3 types of bias in AI | Machine learning

Summary

The 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.