AI and machine learning often operate as 'black boxes,' making it difficult to understand how they produce results. This opacity can lead to challenges in ensuring data quality and making informed decisions. For instance, a large language model once generated a seating chart that included a nonexistent name, highlighting the potential for errors that could have been caught with proper data quality checks.

Organizations should focus on evaluating AI tools for reproducibility. If different iterations of a test yield inconsistent results, it indicates potential issues with the tool. Reliable AI tools should produce consistent and predictable outputs, which is essential for maintaining trust in their results.

Bias in AI can lead to significant issues, such as the exclusion of qualified candidates in hiring processes based on biased training data. Organizations need to be aware of how their AI tools are trained and ensure that they do not perpetuate existing biases. Implementing strict guidelines for AI usage and monitoring compliance with privacy laws can help mitigate these risks.