Important points
Machine learning fairness is an important consideration when developing machine learning models to prevent bias within the model.
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What is fairness in machine learning?
Machine learning fairness is the process of correcting and eliminating algorithmic biases (such as race, ethnicity, gender, sexual orientation, disability, and class) from machine learning models. Machine learning is a branch of artificial intelligence (AI) that stems from the idea that computers can learn from collected data to identify patterns and make decisions that mimic human decisions with minimal human intervention.
Watch this video for an overview of algorithmic fairness.
Why is it important to address fairness in machine learning?
Unintentional discrimination in machine learning algorithms is just one reason why addressing fairness and AI ethics is important.
Machine learning is built into the systems and applications we use to buy furniture, find jobs, hire new jobs, apply to colleges, listen to music, take out loans, find news, search on Google, target ads, and more. This strengthens our ability to streamline information and provide recommendations, but it can have serious consequences if we are trained on misinformation and fail to promote fair and equal practices.
Eliminating these potential biases requires data scientists and machine learning experts to find and correct them in algorithmic models. Machine learning, by definition, learns by example, so it can also “learn” to avoid bias as long as it is fed the right data. With machine learning used in a variety of industries, from the criminal justice system to corporate human resources to credit lending, it’s important to have fair and ethical processes.
read more: Understanding AI bias
Case study: COMPAS controversy
COMPAS is a decision support tool developed by Northpointe and used in the U.S. court system to assess the likelihood that an offender is a repeat offender (recidivist). The algorithm sparked controversy by taking a quantitative approach to fairness and predicting which offenders were most likely to reoffend.
3 ways to promote fairness in machine learning
For those working in data science and artificial intelligence using algorithms, there are several ways to ensure that machine learning is fair and ethical. you can:
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Tools for machine learning fairness
There are a wealth of courses, tools, and processes available to help you integrate machine learning fairness into your organization’s workflows and prevent machine learning fraud. Here are some things you can check:
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IBM AI Fairness 360: A Python and R toolkit of technical solutions for fairness metrics and algorithms that help users and researchers share and assess discrimination and bias in machine learning models.
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Google What-If Tool: A visualization tool that examines the performance of a model on a dataset and evaluates it against preset definitions of fairness constraints. Supports binary classification, multiclass classification, and regression tasks.
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Google model card: This tool ensures that the intent of a particular model matches its use case and helps users understand the conditions under which the model is safe and appropriate to proceed with.
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Fair Learn: An open-source Python toolkit to assess and improve the fairness of machine learning. Equipped with an interactive visualization dashboard and unfairness reduction algorithms, this tool helps users analyze tradeoffs between fairness and model performance.
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deon: An ethics checklist that promotes responsible data science by evaluating and systematically reviewing applications for potential ethical implications, from the early stages of data collection to implementation.
Explore free resources for machine learning
Want to learn more about machine learning skills? Check out free resources like the LinkedIn newsletter and Career Chat to stay up to date on the latest trends in the industry. You can also stay up to date with the free resources below.
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