Bias in GenAI Systems: What It Is, Why It Matters, and How to Address It

This IDC Perspective discusses bias in GenAI systems. GenAI's impressive capabilities have earned it a valued role in many organizations. It is answering questions, summarizing complex data sets, and speeding workflows. However, the presence of bias in GenAI output justifies caution in GenAI deployments.IT leaders should confront bias by learning about it and implementing effective bias mitigation strategies, rather than abandoning GenAI. GenAI biases fall into the categories of data, algorithmic, operational, drift, and confirmation. IT leaders should recognize each type and understand how to correct it.There are many good reasons to address bias, starting with an organization's own ethical guidelines. Local, state, national, and international laws, such as the EU AI Act, are coming onto the books, and violations could lead to significant penalties in addition to loss of customers, employees, and reputation. Audits for bias are a way to achieve regulatory compliance and in some cases are mandated by the new laws. Consultancies are becoming expert in bias audits and bias mitigation, while law firms are becoming expert in bias defense."Bias will not go away on its own. IT leaders must understand the risks of the biases they may face. Then they must evaluate, deploy, monitor, and adjust debiasing technologies on an ongoing basis," says Stanley B. Gibson, adjunct research advisor for IDC's IT Executive Programs (IEP)."Companies that effectively mitigate bias will have an advantage due to enhanced equity and resilience, marked by fair access to opportunity, stronger customer trust, accurate risk decisions, and safer operations that drive both innovation and long-term stability," says Kathy Lange, research director, AI Software at IDC.