Introduction
AI is transforming our world, but ensuring its responsible use requires a focus on AI quality. This goes beyond just performance; it's about building trustworthy and beneficial AI systems. Let's delve deeper into the key aspects of AI quality:
What is AI Quality?
Key Aspects of AI Quality:
- Standards-Based Evaluation: The ISO/IEC TS 25058 standard provides a framework for evaluating AI systems based on an AI system quality model. This leverages existing best practices from software quality standards (SQuaRE) to assess aspects like requirements, design, testing, and ongoing evaluation.
- Data as the Foundation: High-quality data is crucial for effective and unbiased AI. Resources like Why Data Quality is Crucial for Successful AI Implementations explore the importance of good data in AI success.
- Understanding Bias: Data can be inherently biased, impacting AI results. Resources like Common Types of Data Bias (With Examples) can help identify and mitigate these biases.
Further Exploration:
- Quality characteristics for AI based systems provides additional details on quality characteristics for AI systems.
- What is AI Quality? A framework offers a broader framework for understanding AI quality.
- ISO 25010 Product Quality Model (Functional & Non-Functional Characteristics) can be explored to understand quality characteristics beyond AI-specific aspects.
By considering these aspects, we can build robust, ethical, and trustworthy AI for a positive future.