A Strategic Approach to Mitigating Bias in Generative AI

In the C-suite, the Gen AI narrative is shifting from theoretical potential to strategic imperative. As we look to 2025 and beyond, forward-thinking leaders recognize AI not just as an efficiency tool but as a catalyst for enterprise-wide transformation.

However, bias in responses from Gen AI systems can still be a significant problem for many use cases. The data used to train algorithmic systems can be harmful, discriminatory, or promote unsafe behavior among users across the world. That said, legislative bodies are making efforts to regulate bias in AI systems. In March, the United Nations adopted a resolution mandating countries to monitor AI for potential risks and 37 countries are currently working on developing AI-related legal frameworks. 

To mitigate the potential impact of bias and ensure compliance, companies should consider a comprehensive testing strategy that draws on human testing and feedback. Training Gen AI using large datasets isn’t enough; companies should also involve a diverse pool of testers from various demographics and locations. Incorporating human input into the AI development process allows organizations to achieve greater data diversity, improve the accuracy of their models and reduce safety risks. For example, by engaging real users in evaluating model performance, organizations can define quality metrics such as accuracy and relevance more effectively than AI systems alone. 

Addressing bias in AI is not just an ethical imperative, but a business necessity. Companies that tackle bias in their AI systems gain a competitive edge by building more inclusive and reliable technologies. As AI continues to shape industries, those who prioritize bias mitigation will be better positioned to harness full potential. 

Here are four steps to prevent bias in AI:

Build the right testing team:

If you have a diverse user base, then diversity is essential for reducing toxicity and bias in LLMs. Testing teams should encompass individuals of different ages, genders, sexual orientations, races or ethnicities, as well as testers with disabilities. Depending on the application and underlying model, companies may also take into account factors such as education levels, political views, and technological ability. Each tester must adhere to proper confidentiality and data privacy regulations for their region.

Testing different elements of the application: 

Once objectives are clearly defined, companies should identify functional defects, assess user interaction and collect feedback. Key components to consider include natural usage, which encourages testers to use the application as they would in the real world; and prompt variation, which sets rules for testers on different types of prompts. It’s vital that the testing detects and mitigates unfair and inaccurate responses, including privacy concerns or violations of copyright. 

Collect feedback at multiple levels:

The initial feedback level should include documenting the application’s response to identify and correct any inaccuracies or biases. The second level should examine the user experience across diverse testing teams, gauging the level of trust users have in the application. The final level should evaluate the model’s progress over time. 

The team needs language and evaluation skills, as the tasks often include research and critical thinking. In some cases, a background in a specific domain may be necessary to enhance the model’s responses based on human feedback.  

Ensure accessibility: 

Companies should ensure their applications work for people with disabilities (PwD), something that can only be achieved through accessibility testing. Directly involving PwD in testing is key to developing AI applications that provide genuine value for users with both permanent and temporary disabilities. It also helps companies to assess whether their AI system generates output that may be biased against the PwD community.  

AI bias can be mitigated and comprehensive quality upheld by training AI algorithms with diverse sets of human-generated data, conforming with evolving regulations and implementing proactive testing methods. By implementing the best practices explored above, companies can pave the way for a safer, more inclusive future. 

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