AI BIAS: The Real World Problem AI Is Hiding (2025 Updated)

Every new technology comes with its merits and demerits. As we can see AI is solving users problems in every industry but we should be more careful while believing AI’s decisions. One of the major issues we can focus on is AI Bias in algorithms.

Bias is not a new concept in ML. The AI itself make assumptions after learning from user generated data.

We can define AI Bias as “The systematic errors unintentionally hidden in AI algorithm that can lead to unfair treatment of individuals or groups based on gender, race, or socioeconomics status”.

Bias can be reduced if we define fairness in AI systems. In this blogpost we will understand the concept of Bias in AI algorithm. We will also discuss how can we tackle this problem through achieving fairness in AI systems.

Lets gets started!

Understanding AI Bias In Algorithm

AI bias is basically a problem in AI system that happens when the trained input data is limited, outdated, or unbalanced which leads to unfair or incorrect decisions.

These biases are not often created on purpose. Instead, they usually come from the data used to train the AI system. If that data reflects existing human biases—like racism, sexism, or favoritism—the AI can learn and repeat those same unfair patterns.

For example, if a hiring algorithm is trained using past company data that mostly includes male employees, it might unintentionally favor male candidates over female ones. If the facial recognition system is trained to favour lighter skin toned people, it may struggle to recognize people with darker skin tone.

Bias can come from several sources such as biased data, biased AI models, or biased human decision making. Each source can contribute to unfair decisions.

AI models depends on user generated content (such as chatbots collects data from user prompts, social media posts etc. ). An algorithm that is trained on institutional bias (A type of bias that unintentionally favour one group over other such as racism, sexism), can make the algorithm worse by only focusing on the data that is easily available on the internet, and may ignore the data that is difficult to fetch.

For example, it is easier to analyze Twitter data than more private or harder-to-access sources, so some social issues may get more attention than others just because the data is available.

Data that is available online or the data on which the AI is trained contains different characteristics about people, which are called features. These features contains sensitive areas such as race, gender, or age. These features than creates bias that can lead to unfair treatment. You may think that if we remove these features from training data then the bias can be removed. But this strategy will not work because, other correlated features (called as redundant encoding) may still act as a substitutes.

For example, your college or university may closely linked to socioeconomic background. If a hiring algorithm uses education history or socioeconomics background to make decisions, it might favor candidates from specific university or college while unfairly treated other universities or colleges.

In fact, keeping sensitive features in the data can actually help to build fairer systems. These features can also be linked to what the model is trying to predict. For instance, if a minority group prefers red cars, and red cars are also commonly chosen by aggressive drivers, a model might wrongly assume that minority drivers are more dangerous. This could lead to higher insurance rates for red car owners, which ends up unfairly affecting the minority group.

So, even features that seem neutral can lead to biased results if they are connected in the wrong way. To handle this problem, it’s important to understand the cause-and-effect relationships between different data features. This idea is already used in law and medicine, and researchers are now applying it to make machine learning models fairer.

Data can come in many formats, like numbers, text, images, or even a mix of these, such as videos with sound. Most fairness-focused machine learning (ML) methods work with structured data, where information is organized into fixed features.

However, there are also methods that deal with specific types of data, like text or images. Recently, researchers have found that language data can carry bias, especially in how certain words are linked to gender or race. For example, studies have shown that word embeddings (used to help computers understand language) often include unfair or offensive stereotypes.

The same issue exists in computer vision, where AI learns from image collections like MNIST or uses pre-trained models built on large datasets. These image sets are often assumed to fairly represent the real world, but in reality, they can be biased. This shows that bias can exist in the data itself. In multimodal AI systems that process several types of data at once, there’s often an extra step of turning raw data into features. This extra layer makes it even harder to find and fix where the bias comes from.

Bias in AI can show up in many areas, such as:

• Facial recognition systems that work better for some skin tones than others.
• Loan approval tools that give lower scores to certain groups of people.
• Healthcare algorithms that miss important symptoms in underrepresented populations.
• Recruitment software that filters out qualified candidates based on biased historical data.

