We require our research community to take accountability and authorship of their work, transparently declaring any use of AI in line with our policies.

AI has many benefits, when used responsibly, to enhance discovery, broaden inclusivity, and protect the trustworthiness of the scientific record. Springer Nature supports the responsible and transparent use of AI where it strengthens research quality, upholds editorial independence, and preserves the integrity of the scientific and scholarly record.  

We place human-centred values at the heart of our approach to the responsible use of AI, and these are reflected in our AI Principles and editorial policies. We expect our authors, editors, and peer reviewers to follow our AI Principles. We firmly believe AI supports, not replaces human expertise, and a human should always be accountable to edit and fact-check original work.

The Springer Nature AI Ethics and Policy Forums consistently monitor changes in regulations, technology as well as global and community expectations and update our AI policies in line with these as needed.

For our detailed policies and information relating to AI use including images, videos and generative AI tools, please see below supported by the full policy documents at the links below.

Guidance for researchers, editors and reviewers on the use of AI in publishing

Springer Nature’s AI policies set out an assessment framework governing how AI may be used across the research and publishing lifecycle, applied consistently to authors, peer reviewers, and editors, while recognising their distinct roles and responsibilities.

AI is treated as a supporting technology. Scholarly judgement, accountability, and responsibility always remain human.

Rather than focusing on whether AI has been used, the policies ask each user to consider:

  • how AI has been used
  • the potential impact of that use
  • the level of risk introduced
  • whether appropriate human oversight has been maintained
  • whether transparency and accountability have been preserved

Core expectations

  1. Human accountability is non‑transferable: Accountability for scholarly content, evaluation, and editorial decisions cannot be delegated to AI systems. 
  2. AI may support, but must not replace, scholarly judgement: AI can assist clarity, efficiency, and exploration, but must not determine conclusions, evaluations, or decisions. 
  3. Transparency creates trust and confidence: Transparent declaration of AI builds trust and confidence, and removes ambiguity about how AI might have been used. 
  4. Confidentiality and data protection are mandatory: Manuscripts, peer review reports, and sensitive data must not be shared with unsecured or public AI systems, always in compliance with the applicable local laws. 

Use

Green - Permitted

Assistive AI use

Amber - Exercise caution

Evaluative or interpretive AI use

Red - Not permitted

AI replacing scholarly judgement or lacking transparency

Summary

AI use that supports expression, organisation, or efficiency without influencing scientific, scholarly or evaluative judgement.

AI use that may influence interpretation, framing, emphasis, or evaluative judgement, but remains under human control.

AI use that is opaque or replaced accountable human contribution, generates unverifiable outputs, compromises confidentiality or integrity, or violates applicable laws.

Expectations

  • AI use is likely to be reversible and verifiable
  • AI use does not introduce new intellectual content
  • Accountability for scholarly or evaluative judgement remains clearly human
  • AI contributes to reasoning or critique
  • AI does introduce new intellectual content and requires verification and oversight
  • Human judgement and accountability must be demonstrable
  • AI use is opaque of creates non-verifiable outputs
  • Authors, reviewer or editorial responsibility has been delegated to AI
  • AI use has breached confidentiality, consent, or rights of others

Examples

  • Using an LLM to polish or refine the language
  • Suggesting structure or formatting of manuscript sections
  • Translation, structuring or clarifying reviewer comments
  • Comparing methodological options
  • Stress-testing research questions
  • Data cleaning and deduplication
  • Suggesting analytical, experimental or methodological approaches
  • Drafting explanatory summaries
  • Comparing results to existing literature
  • Extensive copy editing or writing support
  • Pattern identification in exploratory data analysis
  • Explaining outputs from statistical models in plain language
  • Recommending statistical tests or modelling approaches
  • Generating hypotheses, analyses or conclusions and presenting them as human-derived
  • Fabricating data, citations or results
  • Using an LLM to generate core research reasoning without disclosure
  • Assigning authorship or accountability to AI systems or tools
  • Delegating peer review to an LLM
  • Creating photorealistic images (deepfakes)

Compliance and disclosure

Permitted. Disclosure enhances trust and transparency and clearly demonstrates human accountability.

