As technology advances, AI-based systems increasingly play a role in decision-making processes and automating actions that previously required human intervention. A few years ago, Artificial Intelligence was just a concept, a futuristic technology far from becoming a reality. Nowadays, it has permeated many aspects of our lives and is present in almost all walks of life, from healthcare to the automotive industry.
While this is beneficial in many ways, it also brings an inherent risk — AI or algorithmic bias. Since AI-based systems replicate human thought processes, AI will inevitably be subject to the same human biases people may carry in decision-making, such as gender and racial bias. AI bias can manifest in facial recognition, chatbots, and risk assessments and have severe implications for businesses and society.
But what exactly is AI bias, and how can you prevent it in your data? This article will answer these questions and provide some actionable tips for companies to take to ensure AI algorithms are free from bias.
What Is AI Bias?
AI bias, sometimes called algorithm bias or machine learning bias, is a phenomenon where the algorithm used for decision-making has systematic errors based on preconceived notions and stereotypes. AI bias is similar to human biases and can lead to inaccurate decisions. The issue of AI bias arises when an algorithm draws conclusions that are too narrow or based on training datasets that do not accurately reflect the population under study.
AI bias can manifest in many ways, from subtle influences to large-scale errors and false assumptions. For example, societal bias against women could result in creating AI systems that are more likely to favor male candidates over female ones when making hiring decisions. Similarly, cognitive bias against darker-skinned women in the healthcare industry could lead to AI systems that are more likely to misdiagnose darker-skinned women than lighter-skinned ones.
Studies also indicate that AI facial analysis technologies have a higher misidentification rate for people with darker skin tones. This is evident in a Stanford University Human-Centered AI Institute study, where contrastive language-image pretraining (CLIP) misclassified Black individuals as nonhuman two times more often than any other race. This is a classic case of AI racial biases in facial recognition software. Still, the previous year's study indicated that AI language processors misunderstood Black people, particularly Black males, twice as often as White people.
Types of Bias in AI
AI bias can take many forms and is not always easy to spot. However, we can broadly divide bias in AI into two categories.
Algorithmic Bias
Algorithmic bias is when an algorithm produces unfair or incorrect results because of the underlying datasets and assumptions made by the programmer. This type of bias can occur when the data used to train the algorithm contains biases. For instance, if a data set focuses on a particular demographic group and doesn’t include other populations, the algorithm will produce results that reflect the bias in the data set. Thus, this will skew the fairness metrics of the algorithm.
Data Bias
Data bias is when a data set used to train an AI algorithm contains errors or biases. This type of bias can arise from many sources, such as data collection methods, data cleaning processes, and inherent biases in the source data. For example, suppose a dataset used to train an AI system to predict customer behavior has an over-representation of certain genders or ethnicities. In that case, the algorithm's results will favor those groups.
AI Bias Examples
Because AI has become rampant and affects many aspects of our lives, trustworthy AI technology must be as fair and unbiased as possible. Bias in artificial intelligence can have real-world implications, such as unfair treatment of individuals or groups, and can lead to inaccurate predictions or decisions. While utilizing artificial intelligence can be powerful and beneficial, it's important to understand the pros and cons of AI, such as its many biases, before leveraging AI systems.
Below is a closer look at how procedural fairness, or the lack thereof, can affect people in different industries.
- Financial Services: AI is increasingly present in financial services firms, helping them decide about loan approvals and credit ratings. If the algorithm used for decision-making has societal biases, certain applicants may be unfairly denied a loan or given an inaccurate credit rating. For instance, an AI algorithm for loan approvals that uses a data set with data from predominantly White people could lead to unfair loan denial for people of color.
- Education System: Organizations also use AI to decide student admissions to schools and universities. If the algorithm used for decision-making is biased, some students may be unfairly excluded or accepted into an institution based on their resume. For example, if you train an AI algorithm for admissions using a dataset biased toward a particular gender or race, it will skew the admissions process toward those groups.
