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Sprint projectAug 15, 2026Dhaka City

Digital Minds: Human–AI Coexistence Under Moral Uncertainty

Mirza Tairin · Team Digital Minds Research

Submitted to Digital Minds Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Digital Minds: Human–AI Coexistence Under Moral Uncertainty

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Project Summary: Aim: Develop an evidence-grounded conceptual framework for human–AI interaction under uncertainty about potential AI welfare/consciousness. Core approach: Review emerging empirical methods for characterizing potentially welfare-relevant properties of AI systems. Assess the evidential strength and limitations of these methods. Translate different levels of evidential uncertainty into conditional HCI and governance principles that balance human safety, human agency, and potential AI welfare. Core contribution: A framework for reasoning about human–AI interaction when AI welfare is uncertain, without presupposing that current AI systems are conscious or morally considerable. Future extension: Empirically test the framework through human-subject HCI studies and/or experimental evaluation of interaction policies. (From the research page, this would fall under conceptual contribution that sharpen how we individuate the entity of moral concern.)

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How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Concise and well written, this paper positions itself well within the existing literature which it cites. The paper is at times overly prescriptive but at other times makes clear that situational flexibility and context is important. Author is encouraged to keep writing and contributing to the field, and to explore technical implementation in future works.

  2. The paper asks an important question: how should AI safety governance proceed under conditions of moral uncertainty regarding the status of AI agents. The paper highlights directions for research rather than develop a fuller model.

    The question is interesting and important. The answer(s) is currently very preliminary -- basically, keeping track of emerging evidence and making allowance for the growing possibility of moral agency. I wish the paper also engaged with the possibility that we could (maybe) steer AI development towards more or less moral agency. (also, would be good idea to review more recent work on AI revealed preferences)

Cite this project

@misc{tairin2026digital,
  title = {{Digital Minds: Human–AI Coexistence Under Moral Uncertainty}},
  author = {Mirza Tairin},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/digital-minds-humanai-coexistence-under-moral-uncertainty-9u33}},
  url = {https://apartresearch.com/sprints/projects/digital-minds-humanai-coexistence-under-moral-uncertainty-9u33}
}

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