Quality Assurance for AI: An Inevitable Tradeoff
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Opaque, Complex, Biased, and Unpredictable AI
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…
Machine/AI as Inventor? Notes on Thaler v. USPTO
Can “an artificial intelligence machine be an ‘inventor’ under the Patent Act”? According to the Memorandum Opinion filed on September 2, 2021, in the case 1:20-cv-00903, the US Patent and Trademark Office (USPTO) requires that the inventor is one or more people [1]. An “AI machine” cannot be named an inventor on a patent that…
Can an Artificial Intelligence Trained on Large-Scale Crawled Web Data Comply with the Algorithmic Accountability Act?
If an artificial intelligence system is trained on large-scale crawled web/Internet data, can it comply with the Algorithmic Accountability Act? For the sake of discussion, I assume below that (1) the Act is passed, which it is not at the time of writing, and (2) the Act applies to the system (for more on applicability,…
What Is an Explanation?
Many people spent a lot of time, across centuries, trying to build good explanations, and trying to distinguish good from bad ones. While there is no consensus on what “explanation” is (always and everywhere), it is worth knowing what good explanations may have in common. It helps develop a taste in explanations, which is certainly helpful given how frequently you may need to explain something, and how often others offered explanations to you.