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
 
			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…
 
			Is it one that led to the best outcome? Or one that integrates all the relevant and available information? Maybe one that is liked by a majority? If decision governance is followed to the letter, will that guarantee a high quality decision? The quality of a decision depends on the following: The reason a decision…
 
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			This text follows my notes on Sections 1 and 2 of the the Algorithmic Accountability Act (2022 and 2023). When (if?) the Act becomes law, it will apply across all kinds of software products, or more generally, products and services which rely in any way on algorithms to support decision making. This makes it necessary…