What to Know About Bias in AI Algorithms

Bias in AI algorithms reflected in inconsistent recommendations or results across different user profiles.

Our online lives are controlled by invisible AI (Artificial Intelligence) algorithms that prompt our search results, social media posts, and the articles that pop up in our newsfeeds. The inherent bias in AI algorithms can dictate what we see, what information we consume, and the research we utilize.

The content we produce, too, lives in service of the almighty AI algorithm. If we hit the “right” notes, use the right SEO tools, and optimize the right information and word combinations, millions of people might see our content. Alas, if we fail to understand or use algorithms to inform the content we produce, our work may fade into the abyss and not be favored by AI’s optimization.

Biases in algorithms can impact our personal lives as well. AI algorithms aren’t biased on their own — but the input they’re trained on can cause discrimination in numerous sectors and impact our ability to get a financial loan, proper medical diagnosis, and more.

AI algorithms can benefit brands, businesses, content creators, and individual Internet users, but the inherent bias found in AI algorithms can present challenges as well. Where do these biases come from, and what can we do to fix them?

What are AI algorithms?

If you’ve ever produced online content or posted on social media, you’re probably familiar with AI algorithms. But you might wonder what, exactly, algorithms are and how do they operate?

AI algorithms are the specified sets of instructions that allow computers to analyze data, learn, and grow their knowledge base, and make autonomous decisions. Algorithms allow AI to perform human-level tasks like natural language processing, informed insights, pattern recognition, and prediction based on data sets.

How AI algorithms work

AI algorithms work by identifying and processing patterns in data. This allows algorithms to make decisions and predictions, discover hidden patterns, and learn through trial and error or labeled data.

These algorithms train on data and adjust mistakes to learn to make accurate predictions and provide correct responses.

IT professionals can use AI models and algorithms to perform numerous tasks — from predictive text to language processing, image detection and creation, prompt responses, and so much more. Human experts create AI algorithms to do the following:

Follow Instructions for Learning: Humans provide the guidelines and steps an AI algorithm needs to take in order to learn from data input.
Data Analysis: AI algorithms allow computers to analyze and process extremely large datasets to identify data patterns and trends, and train for learning and decision-making.
Make Autonomous Decisions: After training on data input, AI algorithms can create classifications and predictions, and make autonomous decisions based on learned data.

Types of AI algorithms

AI algorithms are categorized by how they learn and what tasks they can complete. Generally, there are three universally recognized types of AI algorithms: Supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms. Here’s a closer look at each:

Supervised Learning: AI algorithms trained by supervised learning are given a dataset input that is paired with an output label. For example, in an AI image algorithm, an image of a cute puppy might be labeled “dog” for corresponding output labels. The algorithm then can predict when other images should also be labeled “dog.”
Unsupervised Learning: The input given to AI algorithms trained on unsupervised learning doesn’t contain predefined labels or output. The algorithm then identifies hidden patterns and relationships within datasets.
Reinforcement Learning: An AI algorithm trained on reinforcement learning interacts with a set environment, such as a self-driving car, and receives either rewards or penalties for its behavior, based on long-term goals.

For instance, if a self-driving car is responsible for a collision, its AI might be punished in order to learn similar circumstances in the future.

Examples of subcategories of AI algorithms

Here are some examples of subcategories of AI algorithms:

Decision Trees: Decision trees split data into subsets based on shared features, in order to make predictions. For example, FitBits use decision trees to determine your level of fitness and successful workouts.
Linear Regression: Linear regression predicts consistent data values. For example, AI might determine current prices on consumer goods by using linear regression.
Logistic Regression: Logistic regression classifies data and helps increase cybersecurity. For example, logistic regression can determine and automatically block spam emails.
Neural Networks: Neural networks are structured like the human brain and programmed to process information in a human-like manner. For example, Google search uses a neural network algorithm.
Natural Language Processing (NLP): NLP allows computers to process, understand, and generate human language. For example, ChatGPT uses an NLP learning AI algorithm.
Random Forests: Random forests create multiple decision trees trained on specific sets of data and then combine learning from each tree to allow an algorithm to make a decision. For example, a random forest might be used to create a strong email filter.

