Bias in Artificial Intelligence Models: Causes and Solutions

Once upon a time, Amazon built an AI-powered hiring tool to simplify its hiring process. You see, Amazon is one of the biggest companies in the world. So it makes sense that they need an automated process to sort through the thousands of applications each department gets. However, there was just one major problem with this arrangement and as such, Amazon had a huge problem. Their AI-powered tool, meant to streamline the recruitment of every qualified candidate, started favoring male candidates. This is one example of bias in artificial intelligence that happened in 2018.

Unfortunately, Amazon is not the only company that has seen its fair share of bias in artificial intelligence. This is a big problem as artificial intelligence is meant to help everyone. Therefore, it should not have any form of discrimination that may affect its performance. Bias in artificial intelligence causes many problems that can affect a person’s life negatively. Imagine if the defective Amazon AI recruitment tool had not been discovered and scrapped it would have robbed many qualified female candidates of the opportunity to work in that company.

Can we pause for a moment and ask, what causes bias in artificial intelligence? Truthfully, there are various factors but we have been able to identify these problems. Identifying them helps us create solutions to solve them, thereby eliminating bias in artificial intelligence. In this article, we will discuss the contributing factors to the bias we commonly encounter while using AI models and solutions to reduce these biases and make AI safe and fair to everyone.
Let’s jump right into it!

What causes bias in artificial intelligence models?

AI bias refers to the bias in the results produced by AI systems. The result of these biases is a reflection of human biases within a society that may or may not be based on historical and current societal inequalities. It is also known as bias in machine learning.
There are a lot of factors that cause bias in artificial intelligence models. Generally, AI biases come from four main sources. Here are the four sources that cause bias in artificial intelligence.

Algorithmic bias

The design of an AI model may sometimes favor certain groups of individuals over others. This occurs when the design and hyperparameters used in training the AI models inadvertently introduce bias, leading to biased outcomes. When this happens, the result is an AI model that favors certain groups over others, resulting in discriminatory outcomes. This can happen even if the training data is unbiased.

Training data bias

In some cases, the algorithm of the AI model is okay, but the data with which it was trained is biased. Biases in the training data will ultimately lead to biased outcomes. For example, training data that favors a certain demographic of individuals or contains historical biases will reflect these biases in the predictions and decisions it makes.

Cognitive bias

These are biases that reflect the prejudices and biases of the individual or team that developed the AI model. These biased decisions can occur at any point in the development of the AI model and may seep in without the developers realizing it. Eventually, the role of the smallest human decision in the development of an AI model creates a ripple effect that spreads throughout the structure of the AI model. This affects how the model makes decisions and future predictions and alters it useless for the affected group of individuals.

Out-group homogeneity bias

This occurs when the developers simply have no idea about another group they don’t belong to. They simply don’t know what they don’t know. Generally, people have a better understanding of the group they belong to. For example, people of certain age groups have a better understanding of the problems faced by their age group and what solution would work best.
When this occurs, the AI model has a limited understanding of individuals not part of the group considered in the training process. Therefore, the results and outcomes of this model may lead to bias.

These biases exist in the AI models we use today. A typical example is when AI generates results showing that all doctors are males and nurses are females. In summary, bias in artificial intelligence comes from two major sources: the design of the models themselves and the training data they use.

Real-world implications of bias in artificial intelligence

In a perfect world, AI would be without bias. But we don’t live in a perfect world. There are stereotypes about certain groups of individuals. Take, for example, the Amazon AI recruitment tool that failed in 2018. It worked based on the historical stereotype that men were better qualified for positions than women. Therefore, it discriminated against women whether they were qualified or not.

These biases also affect other groups of individuals in different scenarios based on the stereotypes about them. Some of the implications of AI bias include:

• Facial recognition errors: this has grave consequences in the judicial system where AI-powered facial recognition systems misidentify individuals that may fit into a criminal profile due to their skin color or ethnicity.
• Hiring discrimination: just like Amazon’s hiring tools, some AI-powered recruitment tools may favor men over women in certain work positions.
• Economic inequalities: there may be disparities in loan approval by financial systems due to biases in their AI models. This causes individuals who do not fit into the stereotypic model to get their loan requests denied.
• Poor trust in AI systems: biases and discrimination from AI lead to distrust in AI systems. Affected individuals may not want to use AI systems which further limits AI development in the world.

How to solve bias in artificial intelligence?

When developing an AI model we must take all the necessary precautions to avoid any form of bias. Mitigating AI bias is necessary to ensure that AI is fair and accessible to everyone regardless of the group they belong to. Here are a few steps to ensure that AI is free of biases.

Diversify AI development team

Bias may seep into any stage of AI development due to cognitive bias. Therefore, companies must make use of inclusive teams in AI development to bring in different perspectives which can help to identify potential biases.

Diversify sources of information and training data

Earlier, we pointed out that sometimes bias occurs when the developers or team of developers simply don’t know what they don’t know. To solve this problem, information should be obtained from individuals from all concerned groups that the AI model is aimed at. With the development of the internet, this is fairly easy as it eliminates geographical limitations.

Quite understandably, there might be some limitations with getting all groups represented on the training team. Especially for companies that have financial limitations. In this case, the training data, developers must ensure that the model is trained on all segments of the population to minimize bias.

Implement periodical audits to check for bias

Some bias may seep into the development of the AI models at various stages of development. This is no one’s fault, that is why regular audits must be carried out to ensure that the AI model is free of any form of bias. In a situation where bias is identified, the AI model is pulled back and the issue is resolved accordingly.

Practice transparency in AI development

Transparency gives users an understanding of how AI models work. It allows them to see the decision-making process and outcome of an AI model. Therefore, by ensuring that the model is transparent not only developers can identify biases in the model, but users can also identify these biases. Since they are the ones being affected by any bias it can be quickly identified. It is a typical case of, “he who wears the shoes knows where it pinches”.

As such, developers should adopt explainable AI practices to make AI decision-making processes transparent.

Establish strict ethical guidelines

Ethical AI practices enforce policies and guidelines that promote fairness and remove discrimination in all AI models. Without ethical AI practices the AI model will have inaccurate data which may have harmful consequences for underrepresented or marginalized groups and individuals.

At Openfabric AI, we implement these precautions to develop tools that are inclusive of all individual groups. As the internet of AI, these precautions are necessary to ensure that our tools are free of bias and serve every individual equally. You can check out all our AI tools in the MARKETPLACE.

Conclusion

In recent times, we use AI models in almost every aspect of our lives. Schools, hospitals, financial institutions, and even the legislative systems use AI to enhance their interaction with society. This involvement with AI means that any bias will negatively affect the livelihood of the affected groups.

Therefore, as developers, we must take necessary precautions to ensure that AI is not biased and safe to use for all individuals regardless of who they are. When using AI, we must ensure to report any bias that may be encountered. This will help build trust and ensure a better use of AI.
Remember that AI is not dangerous and its errors can be fixed. Do your part and help fix the bias in artificial intelligence today!

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