Connecting the Dots: Metacognitive Scaffolds for a Polymathic Approach to AI Ethics

"When one tugs at a single thing in nature, he finds it is attached to the rest of the world." - philosoph John Muir

Due to my active engagement with AI — from avidly using AI tools and training others on them to lecturing on AI ethics — I am deeply concerned with the need to marry technology with humanism.

As a polymathic researcher, I instantly think of the need for a polymathic approach here, as this can be key. The growing supercomplexity craves polymathic solutions!

One research paper caught my attention by addressing exactly that. Jakob Stenseke's paper, "Interdisciplinary Confusion and Resolution in the Context of Moral Machines, " illuminates the path to ethical AI as fraught with conceptual confusion and disciplinary divides. How can we, as a society, ensure that the machines of tomorrow respect the moral values of today? The answer lies in the power of interdisciplinary collaboration.

Imagine you're trying to put together a complex jigsaw puzzle, but each piece comes from a different set. At first glance, the pieces don't seem to fit together; they come from different pictures and thus seem incompatible. This is similar to the challenge faced when different academic disciplines try to collaborate — each brings its own concepts, methods, and language, which might not readily align with those from another field.

Metacognitive Scaffolds as Tools for Translation

To tackle these interdisciplinary challenges, Stenseke introduces the concept of metacognitive scaffolds, which act as translators in a multilingual meeting. These scaffolds aid in articulating and clarifying the assumptions, methods, and goals of various disciplines, promoting deeper mutual understanding. Essentially, these tools function as bridges across the gaps of misunderstanding, clarifying the diverse assumptions, methodologies, and objectives each discipline contributes. By enhancing this understanding, we facilitate a dialogue where philosophical insights and technological innovations synergize, improving both the ethics and functionality of AI.

The Stakes of Collaboration

The imperative for a polymathic collaboration in AI ethics is stark. Consider the broader implications:

• Ethical Depth: Integrating insights from philosophy, social sciences, and technology ensures that AI systems are grounded in a comprehensive ethical framework, reflecting our complex moral landscape.
• Technical Robustness: Polymathic contributions help translate ethical concepts into executable algorithms, ensuring that AI applications perform ethically in diverse real-world scenarios.

Thus, metacognitive scaffolds in interdisciplinary research act as these guiding frameworks, helping bridge the conceptual gaps between disciplines such as philosophy and engineering in the context of AI ethics.

They allow researchers to see beyond the immediate confines of their specialized knowledge and find meaningful ways to integrate their insights with those from other fields, creating a unified and effective approach to complex problems like ethical AI development. It's a beautiful solution — and fittingly, the great Leonardo da Vinci emphasized that the right technical solutions should be beautiful!

The future of ethical AI calls us to transcend the stark binary of code and creed. It seek a constellation of minds — philosophers, engineers, ethicists, and technologists — to scatter their combined wisdom like stars across the firmament of technology. This celestial gathering could transform the way ethics are interwoven into AI, ensuring our digital advancements not only serve but also illuminate our societal values.

And, ultimately, why should we settle for less?