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What is AI Governance for?
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.
Algorithmic Accountability Act for AI Product Managers: Section 5
Section 5 specifies the content of the summary report to be submitted about an automated decision system. This text follows my notes on Sections 1 and 2, Section 3 and Section 4 of the Algorithmic Accountability Act (2022 and 2023). This is the fourth of a series of texts where I’m providing a critical reading…
Achieving, Satisficing, and Excelling
Definitions of the concepts derived from the goal concept (including functional and nonfunctional goal, hardgoal, and softgoal) used in requirements engineering are discussed, and precise (and, when appropriate, mathematical) definitions are suggested. The concept of satisficing, associated to softgoals is revisited. A softgoal is satisficed when thresholds of some precise criteria are reached. Satisficing does…
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…
Algorithmic Accountability Act for AI Product Managers: Sections 1 and 2
The Algorithmic Accountability Act (2022 and 2023) applies to many more settings than what is in early 2024 considered as Artificial Intelligence. It applies 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 for any product manager…