To streamline Artificial Intelligence (AI) processing, it’s often necessary to focus only on the most essential elements, filtering out irrelevant details. In knowledge representation, this process involves simplifying complex information, allowing AI systems to concentrate on crucial data and operate more efficiently. Essentially, some components are treated as “black boxes” while attention is given to those that can be more clearly understood or explained symbolically.

While abstraction is valuable for enhancing efficiency and manageability in AI systems, it can also have unintended consequences. By filtering out certain details, abstraction may inadvertently exclude critical factors with ethical or societal implications. For example, important contextual or human-centered information might be overlooked, leading to biased or harmful outcomes.

Ensuring responsible abstraction in AI requires careful consideration of what is included and excluded in the process. It’s essential to understand the ethical impact of these decisions, particularly how they might affect individuals or communities. A responsible approach to abstraction should strike a balance between simplifying complex systems and maintaining enough context to ensure fair, transparent, and ethical AI outcomes.

Sources

AI Abstraction by Stephen M. Walker II, Co-Founder / CEO https://klu.ai/glossary/abstraction

When prompted with “Abstraction in AI Responsibility” the ChatGPT-generated text that listed elements required in Value of use such as Simplification of Complex Systems, Layered Systems and Distributed Responsibility, Algorithmic Bias and Responsibility, Moral and Ethical Decisions, Legal Frameworks “As AI systems become increasingly sophisticated, it is essential to find ways to ensure that human responsibility is not obscured or diluted by the complexity of these systems” (GTP4 , 2024 https://chatgpt.com/).