Best Practices for Avoiding AI Biases in Data and Why It’s Important

Machine Learning, Artificial Intelligence, Blockchain, Genome Editing, etc., are some of the sophisticated technologies surrounding us. As has been the case with any technology, they reduce the need for human intervention in business processes. However, they pose new ethical challenges. Technology is created by humans and often reflects human biases. Machine learning models may reflect the biases of designers. Biases may squeeze in due to the approach of data scientists who implement the models. Some AI biases in data may come from data engineers that gather data.

In the book ‘Mitigating Bias in Artificial Intelligence’ the author Berkeley Haas says that “the use of AI in predictions and decision-making can reduce human subjectivity, but it can also embed biases resulting in inaccurate and discriminatory predictions and outputs for certain subsets of the population.”

Addressing bias in AI is a smart move for business as the stakes are high for business leaders. Biased AI systems can produce erroneous and discriminatory predictions. It can impact a business’s reputation and future opportunities and earnings. 

Marketers and others depend on AI to a considerable extent to help create the best prospects for a company’s products and services. However, they must take steps to remove any unintentional bias from the AI algorithms. It can prevent their powerful marketing messages from going to potential buyers. 

Technology experts advise the following ways of eliminating or at least minimizing AI biases in data.

Reviewing AI Training Data

Understanding training data is vital for any business aiming to make processes more efficient with data-driven results. The main reasons for AI bias are the academic and commercial datasets. Cross-training employees in various departments about the AI bias process and its adverse impact can help combat the problem. 

Data scientists can ensure that their data gives an accurate and comprehensive picture of the diversity relayed to the end-users. They analyze all the cases and the cause of action to prevent any discrepancies. Businesses must take a close look at the background and experience of different individuals in the tech team. 

Check and Recheck AI’s Decisioning

With manual lead scoring systems, it was relatively easy to inspect and identify the discriminatory elements. In modern AI models, such features may be tough to detect. It takes special training to understand them. 

One practical way of enabling AI to be stringent but always transparent is to review the application of the AI. A lot of discussions are happening around the potential bias in AI. Of course, no one agrees that AI is perfect. But AI truly eliminates many system biases introduced by humans.

A scoring model created by humans may be partial to the biased opinions of its developers. Those creating the model may inadvertently select the attributes and engagement actions that may not be entirely foolproof or fair.

AI decisioning needs to allow checking by humans. There is transparency in the usage of AI. But humans and technology can collaborate and make each other accountable to mitigate discrimination.

Receive Input Directly From Customers

Organizations must understand the limitations of their data and then analyze customers’ experiences. The best way of doing this is to actually talk to customers from time to time. Collate and record their personal experiences.

Contact customers by phone or email and encourage them to share their experiences in all honesty. Once the issues are understood, they are analyzed, and the necessary corrective steps are taken. Customer support can document complaints from customers and fix problematic algorithms. 

Carrying Out Constant Monitoring

Companies can create a framework for ethical decision-making in data and machine learning projects. This is an effective way of constantly monitoring AI systems and detecting bias. Precautions need to be taken in every phase to prevent bias from creeping into the system. Review and monitoring of output are crucial to keeping bias away. 

Organizations that follow this method also keep a close watch on various aspects. These include law, human rights, IP and database rights, data sharing, policies, anti-discrimination laws, etc. Monitoring involves looking at data consumption patterns, data sharing processes, awareness, transparency, consent, and data disclosure transparency.

Better control of AI bias is achieved by tracking ongoing implementation, looking at reviews, and repetitions of data ethics concerns. Companies must also consider the process of data disposal and data deletion.

What Can These Steps Do?

When you apply these changes to your AI processes, they can definitely help mitigate and even eliminate AI bias. But some issues may need technological answers. A multidisciplinary approach is also recommended. Opinions from social scientists and humanities professionals can help in devising better strategies.

Still, these changes alone may not help businesses in certain situations. They possibly need more robust and reliable tools to determine whether a system is good enough for release. These tools also help decide whether to give permission for completely automated decision-making in some situations. 


We know that an entirely unbiased AI is unlikely in the real world. AI works on data input generated and provided by humans. There are several human-based prejudices in existence, even in technology. The discovery of new AI biases in data keeps adding to the overall number of biases regularly. 

That’s why one can conclude with a degree of firmness that a wholly impartial AI system will never be achieved. However, one can combat this AI bias by testing data and algorithms scrupulously. Companies must apply best practices while gathering and using data and creating AI algorithms.

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