Evaluation

An evaluation is the process of monitoring and testing an artificial intelligence (AI) solution for accuracy, reliability, bias, and compliance.

What Is Evaluation?

No matter how advanced it is, any AI solution is prone to making mistakes on occasion. Regular evaluations can help human experts monitor AI to optimize their learning capabilities and generate better results. During evaluations, developers test and assess AI on several categories, including:

• Reliability
• Bias and discriminatory outputs
• Robustness of outputs
• Speed and accuracy
• Ethical and legal compliance

Systematic evaluations can occur in different ways. Human-in-the-loop (HITL) evaluation involves a human expert who assesses AI outputs and takes steps to optimize the machine-learning capabilities. Data-centric evaluation looks at the data that’s being fed into the AI to ensure it’s accurate and robust. Regardless of the approach, developers adapt their evaluation model to the AI type they’re assessing. A natural language processing machine, for example, might require more qualitative assessment, while a regression model might be more data-focused.

Related Terms

• Human in the Loop (HITL)
• Client-Side Testing
• Multivariate Testing
• Testing Hypothesis
• AI Agent

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