ISO TR 24027:2021 - Information technology - Artificial intelligence (AI) - Bias in AI systems and AI aided Decision Making

Technology Consulting

ISO TR 24027:2021 - Information technology - Artificial intelligence (AI) - Bias in AI systems and AI aided decision making

AI systems are susceptible to bias, which can lead to unfair outcomes and undermine public trust. ISO TR (Technical Report) 24027:2021 provides guidance on identifying, mitigating, and preventing bias in AI systems. 

This technical report explores the various types of bias that can be present in AI systems, such as data bias, algorithmic bias, and human bias. It also offers strategies for identifying and mitigating these biases, promoting fairness and transparency in AI decisions. 

  • Promotes Development of Fair and Unbiased AI Systems: By following the recommendations in ISO TR 24027, organizations can identify and address potential biases in AI systems, leading to fairer and more reliable outcomes. 
  • Minimizes Discriminatory Outcomes Associated with AI: Mitigating bias in AI systems helps ensure that AI decisions are fair and impartial, reducing the risk of discrimination against individuals or groups. 
  • Builds Trust in AI Decisions by Ensuring Fairness: Demonstrating a commitment to fairness in AI development fosters public trust in AI systems and encourages their widespread adoption with confidence. 
  • Data Scientists: Responsible for collecting, cleaning, and preparing data used to train AI models, ensuring data quality and addressing potential biases. 
  • Machine Learning Engineers: Developing AI models and algorithms with bias mitigation techniques in mind. 
  • AI Developers: Building and testing AI systems with awareness of potential biases and implementing strategies to minimize them. 

By implementing a proactive approach to identifying and mitigating bias in AI systems, organizations can: 

  • Avoid legal and reputational risks associated with biased AI decisions. 
  • Enhance the effectiveness and reliability of AI systems by ensuring fair and unbiased outcomes. 
  • Demonstrate leadership in responsible AI development and attract talent who value fairness in technology 
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