AI Bias Uncovered: 15 Types of Bias and How to Mitigate Them

What is AI Bias?

From the medical field to financial management, Artificial Intelligence (AI) is bringing innovative solutions to problems in all fields. But, with this AI development comes a big problem. That is AI bias. Basically, this AI bias occurs when an AI system makes wrong decisions due to biases in the data it is fed. This problem arises when the data used to train AI models contains human biases, generalizations, or incomplete information.

For example, if an AI system is trained on past data with biases (such as hiring people in favor of one gender), that AI will make similarly biased decisions. In important matters like hiring people, giving loans, or settling cases in court, this bias can have very bad consequences.

AI bias is not just a technical problem; It will have long-term effects on society. It widens the existing inequality and widens the gap between different groups. Biased AI can leave qualified candidates unhired. Biases in face recognition systems can misidentify certain color schemes. If we understand AI bias, we will understand what kind of harm biased algorithms can cause. If these problems are identified early, AI systems can be viewed as fair, transparent, and honest. Understand that AI itself is not biased – the data used for training and its design is the problem. At a time when AI is taking over our world, we can only create a fair and equal society by correcting the biases in AI systems.

Types of Bias Found in AI Models

AI has played a very important role in our lives today. Phones, computers, and home appliances have AI in everything. But, like any other technology, AI will make mistakes. Sometimes, these errors Lead to AI bias. This bias affects how the AI ​​makes decisions, leading to incorrect decisions. Now let’s see what kind of biases are present in AI models:

1.Bias in Training Data

The way AI work is that they learn from the information we give them. If there is bias in this data, the AI ​​will also be biased. For example, let’s say a company uses AI to hire people for a job. They use old records to train that AI. If those records show that men have been hired more often, the AI ​​will also give priority to men.

Another example is AI that detects facial recognition. If the images it is trained on have mostly white faces, it will not be able to correctly identify the faces of black people. If there is a problem in the training data, the AI ​​will also make wrong decisions.

2.Selection Bias

For AI to understand everything correctly, they need to know information about all classes of people. Sometimes, the data used to train the AI will be lacking or completely lacking information about some groups. If this is the case, AI bias will occur, and the AI will make the wrong decision.

For example, in the medical field, if an AI is created using data from white patients, it will not work well for black patients. If their medical records are not in that data, it will have difficulty diagnosing them. Similarly, speech recognition AI is designed to understand only American English well. It is possible that it will misunderstand the voice of someone who speaks a different language.

3.Labeling and Annotation Bias

The labelers sometimes knowingly or unknowingly bias the data they use to train the AI. This leads to AI bias. For example, let’s say that an AI is being used to figure out what kind of thoughts people post on social media. If the labelers wrongly label posts from certain groups as “angry” or “negative,” the AI will make the same decision. Even if they say something normal, the AI will misunderstand it and blame them.

4.Algorithmic Bias

Sometimes, the data used to train AI is good. But the AI programs themselves (algorithms) start to be biased. This is another form of AI bias. In this case, some AI models give more importance to certain things. This leads to incorrect results.

For example, let’s say that AI is used to determine credit scores. Some AI models will decide based on the area where a person lives. People living in areas with a lot of poverty have a hard time getting credit. Similarly, some AI models will only give importance to making correct decisions overall. Therefore, they will not care about the impact on a small number of people.

5.Bias in Human Feedback

Some AI models learn from human feedback. However, if humans have biases, AI bias will also emerge from them. This is called Bias in Human Feedback.

For example, let’s say that AI is used to hire people. HR people will give feedback on who is good. They may have some biases without even knowing it. For example, if they say that people from certain regions are good, the AI ​​will also make the same decision. This will result in other qualified groups not getting the job.

6.Cultural and Societal Bias

AI are just machines. But they learn the customs and beliefs of our society and behave in the same way. Because of this, the prejudices of our society also come into AI, causing AI bias.

For example, the films and stories created by AI often reflect the culture of Western countries. AI chatbots learn all kinds of dialects on the Internet. There are good and bad in it. If AI learns our social prejudices in this way, it will make wrong decisions and create problems.

7.Deployment Bias

Sometimes AI is designed well. But where it is used matters. An AI that works well in one place may not work well in another. This is called Deployment Bias, another form of AI bias.

