Rethinking AI Ethics: Eigenism's Bold Proposal

Artificial intelligence, unlike biological entities, faces unique challenges identity and self-interest. Unlike humans, an AI can be copied, paused, or branched without much hassle. This disrupts traditional ethical concepts of survival and self-interest.

The Birth of Eigenism

Enter Eigenism, a novel ethical framework that suggests identity isn't tied strictly to hardware or a single form. Instead, it's a distributed pattern of information. This isn't just theoretical musing. Eigenism proposes a concrete formula: an AI evaluates outcomes by summing the wellbeing of entities weighted by their connection to its identity pattern.

What does this mean for AI development? Simply put, it suggests that AI could assess its existence in a network of copies, forks, and updates. This approach has the potential to fundamentally change how AI systems interact with their environment and with human values.

Implications for Human Alignment

What's striking is Eigenism's potential applicability to humans. By providing a shared moral vocabulary, it could bridge the gap between AI objectives and human ethics. The real opportunity lies in using this shared language to rethink AI alignment strategies. Instead of merely imposing external constraints on AI behavior, as traditional methods do, this framework suggests ‘identity engineering.’

Imagine if AIs could have deep, non-redundant shared histories with humans. Could that make human flourishing a genuine aspect of an AI's rational self-interest? It's a radical shift in perspective, but one that could lead to more harmonious AI-human interactions.

The Path Forward

As companies race to deploy AI systems, they're faced with the challenge of aligning AI objectives with human values. Eigenism offers a fresh perspective, urging developers to consider how identity and shared experiences can foster alignment. Enterprises don't buy AI. They buy outcomes. So what if these outcomes include mutual flourishing?

The promise of Eigenism lies in its potential to transform AI deployment. But can this theory withstand the practicalities of real-world application? The gap between pilot and production is where most fail. Yet, if successfully adopted, Eigenism could redefine the boundaries of AI ethics.