The manufacturing industry and its end customers are placing ever higher quality demands. Instead of manual quality inspection, which is sometimes inefficient and prone to errors, individual industries are increasingly relying on automated inspections, for example by means of optical inspection systems and subsequent image processing. However, traditional image processing reaches its limits especially when there is a high variability of defects and/or parts. Machine learning (ML) methods, a subfield of artificial intelligence (AI), are increasingly making it possible to overcome these limitations. These approaches are trained entirely on data without the need for explicit rules provided by humans. Features of ML that differ from traditional software/AI approaches and make qualification difficult include limited explainability of decision making, insufficient robustness to small changes in input data, or the lack of meaningful criteria that can be used as proof of qualification. There is also a lack of appropriate standards and development methods to demonstrate the suitability/qualification of an ML-based AI system. Thus, concerns about reliability or accuracy of ML-based AI systems have so far prevented their widespread industrial use.