# Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraints

arXiv: https://arxiv.org/abs/2511.16582 · HTML: https://mlsanity.com/paper/2511.16582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 75 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Taking AI Welfare Seriously](https://mlsanity.com/paper/2411.00986/) (2024)
- [Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms](https://mlsanity.com/paper/2212.14787/) (2022)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [Sources of Richness and Ineffability for Phenomenally Conscious States](https://mlsanity.com/paper/2302.06403/) (2023)
- [The Singularity May Never Be Near](https://mlsanity.com/paper/1602.06462/) (2016)
- [Report on Candidate Computational Indicators for Conscious Valenced Experience](https://mlsanity.com/paper/2404.16696/) (2024)
- [The assumptions that restrain us from understanding consciousness](https://mlsanity.com/paper/2506.21485/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness](https://mlsanity.com/paper/2512.12802/) (2025)

## Abstract (arXiv)

We develop a taxonomical framework for classifying challenges to the possibility of consciousness in digital artificial intelligence systems. This framework allows us to identify the level of granularity at which a given challenge is intended (the levels we propose correspond to Marr's levels) and to disambiguate its degree of force: is it a challenge to computational functionalism that leaves the possibility of digital consciousness open (degree 1), a practical challenge to digital consciousness that suggests improbability without claiming impossibility (degree 2), or an argument claiming that digital consciousness is strictly impossible (degree 3)? We apply this framework to 14 prominent examples from the scientific and philosophical literature. Our aim is not to take a side in the debate, but to provide structure and a tool for disambiguating between challenges to computational functionalism and challenges to digital consciousness, as well as between different ways of parsing such challenges.