Permitted with human oversight, verification, and transparency through disclosure.

If AI materially influences evaluative judgement, accountability must remain clearly human-led and defensible.

Not permitted.

How can research published with Springer Nature be used in an AI context?

How is content published by Springer Nature used in an AI context?

Our focus is always on how we can best use AI to help researchers get published faster, find relevant content quickly, ensure the integrity of the academic record, and reduce barriers. We have been using AI for over 10 years already in support of these goals. It remains, though, a fast-moving area and we are always open to things which will improve the experience for our customers and the wider research community. When we do this, we will always apply the same considerations we have to date:

  • Does it benefit our customers and the wider research community? 
  • Is there a ‘human’ in the loop? 
  • Does it meet our AI governance principles of fairness, transparency, accountability, and respect?”

Who owns the copyright for research published with Springer Nature?

Authors retain copyright in their submitted and published work. For subscription articles, Springer Nature is granted by the authors an exclusive License to Publish (LtP) to protect their interests.

Can authors reuse their own work?

Yes. Authors can share and reuse their work freely for research, education, and other non-commercial purposes. Authors should refer to their publishing agreement for specific guidance.

What is ARC3?

ARC3 is Springer Nature’s data licensing solution designed to support research, analytics and AI. Further information can be found here: https://www.springernature.com/gp/rd/research-intelligence

Can academic customers/ institutions use Springer Nature content within an AI context?

AI training and third-party access are not allowed unless explicitly licensed. Academic customers/ institutions may use Springer Nature content for internal AI use, – i.e. use by staff or students at the institution in a teaching context.  Springer Nature also offers customised content services for institutions.

 Can commercial or corporate services use Springer Nature content within an AI context?

Yes, if the use aligns with Springer Nature’s AI guidelines and usage is explicitly agreed. Selected content may be reused and made available to external users, excluding any restricted under historic laws or contracts, but only where that re-use is licenced by Springer Nature.

How can third-parties use Springer Nature content within an AI context?

Third parties may be licensed to use select content for internal AI-powered tools. Measures are put in place, however, to ensure that content is not used beyond the agreed scope.  Attribution must be clearly provided if resulting content is made available externally and it is sufficiently specific, such as article summaries. This would be agreed as part of arranging suitable licences to use Springer Nature content.

What are the licensing terms for third-party use of Springer Nature content?

Use is limited to short summaries with proper attribution. Verbatim distribution is prohibited. Upon termination, all hosting or processing of content must cease.

Does Springer Nature license content for use in LLMs (Large Language Models)?

Only in limited cases. Licensing maybe considered for standalone LLM systems. Currently attribution to all the work (referring to content as outlined at the start of this guidance) that the model has been trained on is not practical. It remains, though, a fast-moving area and we hope solutions will be found for this. We are always open to things that will improve the experience for our authors, customers and the wider research community and we will continue to work with them to improve this.  Additionally, we will always apply the same considerations we have to date:

  • does it benefit our customers and the wider research community?
  • Is there a ‘human in the loop’?
  • Does it meet our AI governance principles of fairness, transparency, accountability, and respect?”
  • Does it meet the conditions of our ARC3 framework?

Does Springer Nature use the content it has published to power/ build its own internal AI tools?

In line with Springer Nature’s AI guidelines, selected content that we have published may be reused to develop Springer Nature tools that enhance publishing services and the research experience. Restricted content and opt-outs are excluded.

 Springer Nature is committed to adopting an ethically focused approach while using, designing, developing, and deploying AI-assisted solutions. We design and use solutions which contain AI or are enabled by AI responsibly, making sure that we consider and mitigate any negative impact, be it societal or environmental.

You can contact: