Quality Assurance for AI: An Inevitable Tradeoff

How do you ensure #quality of a #service which uses #AI for #personalization? #QA in that case is all about risk management. pic.twitter.com/IdKcY5noBL
— ivanjureta (@ivanjureta) January 31, 2018
Compliance to relevant laws is increasingly recognized as a critical, but also expensive, quality for software requirements. Laws contain elements such as conditions and derogations that generate a space of possible compliance alternatives. During requirements engineering, an analyst has to select one of these compliance alternatives and ensure that the requirements specification she is putting…
Opacity, complexity, bias, and unpredictability are key negative nonfunctional requirements to address when designing AI systems. Negative means that if you have a design that reduces opacity, for example, relative to another design, the former is preferred, all else being equal. The first thing is to understand what each term refers to in general, that…
If a requirement can be easily rephrased as a criterion in a decision problem (as I argued elsewhere), then what is advice?
Just like l’art pour l’art, or art for the sake of art was the bohemian creed in the 19th century, it looks like there’s an “AI for the sake of AI” creed now when building general-purpose AI systems based on Large Language Models. Let’s say that the aim for a sustainable business are happy, paying,…
We should reduce the cost of authorship and create an incentive mechanism that generates and assigns credibility to authors in a community.