on the governance of decisions and advice
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
In the context of human decision making, a decision is a commitment to a course of action (see the note here); it involves mental states that lead to specific actions. An AI system, as long as it is a combination of statistical learning algorithms and/or logic, and data, cannot have mental states in the same…
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,…
Figures 1 and 2 show cost versus time; Figure 1 shows long iterations, Figure 2 short iterations. We choose to do something at time zero, at the origin of the graph in the Figure, and when we do so, we do it under some assumptions that we made at that time. Dashed red lines convey…
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…
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…
