How can we ensure AI has less bias than humans?

Written by: Lauren on January 24, 2024

Eliminating bias in AI is a complex and ongoing challenge. But it’s essential to ensure fairness and equity in AI systems. I believe that AI has huge potential to help us all make fairer decisions. That’s only if we carefully work toward fairness in AI systems. AI is used in many different areas: criminal justice, hiring, and healthcare. But one question stands clear for me – how do we ensure AI’s decisions are less biased than human ones?

There are several examples of biased AI, which often comes down to diversity in its creation. An example of this is facial recognition datasets, data that has 1000s of annotated faces. Used for cybersecurity, law enforcement and customer service. Yet, it turned out that during the creation of the datasets, most of the population was unaccounted for. The developers had unconsciously created more accurate datasets for people like themselves. Generally white, middle-aged men.

This led to a disparity in equality and a lack of accurate, diverse representation. It resulted in high error rates for women, children, older ages, and people of colour. In some cases, this leads to huge issues and even false arrests. 

Since discovering the potential biases with AI, most major tech companies are working to improve them. Due to the aim of eliminating bias. 

One key way to eliminate bias is – use AI! 

Using AI to Eliminate Bias

Need some inspiration on how to do this? Read on for some examples of how to do this.

Use AI in Hiring 

Use AI tools, such as Gender Decoder or Textio, in your hiring practices. These tools scrutinise job descriptions for hidden biases around gender and other characteristics. Thus leading to hiring becoming more inclusive. In tandem, it addresses affinity bias, often seen in hiring processes. 

Bias Detection in User Feedback 

AI can analyse user feedback and comments on websites. It identifies and addresses biased or harmful content, making online communities more inclusive.

Skill-Based Assessments 

Use AI platforms, such as Pymetrics, for skill-based assessments and tests. In doing so, candidates’ abilities are objectively checked instead of subjective judgments.

AI-driven Multimedia Recommendations 

AI can suggest a diverse range of images and videos to users. To reduce the risk of reinforcing stereotypes or biases.

Automated Feedback and Coaching 

AI tools, such as Leapsome, can provide personalised feedback and coaching to employees. This helps them improve their performance and skills without introducing human bias.

Real-time Content Evaluation 

AI can check the real-time content posted on websites. It then flags or filters out biassed or harmful content as it appears.

Diversity and Inclusion Metrics 

AI tools can track and analyse diversity and inclusion metrics in real time. These tools aid organisations to identify areas where they need to improve. Thus setting targets for a more inclusive workplace. DiversityCatch is a great one for businesses to use. 

Cultural Sensitivity Checks 

AI can check content for cultural sensitivity. It then flags or suggests revisions when it identifies potential biases.

Pay Equity Analysis

Using AI to analyse salary data to notice and address gender or race-based pay disparity. Working toward the goal of ensuring that compensation is fair. PayAnalytics is a helpful tool for this. 

Accessibility Features 

AI-driven accessibility tools can assist users with disabilities. It provides equal access to website content and services, reducing accessibility biases.

These examples demonstrate how AI can be integrated into websites and organisations. In order to identify, reduce, and prevent bias, overall, this creates a more inclusive and equitable environment. It’s vital to use AI responsibly and in conjunction with human oversight. This is to ensure that it effectively addresses bias without creating new challenges.


Human judgement remains essential to guarantee the fairness of AI-supported decision-making. AI systems can inherit biases from historical data. This requires human oversight to detect and correct these biases. Ethical considerations and the ability to navigate complex, nuanced situations demand human judgement. 

As AI may lack the capacity to make value-based and contextually nuanced decisions. To foster public trust, meet legal requirements, integrate diverse perspectives, and address unforeseen circumstances, the inclusion of human expertise alongside AI support is indispensable. This results in a more robust and responsible decision-making process.

Tackling bias in AI doesn’t have a quick fix and should be ongoing. As we learn about our unconscious biases, so should the AI we use in the workplace and in our everyday lives. Finding diversity in the tools we use needs to be a collaborative effort to mind the gap and ensure equality for all.

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