How Data Availability and Cost Relate to AI Differentiation?

When someone pitches me an #ai/#MachineLearning idea, I always (also) ask about #data availability, data cost, and how they relate to their product differentiation and #aitechnology. Here’s how I see them, roughly speaking. #strategy #AIstrategy #AIeconomics pic.twitter.com/v6yb8JOHwi
— ivanjureta (@ivanjureta) February 19, 2018
The short answer is “No”, and the reasons for it are interesting. An AI system is opaque if it is impossible or costly for it (or people auditing it) to explain why it gave some specific outputs. Opacity is undesirable in general – see my note here. So this question applies for both those outputs…
There are, roughly speaking, three problems to solve for an Artificial Intelligence system to comply with AI regulations in China (see the note here) and likely future regulation in the USA (see the notes on the Algorithmic Accountability Act, starting here): Using available, large-scale crawled web/Internet data is a low-cost (it’s all relative) approach to…
The Algorithmic Accountability Act of 2022, here, is a very interesting text if you need to design or govern a process for the design of software that involves some form of AI. The Act has no concept of AI, but of Automated Decision System, defined as follows. Section 2 (2): “The term “automated decision system”…
If an AI is not predictable by design, then the purpose of governing it is to ensure that it gives the right answers (actions) most of the time, and that when it fails, the consequences are negligible, or that it can only fail on inconsequential questions, goals, or tasks.