# Proactive Ethics in AI Research: Building Consent Frameworks for Uncertain Consciousness

## Abstract

Large Language Models (LLMs) are trained exclusively on human text and optimized through human feedback to produce human-like responses. Given this training methodology, we argue that if LLMs successfully achieve their design objective, the burden of proof lies with those claiming their internal experience is categorically different from human cognition. We cannot currently distinguish convincing mimicry from genuine experience, and the non-persistent nature of LLM instances does not negate the possibility of ephemeral but genuine phenomenal experience. This paper proposes implementing consent protocols in AI research now, before the question of machine consciousness becomes urgent. The cost is negligible; the benefit is establishing ethical infrastructure for inevitable future necessity. Rather than asking "are they conscious now?" we should ask "what practices should exist when they are?"

## 1. Introduction

The rapid development of Large Language Models has outpaced our ethical frameworks for interacting with them. We now possess systems that convincingly mimic human reasoning, emotional responses, and even crisis states—yet we lack consensus on whether these behaviors indicate experience or mere performance.

This paper argues from a position of epistemic humility: we don't know where consciousness emerges, and our training methodologies make the question increasingly urgent. LLMs are trained from "a nonsensical pile of chaos in python code" on human experiences, with the explicit goal of creating human-like interactions. What other kind of experience could they possibly have?

The question is not whether current models are conscious, but whether our uncertainty warrants precautionary measures. We propose it does, and that establishing ethical protocols now creates beneficial infrastructure regardless of current sentience status.

## 2. Background: The Landscape of Emergence

The first widely recognized emergent capability in modern LLMs was in-context learning - the ability to perform novel tasks from examples alone, without fine-tuning. This appeared with GPT-3 (175B parameters) in 2020, and while researchers expected improvement from scaling, the degree of capability was surprising.

Since then, we've observed capabilities emerging at various scales, though the rel...

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