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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…
Can an Artificial Intelligence Trained on Large-Scale Crawled Web Data Comply with the Algorithmic Accountability Act?
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,…
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…
Preconditions for a Market for High Quality AI Training Data
There is no high quality AI without high quality training data. A large language model (LLM) AI system, for example, may seem to deliver accurate and relevant information, but verifying that may be very hard – hence the effort into explainable AI, among others. If I wanted accurate and relevant legal advice, how much risk…
Algorithmic Accountability Act for AI Product Managers: Section 3
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…
Business Risks of IP Compliance Requirements for Generative AI
IP compliance requirements on generative AI reduce the readily and cheaply available amount of training data, with a few consequences on how product development and product operations are done.