To reduce AI bias or algorithmic bias, developers should use diverse and high-quality training data, regularly test algorithms for fairness, and include people from different backgrounds in the design and review process. By doing so, they can create AI systems that are more accurate, fair, and trustworthy for everyone.

You can see the Real-Life Example of Bias in training data i.e., Amazon’s Sexist Hiring Algorithm

In 2018, Reuters reported that Amazon developed an AI hiring tool to help screen job applicants, but it turned out to be biased against women. The system was trained on resumes submitted to Amazon over a 10-year period, most of which came from men, since the tech industry has been male-dominated.

As a result, the AI learned to favor male candidates and downgraded resumes that included words like “women’s” (such as “women’s chess club captain”) or that came from all-women’s colleges. Even though Amazon didn’t program the tool to be sexist, the bias in the training data led the AI system to make unfair decisions. Amazon eventually scrapped the tool after discovering the issue.

Reference:
Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.
Link to article

Let see the real world example of Bias in algorithm design i.e., Apple Card credit limit controversy

In 2019, several users, including tech entrepreneur David Heinemeier Hansson and even Apple co-founder Steve Wozniak, reported that Apple Card (issued by Goldman Sachs) gave them significantly higher credit limits than their wives, despite having shared assets and similar or better credit scores.

The issue wasn’t necessarily happened due to biased data, but rather how the algorithm was designed to weigh factors like spending patterns and credit history possibly in ways that unintentionally disadvantaged women.

Since Apple and Goldman didn’t disclose exactly how the algorithm worked, it raised serious concerns about transparency and fairness in algorithmic design. This example shows how an algorithm, even if not intentionally discriminatory, can still produce biased outcomes if they are not carefully designed and tested.

You can see the BBC report.

Biases in Proxy Data

The developers use “proxy data”, when the real data is not available easily. But as we know proxies don’t always represent the real thing accurately. For instance:

• Using ZIP codes as a proxy for income might reflect racial or economic segregation.
• Using school attendance as a proxy for future success might ignore factors like access to education or personal challenges.

Proxy data can introduce hidden bias because it’s not a perfect match for what the algorithm is really trying to measure.

Now, let us understand by the real world scenario that happened by using proxy data i.e., healthcare algorithm used in U.S. hospitals to predict which patients would benefit most from extra medical care.

This algorithm used healthcare spending as a proxy for a patient’s health needs. In theory, the more money spent on a patient, the more care they likely needed. But in reality, Black patients often receive less medical care than white patients due to long-standing systemic inequalities—not because they’re healthier.

As a result, the algorithm underestimated the health needs of Black patients, giving them lower risk scores and denying them the additional care they actually needed. Researchers found that if the algorithm had been based on actual health conditions instead of spending data, the number of Black patients identified for extra care would have more than doubled.

You can read the whole story here.

Biases in Evaluation

Sometimes, the thorough testing of AI system is not enough to label it biased proof. If the evaluation data used in training is not diverse or doesn’t reflect real-world conditions, the algorithm might seem accurate in testing but fail in practice.

• An algorithm tested only on English-language documents might perform poorly in other languages.
• If the test data doesn’t include edge cases or minority groups, performance results will be misleading.

Good evaluation must include a wide range of scenarios to ensure the AI works fairly for everyone.

A real-life example of bias in evaluation is seen in some facial recognition systems used by law enforcement.

A 2019 study by the U.S. National Institute of Standards and Technology (NIST) found that Asian and African American people were up to 100 times more likely to be misidentified than white men in facial recognition evaluations.

This happened because the evaluation process did not fairly represent all demographic groups, that leads to biased performance results and unfair real-world outcomes.

You can read the full article here to know the complete information.

How Can We Solve AI Bias in Algorithms?

In order to solve AI bias the developers should use diverse and representative training data. If AI is trained on biased or incomplete data, it will learn those same biases. To avoid this, developers must carefully select and balance datasets, making sure that they include different genders, races, ages, and backgrounds.

Regular audits of the data and algorithms should also be done to catch and correct any unfair patterns early. Open-sourcing datasets or making them transparent for peer review can also helps to ensure fairness from the start.