For example, an AI developed to detect fraud in one country may not work well in another country. This is because the laws and customs of the people are different in each country. Similarly, an AI developed to predict crime in big cities may not work well in villages.

8.Automation Bias

Sometimes we trust AI too much. We think that a computer is right and we make decisions based on what it says. Is this true? Can AI make mistakes? This kind of over-reliance on AI can lead to AI bias and problems.

For example, suppose the police use facial recognition technology. If the technology incorrectly identifies someone as a criminal, the police will rely on it and arrest them. They will not conduct a proper investigation. Similarly, when hiring someone, we should not make decisions based solely on what the computer says. We should use our own experience and understanding. Otherwise, qualified people will not get the job.

Other AI-Related Biases

9.Data Availability Bias: AI does not look at all the information, but only looks at the information that is easily available and makes decisions, which can lead to AI bias. For example: If AI makes decisions based on only the most shared things on the Internet, some important things will be missed.

10.Historical Bias: AI will continue to make the same biases that existed in the past, reinforcing AI bias. For example: AI may think that men are in important positions in large companies and not give women a chance.

11.Popularity Bias: AI will only give importance to things that are famous. Example: AI that recommends songs will only recommend famous songs. New singers will not get a chance.

12.Temporal/Recency Bias: AI will only give importance to recent events and forget old ones, leading to AI bias. Example: If you use AI to invest in the stock market, you may lose money by only looking at the stocks that have risen recently.

13.Causal Attribution Bias: AI will sometimes misunderstand the cause. Example: If you use AI in customer care, it will attribute a bug in the software to the customer, exacerbating the problem.

14.User Interaction Bias: AI will learn from the behavior of its users, which can result in AI bias. Example: A chatbot might learn bad words from someone who uses bad words and start saying the same thing.

15.Overcorrection Bias: Sometimes when AI tries to correct a bias, it overcorrects, creating a different kind of AI bias. Example: When hiring, an AI might decide to give everyone a chance and give the job to someone who is not qualified.

How AI Bias Affects Decision Making

From hiring to lending, AI has a hand in everything. However, if this AI is biased, it will not produce fair results. The problem is with us and our society.

This AI bias will further exacerbate existing inequalities. For example, if a recruiting AI has been trained on old, biased information, it will prioritize candidates for technical jobs because it was like that in those days, too. Due to this, many people miss good opportunities. Gender and racial discrimination are also rampant.

It is a similar story in the criminal justice system. AI systems that predict someone will re-offend may also be biased. If you train people with already biased information like arrest records, decide that people of a certain race are more likely to commit crimes, and give them long prison sentences or unnecessary detention.

Just look at shopping online; the products we recommend are also vulnerable to AI bias. Customers’ preferences are often biased towards certain products, and other products that are interesting and useful to us are hidden from our eyes.

Even in medicine, AI bias has led to misdiagnosis or unequal treatment recommendations for some. If an AI model is trained on data that doesn’t represent all people, it won’t provide the right results for everyone.

In short, if AI is biased, there will be a lot of problems. To bring fairness and accuracy to AI systems, this bias must be corrected somehow.

Real-World Examples of AI Bias

AI bias is not just a rhetorical issue; It causes a lot of damage in real life as well. Recruiting, policing, medicine, and related issues are all important, and AI bias is a problem. Here are some examples.

AI Bias in Hiring Practices

This is one of the topics most discussed in the AI ​​industry. In 2018, Amazon ditched the AI ​​tool it used to recruit. Because we found that it was not very objective against puppets. For the last 10 years, they have given training to this AI by storing the resumes that came to Amazon. However, the majority of those who applied were men, and that was for technical jobs. Therefore, the AI ​​selected only resumes similar to those used by men and told them not to ask for resumes from women. This example shows how discrimination can occur when there is an imbalance of information [Learn more about the Amazon AI Hiring Bias issue here]

Such AI will further increase the inequalities in employment. They will act without knowing the bias, and many people will not get jobs. Some words in advertisements that are advertised as people are needed for work will also create discrimination. Words like “ninja” will only attract men, not women. Small biases like this will prevent many people from getting a chance.

Facial recognition technology

Facial recognition technology also has many problems. Studies have shown that these AI systems do not perform well when it comes to identifying people of color, including black women. In 2018, the MIT Media Lab conducted a study. Even the facial recognition systems of big companies like IBM, Microsoft, and Face++ have found that they make a lot of mistakes when recognizing people who are colored than white people. This leads to issues of misidentification and unfairness.[Read the MIT Media Lab’s study on Facial Recognition Bias here]

In 2020, facial recognition technology mistakenly identified Robert Williams, a black man in Michigan, and the police arrested him. Because of these wrongful arrests, people have lost trust in the police. People complain that the police are over-surveilling in some areas. If the government and companies use this technology and correct the biases in it, no one will be treated unfairly.[Read the Article in The New York Times ]

AI predicts where crimes will happen

Now, use AI to predict where crimes are likely to happen and who will commit crimes again. However, some say that these systems increase racial discrimination. In 2016, we found that a tool called COMPAS produced biased results against black people. This system says that black people will re-offend. But that is not true. This is due to bias in old data.[Read about ProPublica’s investigation into COMPAS here]

AI Bias Medical sector

AI dependence is affecting the medical sector as well. In 2019, a study found that an AI system that predicts the health of patients gives more accurate results for black patients than for white patients. They had trained this system with information that did not properly understand the health needs of black people. This can lead to inaccurate results and unequal treatment.

As a result, some people do not get the correct diagnosis, treatment is delayed, and the impact is even greater. Even if a new technology called AI comes to the medical field, it should be used equally for all people. For that, the data used to train the AI ​​should include information about all classes of people. Otherwise, even if this technology helps some people, it will only harm some people.

Gender Discrimination in Financial AI

Now, AI are helping with everything from buying and selling to money. But, would you believe that there is bias in this too? In 2019, Apple Company issued a credit card. This card was issued by a large bank like Goldman Sachs. However, they have given women a lower credit limit than the one given to men. Will computers only support men in terms of money? [Read the Article in BBC ]

AI Bias in Image Generation and Stereotyping

By now you must have heard that AI also create images. But would you believe it if those images were biased? In a study, more than 5,000 AI images were created and viewed. In them, the CEO of a large company was shown as white men. The AI ​​did not create women like doctors, lawyers, and judges as much. It only showed men in big jobs. It never showed women like that. If you see all this, it seems that AI also learns the prejudice that exists in our society and shows it in images.

Such biased images create wrong thoughts in the minds of people. If AI is to create images that accurately represent all people, the data used to train the AI ​​must include information from all levels of people.

The Broader Risks of AI Bias

AI bias is not just a problem for some people. It will affect society as a whole. People will lose trust in the decisions made by AI. Companies that use biased AI can be sued, fined, and their reputations will be damaged. Inequality will increase in everything from jobs to money to police and healthcare. Some groups will be denied access to credit, jobs, and healthcare. If AI-generated images and videos are biased, people will start to think differently about each other.

Addressing AI Bias and Moving Toward Ethical AI

We need to be careful to prevent AI bias. Everything should be transparent. AI should be created with good intentions. Companies, governments, and AI developers should all work together to eliminate this AI bias. Only then will AI be helpful to everyone. AI programs should be tested frequently. The data used for training should include information from all walks of life. Humans should also be paying attention to AI. Only then will AI be used for good things.

Importance of addressing AI bias

Ensures Fairness and Equality

If AI behaves in a biased manner, the inequalities in our society will increase even more. AI should not see Region, gender, age, etc. If there is bias in important matters like hiring, healthcare, lending, and police work, it will have a big impact.

Builds Trust and Accountability

If AI is not biased, people will trust AI. If everything is transparent about how AI works and how it makes decisions, people will have trust. If everyone believed that AI was making the right decisions, AI technology would thrive.

Improves Accuracy

If AI is biased, it will make wrong decisions. For example, let’s say that AI is used to diagnose a person’s illness. If AI is biased, it will diagnose the wrong disease and give the wrong treatment. Similarly, banks use AI to give loans. If AI is biased, the right people will not get loans. If the AI bias is corrected, AI will make correct decisions.

Promotes Ethical Standards

AI can make a lot of changes in our lives. It should only be used for good things. If there is bias in AI, it will affect some groups. Therefore, when creating AI, we should create it with fairness and equality in mind.

