3 types of bias in AI | Machine learning

Google

25 Aug 201702:33

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

00:00

🎮 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

Bias refers to a prejudice or predisposition toward a certain perspective or outcome. In the video, bias is discussed in the context of both human and machine learning systems. The speaker highlights how personal biases can influence the data used to train algorithms, leading to skewed results. For instance, a machine might not recognize certain types of shoes if it has only been exposed to specific examples during its training.

💡Machine Learning

Machine learning is the process by which computers learn to solve problems by analyzing patterns in data, rather than being explicitly programmed step-by-step. The video explains how machine learning is widely used in various technologies, like GPS navigation and language translation, but also highlights the potential for bias in machine learning models based on the data they are trained on.

💡Interaction Bias

Interaction bias occurs when human interaction with technology reinforces certain patterns or outcomes. In the video, this is illustrated through a game where people were asked to draw shoes. Because most participants drew a specific type of shoe, the machine learned to recognize those and became unable to identify other kinds of shoes, demonstrating how user behavior can shape a machine's learning process.

💡Latent Bias

Latent bias refers to the hidden or unconscious biases that emerge in machine learning models based on historical data. For example, if a machine is trained on images of physicists from the past, it may assume that physicists are predominantly male, since most historical figures in that field were men. This illustrates how past patterns can lead to biased algorithms in current technology.

💡Selection Bias

Selection bias arises when the data used to train a machine learning model does not represent a broad or diverse sample. In the video, the speaker discusses how training a model to recognize faces might result in bias if the photos selected are not representative of all demographics. This can lead to models that perform poorly for underrepresented groups.

💡Traditional Programming

Traditional programming involves humans writing explicit instructions for computers to follow step-by-step. The video contrasts this with machine learning, where computers identify solutions by detecting patterns in data. This distinction is important because it highlights how machine learning's reliance on data can introduce bias, whereas traditional programming may reflect the bias of the human programmer.

💡Patterns in Data

Patterns in data are the recurring relationships or trends that machine learning algorithms use to 'learn' and make predictions. The video underscores how finding patterns in data is key to machine learning, but also how those patterns can be influenced by biased or incomplete data. For instance, if a machine is trained only on specific kinds of shoes, it will learn patterns based on those examples and fail to recognize others.

💡Feedback Tool

A feedback tool is a feature that allows users to report or flag inappropriate or misleading content generated by technology. In the video, the feedback tool is mentioned as part of an effort to prevent negative biases in machine learning applications, such as biased autocomplete suggestions in search engines. This tool helps refine the system by incorporating user input.

💡Search Results

Search results refer to the information that appears when a user enters a query into a search engine. The video mentions how machine learning is used to determine which results appear at the top of the list, but it also warns of the risk of bias if the algorithm ranks offensive or misleading information too highly. This illustrates the challenge of ensuring search engines provide balanced, unbiased results.

💡Technology for Everyone

The concept of 'technology for everyone' emphasizes the idea that technological systems should work equitably for all users, regardless of their background. The video promotes this notion by highlighting efforts to reduce bias in machine learning, ensuring that all people are represented fairly in the data used to train these systems. This goal is central to the video's message of creating inclusive, unbiased technology.

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.