Another powerful solution is to build ethical guidelines directly into the development process. Companies should have clear standards for fairness, accountability, and transparency.

This includes that companies should test AI models in real-world scenarios and use the tools that explain how AI makes decisions (known as explainable AI).

By involving people from different communities and industries in the design and testing phase can also helps to spot bias before it becomes a problem. By combining better data, strong ethics, and continuous monitoring, we can create AI systems that treat everyone more fairly.

Fairplay AI Bias Funding Partners

Fairplay is the world’s first fairness as a service company that focused on AI fairness. Their mission is to reduce the hidden AI bias in algorithms to help the people of different color groups, women and other historical disadvantaged group.

Here the screenshot of their mission you can read:

My Experience: Detecting Bias in a News Recommendation Model for The Syntellix

While enhancing the recommendation feature on The Syntellix, I set out to ensure it treated all readers fairly. Using IBM’s AI Fairness 360 (AIF360) toolkit, I tested the model trained on user clicks and reading behavior. Initially, the model seemed accurate—it served up relevant articles based on what users had interacted with before.

However, fairness metrics revealed a hidden issue: the model consistently recommended fewer tech and AI news articles to users from certain demographics, even when their browsing habits matched those of others who received them. Digging deeper, I traced the problem to imbalances in the training data—early click patterns from a specific user group had skewed the model’s learning.

To fix this, I rebalance the dataset by oversampling underrepresented groups and adjusted feature weighting to ensure interest, not demographics, drove recommendations. After re-training, the model delivered a more balanced mix of relevant articles to all users, regardless of background. This process was a real reminder that bias can hide in plain sight, and tools like AIF360 are essential for uncovering and addressing it—even in small-scale, real-world systems like The Syntellix.

The Relationship Between AI Bias and Generative AI

Generative AI—like ChatGPT or image generators—creates content based on patterns in its training data, which means it can also reproduce and amplify real-world biases.

If the data has stereotypes (e.g., associating doctors with men or nurses with women), the AI may repeat them in its outputs. Worse, because generative AI seems objective, people may trust its biased results without questioning them. The fix? Better data, transparency in how models are trained, and ongoing testing to catch unfair patterns before they cause harm.

What Will Be The Future of AI Bias

The future of AI bias will depend on how we design and govern these systems today. While AI has the power to amplify existing inequalities, it also offers tools to detect and correct biases—if we prioritize fairness from the start.

We should expect tighter regulations, better bias-detection algorithms, and more diverse training data to minimize harm. But without accountability, biased AI could deepen discrimination in hiring, healthcare, and law enforcement.

Some Tools To Reduce AI Bias

Tool	Developed By	Purpose	Best For	Link
AI Fairness 360	IBM	Detects and mitigates bias in datasets and ML models with 70+ fairness metrics.	Auditing models pre- and post-deployment.	Visit
What-If Tool	Google	Interactive visual analysis of ML models to test fairness scenarios.	Exploring “what-if” bias edge cases.	Visit
Fairlearn	Microsoft	Assesses and improves fairness in AI systems (classification/regression).	Teams integrating fairness into workflows.	Visit
Aequitas	UChicago	Bias audit toolkit for datasets before model training.	Researchers analyzing data disparities.	Visit
Hugging Face Evaluate	Hugging Face	Benchmarks models for bias, ethics, and performance.	NLP-focused bias testing.	Visit

Conclusion: Navigating the Complex World of AI Bias

The real goal of artificial intelligence (AI) is to treat everyone fairly. While bias in AI is a real and serious issue, there are clear steps we can take to reduce it. We should use trusted tools like IBM’s Fairness 360 and Microsoft’s Fairlearn, create diverse teams and ethically review the data, we have the power to shape better, and build more inclusive AI systems.

The point here is that we should not totally rely on tools, instead we should totally monitor the hidden bias in AI systems over time. If you are a developer, policy maker, or the user of AI system, it is your ethical responsibility to raise your voice if you see anything wrong. Whether you’re writing code, making policy, or just using AI in your daily life, your voice matters. You should challenge the unfair results, push for transparency, and support AI that works for all—not just the majority.

The choice is ours. Let’s make it wisely.

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