AI Bias: Recognizing and Addressing Algorithmic Prejudice
Last updated: July 10, 2025 Read in fullscreen view
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| About the Author | Victor Ortiz | goodfirms.com | Victor Ortiz is a Content Marketer with GoodFirms. He likes to read & blog on technology-related topics and is passionate about traveling, exploring new places, and listening to music. |
Artificial Intelligence, or AI, is a technology that needs no introduction these days. It is being widely used in domains as diverse as education and warfare. As many technologies have their own pros and cons, AI is no different.
It is possible to create AI that is biased toward certain groups of people or even individuals. In this blog, we will examine this prejudice and how to address it.
So, let's dive straight in!
What is AI bias?
AI systems use algorithms to process vast amounts of data and find hidden patterns or insights. This is done to predict the output according to the input variables or data. However, if the algorithm itself is biased, it will affect the output, leading to wrong decisions.
This bias is more dangerous in healthcare, HR(Human Resources), or law enforcement. It can negatively affect algorithms through skewed input data, biased programming, or wrong output interpretation.
Types of AI Bias
You have a brief idea about AI bias and its impact on outcomes. Let's have a detailed look at different types of AI biases.
(1) Algorithm Design Bias
This type of bias can be created due to programming errors like the AI designer unfairly giving weightage to different factors of the decision-making process.
Weighting factors is a technique to avoid bias, however this requires assumptions on the part of designers. This can lead to bias and inaccuracies in the output.
It may also happen that developers might embed the algorithm with conditions, either consciously or unconsciously.
(2) Data Bias
Incorrect data has characteristics like lacking information, being non-representative, being historically biased, or being "bad." These characteristics lead to algorithms producing unfair output and amplifying biases in the data.
AI algorithms that accept biased results as inputs for decision-making create a never-ending feedback loop.
(3) Evaluation Bias
There can be bias in evaluation by algorithms when results are interpreted based on the fixed conception of the individuals involved. Although it may be a neutral and data based algorithm, the way its output is applied by individuals or business may result in incorrect decisions.
(4) Proxy Data Bias
It can happen that AI systems may replace sensitive parameters in data like race or gender with proxies to mask them.
However, this approach can lead to unwanted results in case the proxies match with some other real attributes.
As a result, individuals whose proxy data matches some other real attribute may face bias.
Real World Examples of AI Bias
Let's have a look at some of the real world examples of AI bias and see how it can impact individuals as well society as a whole.
(1) Recruitment
It is possible that Resume screening algorithms and job description builders can cause workplace biases. It may happen that the screening software tool may favor male-associated terms or be biased against females for employment gaps.
(2) Law Enforcement
There can be biases in predictive policing algorithms. As a result of the bias, the algorithm may predict higher crime rates in certain neighbourhoods which will result in increased scrutiny of that area.
(3) Credit Scoring
Algorithms are used by banking & financial services companies to determine lending feasibility for individuals or businesses. However, due to biases in credit scoring algorithms, applicants from low-income neighbourhoods may have higher rejection rates.
(4) Education
In case of algorithms being used for evaluation and admission, there are chances of them being biased towards a particular student group. These algorithms may favor students from well-known schools as compared to under-resourced schools.
(5) Healthcare
AI in healthcare can introduce bias not only in diagnosis but also in giving treatment. It can happen that the AI system has been trained with data from a single ethnic group and may make mistakes when used for other groups.
5 Ways for Addressing Algorithmic Prejudice
Now you have an idea about AI bias and the various types of AI bias. Let's discuss ways in which this algorithmic bias can be addressed.
(1) Algorithmic Fairness
AI bias can be significantly reduced by introducing algorithmic fairness techniques. These techniques include re-weighting data to balance representation, using fairness constraints in optimization processes, and using differential privacy.
(2) Human Reviews
AI can process vast amounts of data in a matter of seconds but does not have the understanding of humans. Humans should review the results of AI based algorithms to see whether there are any biases.
There should be regular human audits, reviews of AI decisions and feedback should be sought from diverse stakeholders to ensure AI systems aligns with ethics.
(3) Diverse Data
AI systems can make fair and accurate decisions only when the training data provided includes all possible scenarios and demographics. You must make use of diverse data sets, so as to enable your AI systems to not make bias by favoring one group.
Data sets should be regularly updated as per changes in the society and to prevent old, known biases.
(4) Transparency of AI Systems
You should make the AI decision making process clear to be understood by all the stakeholders. For this, you must provide detailed documentation which mentions how AI systems have been trained, data used by them and decision-making logic.
All these details provided will help in trusting and understanding of the AI system by all stakeholders.
(5) Bias Testing
You should conduct regular bias testing by finding out the 'AI systems' output for known benchmarks. This will help in detecting anomalies in output across diverse demographic groups or data.
As a result of bias testing, you will be able to identify areas where the AI system might discriminate or favor particular groups. After testing the AI software, developers can make changes in it to eliminate biases.
Wrapping Up…
We have just seen the tip of the iceberg as far as AI is concerned. It will be used extensively in the near future in all walks of life and in each sector of business. This makes it very important to address issues of bias in the technology, as it will impact thousands or millions of people directly.
We have seen the different types of AI bias and the ways in which they can be addressed. It is possible many new types of biases may creep up in AI technology in the future. As a result, businesses and citizens need to keep a close watch on AI systems.
Victor Ortiz
Content Marketer
Victor Ortiz is a Content Marketer with GoodFirms. He likes to read & blog on technology-related topics and is passionate about traveling, exploring new places, and listening to music.
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