for example, if you were training a computer
on what a physicist looks like, and you're using pictures
of past physicists, your algorithm
will end up with a latent bias skewing towards men.
And selection bias-- say you're training a model
to recognize faces.
Whether you grab images from the internet or your own photo
library, are you making sure to select
photos that represent everyone?
Since some of our most advanced products use machine learning,
we've been working to prevent that technology
from perpetuating negative human bias--
from tackling offensive or clearly misleading
information from appearing at the top of your search
results page to adding a feedback tool in the search bar
so people can flag hateful or inappropriate
autocomplete suggestions.
It's a complex issue, and there is no magic bullet,
but it starts with all of us being aware of it,
so we can all be part of the conversation,
because technology should work for everyone.
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