As artificial intelligence (AI) continues to expand its reach into virtually every aspect of our lives, an important ethical question arises: how do we ensure that bias is not present in machine learning?
After all, if AI is deeply embedded in technologies that shape our lives and decisions, it must work with integrity and fairness.
In this article, I’ll explore the impact of bias in machine learning and discuss the ethical considerations surrounding this ever-growing technology.
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Let’s cover the following:
- What is bias in machine learning?
- Types of machine learning bias
- How to check for bias in machine learning
- How to eliminate bias in machine learning
- Ethical challenges in AI
- How to learn to use generative AI ethically
Read on to find out more about this growing concern of bias in machine learning.
1. What is bias in machine learning?
Bias in machine learning can be defined as a process whereby an algorithm or set of algorithms produces results that are unfairly prejudiced toward certain groups.
Essentially, it means that the algorithm is not able to accurately represent the entire population, instead skewing its output to benefit certain individuals or groups over others. This can lead to discrimination and marginalization.
This can be a huge issue wider than just the project itself, as CareerFoundry’s senior data scientist Tom Gadsby explains in this short video:
To give you a simple example, think of machine learning as a robot that’s learning from a book.
If the book has more chapters about apples than oranges, the robot will think apples are more important or common. This is bias.
If the robot only learned from this book, it might unfairly favor apples in its decisions. This can be a problem, especially if the robot is supposed to treat apples and oranges equally.
In real life, these “apples and oranges” could be different groups of people, and the “book” could be the data we use to train the machine learning system. Bias in machine learning can lead to unfair results for certain groups of people.
2. Types of machine learning bias
To better understand how bias works, we’ll look at some common types of machine learning bias:
Selection bias
Selection bias occurs when the sample data used to train an algorithm is not representative of the population as a whole.
For example, if a machine learning system is trained using data from predominantly one race or gender, it could produce results that favor that group over others.
Algorithmic bias
Algorithmic bias can occur when the algorithms themselves are biased in their design.
For example, if an algorithm is designed to prioritize certain types of information over others, it can lead to unfair outcomes.
Confirmation bias
Confirmation bias occurs when algorithms focus on data that confirms pre-existing assumptions or beliefs rather than look at the data objectively.
This type of bias can lead to skewed results, as the algorithm is more likely to treat certain data points differently than others.
Exclusion bias
Exclusion bias is another type of bias that can occur when certain data points are excluded from the training algorithm.
This can lead to incomplete results or results that are unfairly skewed in favor of one group over another.
3. How to check for bias in machine learning
To ensure fairness and accuracy in your machine learning models, it’s important to check for biases before releasing them into production.
Here are some key steps you can take:
- Audit your data sources: Make sure you understand where your data is coming from and that it’s representative of the population at large. Multiple datasets can be used and their results can be compared for a fairer analysis.
- Analyze your algorithms: Use techniques such as sensitivity analysis or counterfactual reasoning to analyze your algorithms for potential biases.
- Monitor performance: Regularly monitor the performance of your models to ensure that they’re not producing inaccurate or unfair results.
- Conduct data governance: Establish processes such as data governance to ensure that your data is being collected, stored, and used ethically by the stakeholders who use the algorithm.
4. How to eliminate bias in machine learning
Once you’ve identified potential biases, there are several steps you can take to reduce or eliminate them:
- Balance your datasets: Make sure each dataset is balanced and representative of the population at large.
- Use multiple algorithms: Using different algorithms on the same data can help eliminate any biases that individual models may have.
- Adopt a fairness framework: Adopting an AI fairness framework can help ensure that your models are taking into account all factors in their decisions and reducing potential bias. This can especially remove selection bias.
5. Ethical challenges in AI
The ethical implications of machine learning and AI can be serious.
AI systems are increasingly being used to make decisions in areas such as medical diagnostics and criminal justice, meaning any biases present in the algorithms could lead to real-world consequences.
As such, it’s important to consider the ethical implications of any AI system before deploying it.
This could involve considering questions such as: Does this model treat all individuals equally? Could this decision disproportionately hurt certain groups? Is there a potential for abuse or manipulation?
Unethical AI examples
Unfortunately, there have already been examples of AI and machine learning algorithms that have caused harm.
Here are some examples of where AI is being used unethically:
1. Music industry
AI songs are becoming increasingly common in the music industry, with algorithms being used to write and produce tracks based on songs made by artists. This has some ethical implications, as it could potentially displace human creatives or push out certain genres of music.
Universal Music Group has also stepped in to say that AI music needs to be regulated. They have urged streaming platforms to clamp down on the unauthorized use of music from original artists.
If AI-generated music starts to rise in popularity, the implications for musical creativity could be drastic.
2. Crime prevention: COMPAS system
The COMPAS System is an AI trained with a regression model to predict the risk of a perpetrator and is used in fighting crime in Florida. The model was built to focus on accuracy, missing out on the unwanted bias made—showing a higher risk for individuals with a darker skin tone.
This example highlights the importance of considering ethical implications before releasing an AI system into applications with larger consequences like law and order.
6. How to learn to use generative AI ethically
Generative AI is a type of machine learning that creates new data. This could be anything from text to images or videos.
As we know, generative AI has many potential applications, but it also brings with it ethical considerations.
When deploying and using AI, it’s important to ensure that the output is not discriminatory or offensive in any way and follows ethical guidelines such as the General Data Protection Regulation (GDPR) in Europe.
You’ll also need to consider that the output of generative AI could be manipulated or abused and what measures can be taken to mitigate these risks.
Here are some general tips for using generative AI ethically:
- Be aware of existing ethical standards: Research and understand the ethical guidelines for whichever industry and region you’re working in, such as GDPR.
- Review your output carefully: Ensure that the output is appropriate and free from any bias or discriminatory behavior.
- Create a policy: Develop a policy to ensure that those using generative AI are aware of the ethical implications and how they’re expected to use it.
- Monitor closely: Monitor the output regularly to make sure that no unwanted biases are appearing in the data.
For example, when using AI tools for data analytics, you’re probably handling sensitive information, so you would definitely need to review your output codes before running the full analysis. A simple step like this is crucial in preventing accidental bias.
7. Wrap-Up
The ethical implications of bias in machine learning and AI are far-reaching and must be considered before building any system or algorithm. However, with the right tools and processes in place, it is possible to identify and eliminate bias in machine learning, as well as to use generative AI ethically.
We hope that this guide has given you an understanding of the ethical considerations that come with machine learning and generative AI and how to use them responsibly.
If you’d like to learn more about machine learning and AI, check out CareerFoundry’s Machine Learning with Python Course. Just as with the other tech courses they’ve been providing for the past ten years, students will be taught not just how to employ machine learning, but how to employ it ethically in their work.
For more related information on machine learning, do check out the following articles: