AI Bias: What It Is & How to Avoid It?

As organizations increase their usage of AI, people are now forced to question the level of bias that has crept into the AI systems. Examples of AI-related bias in the actual world show us that when discriminatory algorithms and data enter the AI models, the negative results are amplified.

Organizations are motivated to tackle the difficulties that come with AI bias, not only to ensure better results but also to ensure fairness. However, just as gender and racial bias in the real world are difficult to eliminate, eliminating them is no convenient task.

Follow this post to learn more about AI bias and how it can be avoided.

1What is AI Bias?

AI bias, also known as machine learning or algorithm bias, is the algorithm’s tendency to reflect human biases. This phenomenon arises when an algorithm delivers systematically biased results that are owed to erroneous assumptions that are made by the machine learning process. This has become even more challenging as the algorithms tend to reinforce biases.

AI systems also produce very biased results that perpetuate and reflect human societal biases, including current and historical social inequality. As this bias goes unaddressed, it blocks people’s ability to participate in society and the economy. It also reduces the potential of the AI. Businesses are not able to benefit from any systems that produce negative results, fostering mistrust among different employees working in the enterprise.

Here are some of the most common types of bias present in machine learning.

Historical Bias

When beginning the training of a machine learning algorithm, the first step is usually grabbing the historical data, which is generally readily available. However, if one is not careful enough, bias can creep in quite quickly. It typically arises when the data used for training the AI system does not reflect the present reality accurately, and cultural beliefs and prejudices begin influencing decisions. This has an impact not only on the data collected in the past but also on the present data collection.

Sample Bias

Sample bias occurs when the training data does not adequately reflect the real-world usage makeup of your model. This means that one population remains heavily underrepresented or overrepresented.

This also means that the data on which AI is being trained is not representative of the selected population. If the original dataset is not representative of the bigger population, the AI system will underperform.

Label Bias

Training of ML algorithms needs to begin with lots of data being labeled before it can be termed valid. This is actually done many times when we visit a website. An example is when we are asked to identify the squares in an image that shows traffic lights. This is actually part of the confirming labels set for this image to help train the visual recognition models. However, inconsistencies in this labeling can introduce bias into this system.

Aggregation Bias

Aggregation bias comes up when distinct populations or groups are inappropriately combined during the construction of models, bringing out a model that works well only for the majority. In simple words, one single model cannot suit all the groups.

Confirmation Bias

Confirmation bias refers to our tendency to trust information that confirms our present beliefs and discard information that does not. An ML system could be the most accurate one ever. However, if the ultimate result is changed based on gut feelings, all will fail. 

It is especially relevant in machine learning applications where human review is needed before any action at all is taken. For instance, algorithmic diagnoses may be dismissed by doctors because they don’t match their experience. However, it turned out that the doctors had not read the recent literature, which pointed to different symptoms.

Evaluation Bias

A model, therefore, is optimized with the help of training data, but its quality is often measured against specific benchmarks. Evaluation bias can occur when the benchmarks fail to represent the general population or are inappropriate for the way a model will be used.

2How Does AI Bias Impact Society's Bias?

Unfortunately, human prejudice is evident even in AI. It tends to assist humans in making impartial decisions; but only if one works diligently to ensure fairness in AI systems. The underlying data is usually the cause of bias. Here are a few ways in which AI bias impacts society’s bias.

• Models are primarily trained on data obtained from human choices or data taken from historical or social disparities. For instance, word embeddings that are trained on articles from news tend to reflect social gender biases.
• Data may become biased on the basis of how it is chosen or gathered for usage. For instance, in some cases, oversampling may result in more data related to a certain crime.
• User-generated data can lead to bias in feedback loops.
• An ML system may end up detecting statistical connections that are considered socially unlawful or inappropriate. 

3Real-Life Examples of AI Bias

Here are a few AI bias examples that can help understand how bias creeps into AI systems.

Amazon’s Gender Biases

In 2014, they began building a system that would automatically filter job applicants. The idea was to provide the system with hundreds of resumes and then have the relevant candidates automatically picked out. The system was first trained on job applications submitted in ten years and their results.

However, most employees hired at Amazon, especially those in technical roles, were male. The algorithm had learned that as there were more men working in Amazon than males, males were more suitable candidates. This way, there was active discrimination against female applicants. This project had to be entirely scrapped by 2015.

Faulty Criminological Software

The COMPAS algorithm is used in United States court systems. It is used for predicting the likelihood of a crime being repeated – as a guide as criminals are sentenced. This software was analyzed, and it was concluded that it is no better than untrained and random people on the internet. The statistical results generated showed that white defendants were less likely to reoffend as compared to black defendants, which suggests race bias.

Facebook’s Ad Algorithm

In 2019, Facebook was found to be violating the U.S. Constitution by allowing its advertisers to deliberately target adverts according to race, gender, and religion. Job adverts primarily marketed to women included secretarial or nursing work, while those shown to men included those for taxi drivers and janitors. This clearly indicated a bias in how the algorithm formed patterns from the provided data.

U.S. Healthcare

A problem was recently detected in an AI system that was being used in the U.S. for allocating care to nearly 200 million patients, resulting in African-American patients acquiring lower care standards. This issue was linked to the fact that the AI system was using predicting healthcare costs as the deciding variable. Since black patients were often perceived to be able to pay less for the higher care standards, it somehow learned that they weren’t entitled to such standards.

An App for Girls – Giggle

In early 2020, a social networking app, Giggle, aimed to form ‘girls only’ chat groups, relying on gender verification AI software to ensure that only girls could join. The AI system automatically excluded trans girls as well and required them to contact the owners directly if they wished to enter the app. The app couldn’t be launched until mid-2020.

4Useful Strategies to Avoid AI Bias

The need to bring down AI bias has become extremely important as the use of AI increases. Here are some useful strategies that organizations can utilize to avoid AI bias.

Avoid Complete Dependence on Real-World Data

Real-world data often contains unintentional societal and human biases. Therefore, complete dependence on real-world data must be avoided. Instead, organizations must utilize synthetic and real-world data to ensure an unbiased and accurate training dataset.

Regular Data Evaluation

Several years ago, the definition of fairness differed significantly. However, these historical datasets do not necessarily remain accurate as the world changes constantly. AI models designed according to today’s standards may become outdated tomorrow due to environmental and technological changes. This may cause mistakes and bias to creep into the data. Therefore, organizations should regularly evaluate data for potential mistakes and errors.

Diverse Data Team

AI algorithms imitate human thought processes. Therefore, the people who are training and designing the algorithm need to have various perspectives and backgrounds. A diverse data team with members from diverse cultures and genders will be equipped to identify potential bias, creating an accurate training dataset.

Prioritize Transparency

AI algorithms tend to be significantly complex. The identification of biases can be quite challenging without a detailed understanding of the data set and how this algorithm works. Organizations should prioritize transparency to ensure a fair algorithm and clearly explain the decision-making processes behind their algorithms.

Ethical Model Frameworks

Ethical model frameworks serve as a guide to deploying and designing AI responsibly. This helps prevent any bias and includes principles such as accountability, transparency, security, privacy, fairness, and data protection.

Test ML and AI Models Frequently

This step in the avoidance of AI bias cannot be emphasized enough. The ML and AI systems must be regularly tested to ensure that the algorithms shun bias and are able to make accurate decisions. This testing must be conducted before and after the deployment of the AI software and include tests for checking any discriminatory results based on race, gender, or other factors.

5What Can We Learn From All of This?

Bias in AI is dangerous for any organization as it may amplify and reproduce societal stereotypes. They may perpetuate inequalities and reinforce harmful stereotypes, especially as AI becomes even more prevalent in content creation, influencing public perception.

Therefore, it is increasingly important for developers, researchers, and policymakers to prioritize addressing AI bias. A few changes can be extremely beneficial in mitigating AI bias.

Some problems may require a more multidisciplinary approach to solve this issue. It is also important to remember that a completely impartial AI might never come into existence, as an entirely impartial human mind can never exist. However, this problem can be combated by testing algorithms and data and using best practices in gathering, testing, and creating it.

Final Thoughts

As AI advances, it plays a significant role in our decision-making. For instance, AI algorithms are utilized for medical information and other policy changes that significantly impact people’s lives. For this reason, it is essential to use tools such as ChatInsight AI that mitigate bias.

The article proposed some solutions to counter biases in AI. Organizations and individuals must use a multidisciplinary approach to actively participate in creating a fair and unbiased AI system.