What Do You Get when You Mix AI, PR, and HR?
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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…
What is a “Decision” in an Artificial Intelligence System?
In the context of human decision making, a decision is a commitment to a course of action (see the note here); it involves mental states that lead to specific actions. An AI system, as long as it is a combination of statistical learning algorithms and/or logic, and data, cannot have mental states in the same…
Private Data Use Consent as a Generative AI Compliance Requirement
In a previous note, here, I wrote that one of the requirements for Generative AI products/services in China is that if it uses data that contains personal information, the consent of the holder of the personal information needs to be obtained. It seems self-evident that this needs to be a requirement. It is also not…
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…
Opaque, Complex, Biased, and Unpredictable AI
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…
What Is an Explanation?
Many people spent a lot of time, across centuries, trying to build good explanations, and trying to distinguish good from bad ones. While there is no consensus on what “explanation” is (always and everywhere), it is worth knowing what good explanations may have in common. It helps develop a taste in explanations, which is certainly helpful given how frequently you may need to explain something, and how often others offered explanations to you.