AI algorithms categorized into supervised, unsupervised, and reinforcement learning types.

How AI algorithms impact digital content

Implementation of AI algorithms can also significantly impact digital content by automating content creation, influencing the visibility of your content, completely controlling user experiences, personalizing content recommendations, and promoting content moderation.

Unfortunately, they can also create challenges like bias in AI algorithms, viral misinformation, and echo chambers that promote confirmation bias in visible content. There are growing ethical concerns about AI algorithms and the inherent flaws they may contain due to biased training materials.

What is AI algorithm bias?

The bias in AI algorithms stems from AI training and causes inadvertent (and sometimes purposeful) discrimination due to inherent flaws in data input or algorithm design. AI algorithm bias can lead to adverse consequences across a wide spectrum of applications and impact financing, healthcare, recruitment, criminal justice, and more.

Types of bias in AI algorithms include (but aren’t limited to):

Data bias: Due to societal biases that are reflected in training data that result in biased algorithm output.
Design bias: Human error in an algorithm’s design and implementation can cause biased output.
Selection bias: When a target audience of an AI algorithm is not represented by the training data used.
Undersampling or oversampling: Using an overabundance of data that represents a small sample size or undersampling valuable data can cause algorithm bias. For example, deciding that the word “inclusion” is always negative may prevent AI from providing accurate results of any prompt that includes this word.

Real-world examples of AI algorithm bias

According to IBM, more than 180 human biases have been identified and found to impact AI algorithm training. Here are some real-world examples of how bias in AI algorithms has had negative consequences:

The racial profiling of COMPAS: According to research from ProPublica, Northpointe’s COMPAS (Correctional Offender Management Profiling for Alternative Solutions) criminal justice tool, incorrectly flagged defendants of color to have a higher risk of recidivism than their white counterparts, based on inaccurate training.
Financial discrimination: In an interview with CNBC, the former head of Twitter’s machine learning ethics, transparency, and accountability, Rumman Chowdhury, explained that bias in AI algorithms can lead to lending discrimination against minorities and marginalized communities based on geographical risk and other unfair discriminatory factors that don’t encompass an individual’s loan-worthiness.
Misdiagnosis of health issues: Biased algorithms might misdiagnose women or under-refer patients from minority and underrepresented communities. For example, a class action lawsuit was filed against UnitedHealth in 2023, accusing the healthcare giant of rejecting Medicare Advantage elderly patients based on an illegal and biased AI algorithm.
Recruitment discrimination: AI algorithms used in resume screening might favor resumes with masculine language. For example, Amazon scrapped its AI algorithm recruitment tool when it was found to use sexist screening tactics.

AI development team reviewing training data for fairness and accuracy.

3 ways to fix AI algorithm bias

The good news is that AI algorithms work based on the training data they’re given to learn from, and there are steps to prevent negative bias from overwhelming these tools. Although AI may become fully autonomous in the future, AI models still require human oversight in training data input.

Thus, human mitigation of overtly biased information can help erase bias in AI algorithms too. Here are several ways we can fix bias issues.

Balance Data

Feeding an AI algorithm data that fully represents its target audience by gathering information from underrepresented groups and a wide variety of demographics can help to reduce AI bias.

Build Fair Algorithms

Building algorithms that create equal outcomes for different demographics can help reduce bias. By ensuring that an individual (or group) isn’t prohibited from favorable outcomes based on their race, gender, or economic status, AI algorithms can operate without bias.

Use AI Bias Detection Tools

AI bias detection tools like IBM’s AI Fairness 360 (AIF360) can check for unwanted bias in AI datasets and machine learning models and help to correct these biases. AIF360 contains three tutorials on how to mitigate bias in classifying facial images by gender, credit scoring, and predicting medical expenditures.

Bias detection tools can help to prevent the negative consequences of unwanted bias in AI algorithms, and many trusted brands, like Intel, use these tools to help protect you from experiencing negative AI algorithm biases.

Although many businesses are taking proactive measures against adverse bias in AI algorithms, it’s important to be aware that these biases still exist.

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