AIASC 350: AI System Intangibles
Purpose and Scope:
This document provides guidelines for recognizing, measuring, and presenting intangible assets arising from AI operations. It focuses on non-monetary assets without physical substance that are pivotal in AI endeavors, such as algorithms, patents, and licenses.
1. Principle of Intangible Identification:
- Clearly identify and classify AI-related intangible assets based on their nature, origin, and expected utility.
2. Principle of Initial Recognition:
- Recognize AI intangibles at cost if they are expected to provide future economic benefits and the cost can be reliably measured.
3. Principle of Subsequent Measurement:
- Measure AI intangibles either at cost less accumulated amortization and any accumulated impairment losses or at a revalued amount.
4. Principle of Amortization:
- Systematically amortize the intangible's cost over its useful life, unless the life is considered indefinite.
5. Principle of Impairment Evaluation:
- Assess AI intangibles for impairment regularly, ensuring their carrying value does not exceed their recoverable amount.
6. Principle of Disclosure:
- Transparently disclose the nature, carrying amount, useful life, amortization method, and any other relevant information about AI intangibles.
7. Principle of Derecognition:
- Derecognize AI intangibles when they no longer provide economic benefits or are disposed of, recognizing any resulting gain or loss.
8. Principle of Research vs. Development:
- Distinguish between costs associated with research activities and those associated with the development of AI intangibles, recognizing only the latter as assets when criteria are met.
Updates and Amendments:The AIASC 350 guidelines will be periodically reviewed and updated to reflect advancements in AI technology, evolving practices in recognizing and measuring intangibles, and feedback from stakeholders and the public.
Note: This is a fictional representation and does not represent any real-world standard for AI. The development of such standards would involve extensive consultations with experts, stakeholders, and the public. Fictional representations simplify complex AI concepts, stimulate discussion, envision future scenarios, highlight ethical considerations, encourage creativity, bridge knowledge gaps, and set benchmarks for debate in fields like accounting.