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Algorithmic Accountability Act of 2022 and AI Design
The Algorithmic Accountability Act of 2022, here, is a very interesting text if you need to design or govern a process for the design of software that involves some form of AI. The Act has no concept of AI, but of Automated Decision System, defined as follows. Section 2 (2): “The term “automated decision system”…
Critical Decision Concept in the Algorithmic Accountability Act
The Algorithmic Accountability Act of 2022, here, applies to systems that help make, or themselves make (or recommend) “critical decisions”. Determining if something is a “critical decision” determines if a system is subject to the Act or not. Hence the interest in the discussion, below, of the definition of “critical decision”. The Act defines a…
AI Compliance at Scale via Embedded Data Governance
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…
Adaptability | Paper | Papers | Quality
Context-Driven Autonomic Adaptation of Service Level Agreements
Service Level Agreements (SLAs) are used in Service-Oriented Computing to define the obligations of the parties involved in a transaction. SLAs define the service users’ Quality of Service (QoS) requirements that the service provider should satisfy. Requirements defined once may not be satisfiable when the context of the web services changes (e.g., when requirements or…
Black Box Approach to AI Governance
As currently drafted (2024), the Algorithmic Accountability Act does not require the algorithms and training data used in an AI System to be available for audit. (See my notes on the Act, starting with the one here.) The way that an auditor learns about the AI System is from documented impact assessments, which involve descriptions…
