The Hidden Danger of AI Bias—And How to Avoid It

Bias in Artificial Intelligence (AI) has significant implications across many facets of our lives. More specifically, the algorithms developed to help AI learn and the data model upon which that algorithm depends need to be evaluated, iteratively, to avoid bias. Depending on the application, the result produced from AI could have significant ramifications. This article explores how machine learning algorithms function, reviews some common biases, and shares ways to identify, reduce, and eliminate bias in machine learning.

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Understanding AI, Machine Learning, and Bias

Artificial Intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP).  [1]

Machine Learning (ML) is a branch of artificial intelligence that enables algorithms to uncover hidden patterns within datasets. It allows them to predict new, similar data without explicit programming for each task. Machine learning finds applications in diverse fields such as image and speech recognition, natural language processing, recommendation systems, fraud detection, portfolio optimization, and automating tasks. [2]

Bias refers to a tendency or preference towards a certain group, idea, or concept that influences judgments and decisions. In AI, bias can arise at various stages, including data collection, algorithm design, and human interaction with AI models. [7]

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The Impact of AI Bias

Whether you realize it, or not, we are impacted by algorithms daily when we access the internet. Algorithms work by learning patterns and relationships from data to make predictions or decisions. [2] Whether you are using Google for a web search, wondering why particular advertisements are appearing in your browser, or receiving topical feeds from your favorite social media platform, algorithms curate content based on your interactions with the platform.

If you are like me, you have probably been shocked with how accurate an algorithm’s prediction can be. On the other hand, you may have laughed when a seemingly random song appears in your Spotify stream. Then you remember your child liking a Taylor Swift song while using your account on a road trip. Now, Taylor Swift and similar artists regularly appear in your Spotify feed because the underlying algorithm has reacted to new data. In this case, the incorrect song recommendation is merely annoying. However, if an AI model determines that a medical claim is "Not Medically Necessary," the impact on your finances, physical health, and mental well-being can be significant.

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Common Types of AI Bias

To ensure AI delivers the most accurate and fair outcomes, we must recognize how bias can be introduced—whether implicitly or explicitly—at different stages of model development. For statisticians, data analysts, or Lean Six Sigma practitioners, this extends the bias awareness already ingrained in data management. Below are three key areas where bias can emerge in AI models:

1. Data Bias - Occurs when the dataset used is not representative of real world, leading to skewed AI predictions.
2. Algorithmic Bias - Results from assumptions made when designing an algorithm that introduce unintended favoritism.
3. Human Bias - Arises when developers or users unintentionally incorporate personal biases into AI models.

For further insight into data bias and its impact, refer to AI Bias: 8 Shocking Examples and How to Avoid Them.

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Strategies to Mitigate AI Bias

• Evaluate Training Data – Take the time to understand where your training dataset comes from and whether it introduces implicit bias based on the tool’s intended use.
• Use Diverse Data Sets – Ensure the data used to train the algorithm is representative of all groups to minimize systemic biases.
• Leverage Explainable AI (XAI) – Utilize transparent models to understand and communicate how your AI makes decisions and the assumptions applied.
• Conduct Regular Audits – Test and monitor AI models consistently to ensure they perform equitably across different user groups.
• Implement Bias Mitigation Techniques – Use "blind taste tests" where the algorithm is denied information suspected of biasing the outcome. [5]
• Utilize Bias Detection Tools – Consider using AI Fairness 360, Fairlearn, or Fair Test to detect and mitigate bias.
• Stay Updated – Follow the latest research and best practices in AI ethics and bias reduction.

For further guidance on mitigating AI bias, explore: AI Bias - What It Is and How to Avoid It? or check out 5 Ways to Avoid Artificial Intelligence Bias with 'Responsible AI'.

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Final Thoughts

As with most critical business decisions, seek experience to help determine the most appropriate path to solve your business challenge. Like the complexity of choosing a new Electronic Health Record (EHR) vendor, AI solutions are plentiful, have different strengths and weaknesses, and varying applications. AI is rapidly expanding across healthcare, from NLP-driven documentation and coding to diagnostic imaging. If your organization is considering an AI solution, educate yourself with the resources from this article, clearly define the problem you are facing, and engage expertise to guide your journey.

At Coker, we specialize in guiding organizations through AI adoption, ensuring they implement ethical and effective AI solutions. Contact us to learn how we can help you mitigate bias and optimize AI tools for your business.