How Data Availability and Cost Relate to AI Differentiation?
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Can Decision Autonomy of an AI Be Distinguished from Malfunction?
I wrote in another note (here) that AI cannot decide autonomously because it does not have self-made preferences. I argued that its preferences are always a reflection of those that its designers wanted it to exhibit, or that reflect patterns in training data. The irony with this argument is that if an AI is making…
Requirements Loops: Definition & Purpose
A “Requirements Loop” is an evidence-supported explanation of How observed events in an environment have led or are leading to the creation and persistence of those requirements, How to change the environment in order to satisfy the requirements in the future, and How to measure the change in the environment, in order to evaluate the…
Building University Spin-Offs from Research on Decision-Making
This short interview on my research on decision making and use of it in companies, was done in 2018 with fnrs.tv, part of the Belgian Fonds de la Recherche Scientifique – FNRS, in Brussels. Each of my first two academic books led to the founding of a spin-off; see the books here.
What Is the Depth of Expertise of an AI Training Dataset?
I use “depth of expertise” as a data quality dimension of AI training datasets. It describes how much a dataset reflects of expertise in a knowledge domain. This is not a common data quality dimension used in other contexts, and I haven’t seen it as such in discussions of, say, quality of data used for…
When (if ever) Is a Claim Objective?
“Objective”, as in, for example, “what I’m saying is objective”, or “that statement is objective”, or “we need objective criteria when making these decisions”, is a complicated term. It takes a lot of effort to make sure it is understood as intended (or closely enough). It is therefore a costly word to use. Why is…