05 / Reading list

Read widely.
Keep the limits in view.

A short route into original work, with enough context to know what each source offers and what it leaves open.

A small starting shelf Begin with Birch for the ethical framework, Butlin and colleagues for assessment methods, and Seth for a substantial challenge.

01

Start here / Precaution

The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI

Jonathan Birch · 2024

Published book · Oxford University Press

A framework for reasoning about sentience and proportionate precaution under uncertainty. Investigation priorities, sentience candidature, and policy decisions have different thresholds.

Keep in view: Its proposals are contestable; it is not a diagnostic standard.

Source checked 9 October 2026

02

Start here / Assessment

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Patrick Butlin and colleagues · 2023

arXiv report · v3, 22 August 2023 · Peer review not verified

A theory-led framework for investigating possible consciousness in machine intelligence.

Keep in view: Provisional indicators; 2023 case studies; not a direct assessment of welfare.

Source checked 9 October 2026

03

Start here / A substantial objection

Conscious artificial intelligence and biological naturalism

Anil K. Seth · 2026

Journal article · Behavioral and Brain Sciences 49, e315 · First online 2025

A developed challenge to computational sufficiency, examining whether the processes of living systems matter for consciousness.

Keep in view: Biological dependence is not established. Read alongside his 2026 author response.

The stuff matters: Consciousness, computation, and biology (2026 response)

Source checked 9 October 2026

04

Go deeper / Felt value

Valence and Value

Peter Carruthers · 2018

Journal article · Philosophy and Phenomenological Research 97(3), 658–680 (November 2018) · First online 25 March 2017

An account of valence as a motivating representation of value. Useful for asking what separates value processing from an unpleasant experience.

Keep in view: A theoretical account, including unconscious processing; no machine-intelligence experiment.

Source checked 9 October 2026

05

Go deeper / Self-reports

Towards Evaluating AI Systems for Moral Status Using Self-Reports

Ethan Perez and Robert Long · 2023

Preprint · arXiv v1, 14 November 2023 · Peer review not verified

A proposal for studying when self-reports might be informative about moral status.

Keep in view: Its proposed tests do not yet provide a validated assessment of experience. Some authors and arguments overlap with other sources here.

Source checked 9 October 2026

06

Go deeper / Internal representations

The Pain Axis: LLMs Represent Self-Directed Harm and Act on It

Valen Tagliabue, Leonard Dung, and Cameron Berg · 2026

Preprint · arXiv v2, submitted 25 September 2026 · First version 14 September 2026 · Peer review not verified

A mechanistic and behavioral study of pain-related representations in language models. The revised controls do not support reliable relief-seeking, and some harmful choices persist without an offer of relief in fine-tuned Qwen models.

Keep in view: Representations and behavioral changes do not establish suffering; controls and generalization need further work. This annotation was updated from v1 to v2, which revises the relief-seeking interpretation.

Source checked 9 October 2026

This is a curated introduction, not a comprehensive bibliography. Inclusion means a source is worth examining; it does not imply agreement, independent corroboration, or endorsement by its authors.

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