Measuring and Tracking Biases

AI Generated image of multiple colors with different colored blocks

Organizations can measure and track bias in their AI systems by implementing a combination of strategies: 

  • AI Governance: Establishing AI governance frameworks to guide the responsible development and use of AI technologies, including policies and practices to identify and address bias [1] [2]. 
  • Bias Detection Tools: Utilizing tools like IBM’s AI Fairness 360 toolkit, which provides a library of algorithms to detect and mitigate bias in machine learning models [1]. 
  • Fairness Metrics: Applying fairness metrics that measure disparities in model performance across different groups to uncover hidden biases [3]. 
  • Exploratory Data Analysis: Conducting exploratory data analysis to reveal any underlying biases in the training data used for AI models [3]. 
  • Interdisciplinary Collaboration: Promoting collaborations between AI researchers and domain experts to gain insights into potential biases and their implications in specific fields [4]. 
  • Diverse Teams: Involving diverse teams in the development process to bring a variety of perspectives and reduce the risk of biased outcomes [5]. 

These measures help organizations to actively monitor and mitigate bias, ensuring their AI systems are fair and equitable. 


1. IBM Policy Lab: Mitigating Bias in Artificial Intelligence 

2. What Is AI Bias? | IBM 

3. Testing AI Models — Part 4: Detect and Mitigate Bias – Medium 

4. Mitigating Bias In AI and Ensuring Responsible AI 

5. Addressing bias and privacy challenges when using AI in HR