EthicAlly: a Prototype for AI-Powered Research Ethics Support for the Social Sciences and Humanities

Steph Grohmann
7/15/2025
cs.HCcs.AIcs.CY

No confident code implementation found for this paper.

Abstract

In biomedical science, review by a Research Ethics Committee (REC) is an indispensable way of protecting human subjects from harm. However, in social science and the humanities, mandatory ethics compliance has long been met with scepticism as biomedical models of ethics can map poorly onto methodologies involving complex socio-political and cultural considerations. As a result, tailored ethics training and support as well as access to RECs with the necessary expertise is lacking in some areas, including parts of Europe and low- and middle-income countries. This paper suggests that Generative AI can meaningfully contribute to closing these gaps, illustrating this claim by presenting EthicAlly, a proof-of-concept prototype for an AI-powered ethics support system for social science and humanities researchers. Drawing on constitutional AI technology and a collaborative prompt development methodology, EthicAlly provides structured ethics assessment that incorporates both universal ethics principles and contextual and interpretive considerations relevant to most social science research. In supporting researchers in ethical research design and preparation for REC submission, this kind of system can also contribute to easing the burden on institutional RECs, without attempting to automate or replace human ethical oversight.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{grohmann2025ethically,
  title  = {EthicAlly: a Prototype for AI-Powered Research Ethics Support for the Social Sciences and Humanities},
  author = {Steph Grohmann},
  year   = {2025},
  eprint = {2508.00856},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.00856v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2508.00856.svg)](https://codeofpaper.com/paper/2508.00856)

Discussion