- Law Enforcement: Misidentification and wrongful arrests due to facial recognition technology are serious concerns. AI bias in facial analysis technologies could lead to false positives that result in law enforcement arresting and charging the wrong person. This is especially significant for people of color, who are already over-represented in arrests and prison populations.
- Media: AI algorithms often dominate the news articles that appear in search engine results. This means news articles with certain biases can receive more prominence than others, resulting in biased news not representative of the population.
How to Reduce Bias in AI
The need to reduce bias in AI is becoming increasingly important as the use of artificial intelligence increases. These are some steps your organization can take to keep algorithms free from discrimination.
Don't Rely Solely on Real-World Data
It's important to note that real-world data may contain unintentional human and societal biases and should not be the only data source for training AI algorithms. Instead, organizations should use a mix of real-world and synthetic data (artificially generated) to ensure the training dataset is as accurate and unbiased as possible. For instance, synthetic data from a generative adversarial network (GAN) can create more diverse and balanced training datasets.
Evaluate Data Regularly
AI models rely on datasets that reach back several years when the definition of fairness was significantly different. However, the world constantly changes, and historic datasets often do not accurately illustrate present conditions. Similarly, AI models designed by today's standards may be outdated tomorrow due to technological and environmental changes. Thus, algorithms that appear unbiased at this moment could become rife with prejudice in the future.
Additionally, datasets may have errors that can lead to bias in AI. To ensure that mistakes don't creep into the data, your organization should regularly evaluate its data sources for potential omissions. You can do this by using automated checks, such as sentiment analysis and data anonymization, to detect potential bias or errors in the training dataset.
Employ a Diverse Data Team
Since AI algorithms replicate human thought processes, the people designing and training the algorithm must have various backgrounds and perspectives. A diverse data engineering team, with members from different cultures and genders, will be better equipped to identify potential bias in the algorithm and create a more accurate training dataset.
Additionally, having a diverse data team can help your organization understand different populations and create algorithms more reflective of different perspectives.
Prioritize Transparency
AI algorithms can be incredibly complex, and it can be challenging to identify biases without an in-depth understanding of the data set and how the algorithm works. To ensure algorithmic fairness, organizations should prioritize transparency and clearly explain the decision-making process behind their AI algorithms.
Your organization should also provide users with an explanation of how it makes decisions using responsible AI algorithms and a method to contest unfair ones. It will help users feel more comfortable with the algorithm's fairness and create trust in your organization.
Test Your AI and Machine Learning Models Consistently
Your organization should regularly test its AI software and machine learning systems to ensure the algorithms mitigate bias and can make accurate decisions. You should conduct this testing before and after deployment and include tests to check for discriminatory outcomes based on gender, race, and other factors.
Additionally, you should compare the algorithm's results to those produced by humans and ensure that the algorithm makes fair decisions.
Only Use Ethical Model Frameworks
Ethical model frameworks guide how to design and deploy AI responsibly. They focus on preventing bias in AI and include principles such as transparency, accountability, fairness, privacy, security, and data protection.
For instance, the Artificial Intelligence Ethics Framework for the Intelligence Community is a framework created by the U.S. intelligence community that provides guidelines for developing and using ethical AI. It highlights the importance of fairness, accuracy, and safety when developing AI best practices.
Help Prevent AI Bias With Nearshoring Developers
AI bias is a real and pressing issue. Fortunately, there are technical tools and processes that your organization can use to help reduce bias in its AI models. One way to ensure that your algorithms are free from bias is to invest in talented, nearshoring developers. Nearshoring developers can help you create more accurate algorithms that reflect the population by bringing different perspectives.
Is your organization looking for experienced, pre-vetted nearshoring developers? Revelo can help you hire remote software engineers in Latin America. Our talent marketplace has highly skilled and qualified developers from various cultures, backgrounds, and skill sets.
Contact us today to learn how we can help you hire nearshoring developers and reduce AI bias in your organization’s algorithms.
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