As AI gains traction in critical business processes, they have also raised serious ethical issues. AI bias is when an algorithm offers biased outcomes based on inaccurate assumptions.

While algorithmic models do not think like humans, humans can unintentionally introduce Bias during AI development and updates. Majorly, systemic, computational, and human biases drastically affect AI.

By analyzing these levels of AI biases, Businesses can address each one effectively and establish a robust ethical framework.

Practices to Address Systemic, Human, and Computational Bias

• Addressing Systemic Bias

As the term suggests, systemic bias occurs within organizations that treat numerous groups differently. These biases are challenging to address since they are the least obvious. Systemic bias is the most foundational of the three biases since they affect when and how the other factors surface.

When addressing systemic issues, businesses must find and address the blind spots in organizational values. They must ask the employees whether or not those values are reflective of everyone. Companies can utilize such insights to evaluate how AI has been affected. Moreover, the organization’s values filter from top to bottom, influencing the AI developmental ways, even if unintentional.

At the same time, businesses can establish a system of checks and balances to address systemic bias. They must regularly evaluate the AI use cases with multiple team members offering diverse perspectives.

It will also allow businesses to scrutinize new biases, regardless of size. Lastly, companies can allocate another team to assess and resolve the issues by employing automated filters to prevent discrimination.

Also Read: Why is Employee Experience as Important as Customer Experience

• Addressing Human Bias

Human bias occurs when employees use assumptions and conclusions to complete the missing information. More importantly, human bias is challenging to detect since AI models primarily operate on black boxes.

Businesses gravitate more towards transparency. By understanding why the models offer specific results, companies can easily detect the root cause of biases. At the same time, transparency houses explainable AI. Explainability is the ability to demonstrate how an AI system has made a particular decision, prediction, or recommendation.

Businesses can ensure transparency by clarifying the primary inputs their AI models employ. It allows the developers to determine correlations to bias that was previously undetected. Moreover, businesses can ensure transparency by conducting external audits by an unbiased third party.

• Addressing Computational Bias

A broad issue concerning AI bias is its embedment in the training data called computational bias. Within conversational voice AI, for example, businesses detect bias in common practical problems affecting end users- whether or not an AI bot can understand voices and accents from diverse backgrounds.

As per a recent report by Speechmatics, “The Voice Report 2022“, accent and dialect are the factors that prevent voice technology from gaining complete accuracy. At the same time, 55.6% reported that most voices are understood, and 37.8% said too many are not understood.

For a robust experience, businesses must train AI on the data representative of the accents, dialects, and people it is likely to interact with. Bypassing this step might lead to marginalization. Moreover, businesses might miss a minute of dialect distinction, leading to bad developmental choices that would prevent the AI model from understanding specific speakers.

Businesses can address the computational bias by tailoring the AI and training the data. They can achieve this by narrowing their focus to just one interaction that a single AI bot performs rather than making a general-purpose model. This approach gives the model minimal potential for significant data-based bias. For example, use cases like a bot gathering customer feedback, while another use case might offer the customers a claim update.

Another approach would be compiling the training data and eliminating bias as much as possible. Businesses can do this by assessing numerous parties involved and engaging end users to uncover the biases they experienced.

Causes of Bias in AI                    

Bias in AI occurs because humans select the data used by the algorithms and evaluate the application of algorithm results. Without broad preparation and different groups, it becomes easy for unconscious biases to enter machine learning models, after which the AI systems automate and perpetuate the biased models.

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Learning Steps to Avoid Bias in AI

A primary step is educating the data scientists on how responsible AI appears and its embedment within the organizational values. Moreover, businesses must ensure transparency with consumers to help them determine how the algorithms offer predictions and make decisions.

One of the significant pitfalls of AI is the “black box,” where consumers can view the inputs and outputs but lack AI’s internal operations knowledge. Businesses must strive to achieve explainability to understand how AI works and its potential impacts.

A naïve method to diminish bias related to protected classes- gender or race from data is to eliminate the labels marking race or gender from the models. Unfortunately, this approach will collapse since the model can establish an understanding of these secured classes from other labels- postal codes.

Therefore, the usual approach involves eliminating these labels to enhance the models’ results in production. Debiasing algorithms have also recently been developed to mitigate AI’s bias in ways that do not diminish the labels.

Final Thoughts

AI is capable of making a significant impact on the ways businesses operate. However, it must be maintained and built ethically and with complete responsibility. At the systemic level, companies must address the human element of AI; tailoring the models and efficiently vetting training data will help them eliminate bias.

Businesses must understand that detecting, handling, and preventing bias is not a one-time activity; they must ingrain the company culture. It is essential to identify the data, models, and technical limitations for awareness’ sake and so that human methods of preventing bias in AI-human-in-the-loop can be considered.

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