The article also stresses the importance of testing AI with diverse groups of real people to identify and mitigate biases. It warns of the dangers of AI producing incorrect responses or "hallucinations", particularly in critical areas like engineering calculations or medical diagnoses. The author concludes that testing generative AI should include comprehensive validation testing, performance evaluations, bias detection and mitigation, and human scrutiny in real-world situations.
Key takeaways:
- Applause surveyed over 3,000 digital quality testing professionals and found that 90% are concerned about bias in generative AI, and 67% believe AI services infringe on data privacy.
- Companies need to address personal information and privacy, attribution, responsible data gathering, and ensure outcomes that are correct, unbiased and not harmful.
- Testing generative AI is a nuanced process that should include comprehensive validation testing, performance evaluations, bias detection and mitigation and human scrutiny in real-world situations.
- Feedback from real people is essential to ensure that the models are continuously learning and improving, and the content is trustworthy, accurate and free of harm.