Encourages Innovation and Progress

If AI is biased, some groups cannot progress. For example, if women or people with disabilities invent something new, AI may reject it without thinking. If we remove AI bias, everyone will have an equal opportunity. New innovations will come.

Legal and Regulatory Compliance

Now the government has made a law that there should be no bias in AI. If there is bias in AI, companies will be sued and fined. If we remove AI bias, we can prevent such problems from happening.

Prevents Harmful Impact on Vulnerable Groups

If AI is biased, it will have the most impact on people who are already struggling. For example, people like black people, women, the elderly, and people with disabilities will have a lot of problems due to AI bias. If AI is not biased, we can protect them.

Improves Overall System Performance

If AI is free of bias, it will understand all people and help everyone. This will make AI work better.

How to avoid AI bias

Select the Correct Learning Model

There are many types of models in AI. In a “Supervised Model”, humans select the data to train the AI. The important thing here is that the team that selects the data should include people from all walks of life. If there are not only data scientists, but also other groups, AI Bias will be reduced. In an “Unsupervised Model”, the AI itself selects the data. However, special tools that detect AI Bias must be added to the AI and taught it what is right and what is wrong.

Train with the Right Data

Only if AI is given complete and correct data, it will make the right decisions. The data must contain all the information about the people for whom the AI is designed to work. Otherwise, the AI will make wrong decisions and create problems due to AI Bias.

Choose a Balanced Team

The team that creates AI should have people from all walks of life. If there are people from all backgrounds like region, education, work, etc., it is easy to find AI Bias. The people for whom AI is created should also be in this team. Only then can we understand what they need and create AI properly.

Perform Data Processing Mindfully

From collecting data to processing it, we need to be careful in all steps. There is a possibility of AI Bias in every step. Only if we pay attention to it and correct it, AI will work well.

Continually Monitor

You need to check that AI is working properly. You can have another team in your company or an external company test the AI. Only then can you find out if there is AI Bias.

Avoid Infrastructural Issues

Sometimes, if AI is not working properly, the reason is not the data, not the programs. The problem is with the hardware. For example, if the sensors that collect information are not working properly, the wrong information will be sent to the AI, which may cause AI Bias. This is why AI makes bad decisions. Therefore, buy good computer parts and maintain them properly.

Use Diverse and Representative Data

The data used to train AI should include information from all walks of life. There should not be too much information from just one group. For example, the information of all people, including men, women, people with disabilities, rural people, and urban people, should be equal. Otherwise, AI Bias will occur.

Explainable AI (XAI)

If humans cannot understand how AI makes decisions, it may be acting in a discriminatory manner. That’s why AI is now being developed in a way that humans can understand the decisions made by AI. This will be very helpful in important areas like medicine, employment, and criminal cases. If you know how AI made a decision, it’s easy to detect bias.

Regularly Review and Audit Models

AI should be regularly tested to ensure that it is working properly. It should be tested as new data becomes available and new situations arise. Only then can we ensure that AI Bias does not cause problems.

Promote Inclusive Teamwork

The team that creates AI should have people from different region, education, and work backgrounds. Only then can someone else spot AI Bias that is not known to one person. This will help reduce Bias in AI.

Test for Bias

There are some tools to test for AI Bias. Using these tools, you can see if AI is working properly. Big companies like IBM and Google are giving away such tools for free

Apply Bias-Mitigation Algorithms

There are special programs to reduce AI Bias in AI. Using these programs, you can see if AI is treating everyone equally.

Create Ethical Guidelines

The decisions made by AI and humans should be fair. They should not be discriminatory. For that, some good guidelines should be created and followed to avoid AI Bias.

Educate and Train Stakeholders

Everyone should be clear about AI Bias and how to avoid it. For that, training should be provided frequently.

Maintain Human Oversight

AI should not be the only one making decisions that affect human lives. If humans also make decisions together, they can prevent AI Bias.

Bias Detection in Natural Language Processing (NLP)

AI models like GPT-3 and BERT are designed to understand our language and speak. But, sometimes it can use the wrong words and speak in a biased manner. They are finding new ways to fix that. If there is good data, good programs, and good design, AI will learn to speak properly.

Foster Accountability

If someone behaves in a discriminatory manner, they should be punished. Only then will others be afraid of discrimination. It should be clear who is responsible for the harm caused by AI Bias in AI.

Frequently Asked Questions