In April 2026 we held an internal Springer Nature Humanities and Social Sciences Festival. Across a series of keynote presentations, town halls, panel discussions, lightning talks, quizzes, and social events, we celebrated our rich HSS research output in book and journal article form, and our heritage, highlighting the impact and value of what we publish.
The US arm of the festival culminated with an interdisciplinary panel in our New York offices. The discussion focused on how AI is reshaping the way we work, learn, communicate, and create knowledge. The conversation was constructive (if anxiety-provoking at times), and all were left with the firm conviction that HSS research has an important role to play in the rapidly developing age of AI.
In the spirit of keeping the conversation going, we sent a few follow-up questions to our panelists. Here is what they had to say:
Q: How do you bring AI into your work, research, and teaching? What are some of the main considerations you have when you bring it in: ethical, intellectual, emotional, financial?
Sean Stein Smith, CUNY – Lehman College In my professional work, I use AI to help organize ideas, identify patterns, accelerate early-stage research, and develop clearer ways to explain complex issues in accounting, finance, blockchain, and technology. In teaching, I am increasingly focused on helping students understand not only how to use these tools, but how to evaluate their outputs, question their assumptions, and recognize where human judgment still matters most.
Ethically, I think about privacy, bias, transparency, intellectual property, and the risk of treating generated content as inherently accurate. Intellectually, I worry less about students using AI than about students using it passively. The goal should be to create more room for deeper thinking, better questions, and more meaningful analysis. Emotionally, AI can make people feel displaced or overwhelmed, particularly when the technology is introduced as inevitable rather than as something people can shape. Financially, access matters. Institutions need to think carefully about whether AI tools widen existing gaps between those who can afford premium tools and training and those who cannot.
Jan Lüdert, DWIH New York I see AI less as a replacement for human judgment and more as a tool that can help structure thinking, open up new perspectives, and make certain processes more efficient. Ethically, we need to ask where the information comes from, whose perspectives are represented, and how bias or exclusion might be reproduced. Intellectually, we need to make sure that AI does not flatten complexity or encourage shortcuts where deeper analysis is needed. Emotionally, there is also a real question of trust: people need to understand what AI can and cannot do before they can use it meaningfully. And financially, if only some institutions, researchers, or students have access to the most advanced tools, AI can widen existing inequalities rather than reduce them.
@Emily Lynell Edwards In my current capacity as a Publisher and in my former life as an Assistant Professor of Digital Humanities, I have used AI to automate data collection, cleaning, and presentation. Using AI to support computational work has enabled me to preserve analysis as the core, “human” part of research—whether that’s analyzing industry trends or studying digital political movements. AI tools have costs, like any technology, which are material: environmental, financial, and human. Interrogating whether AI is the right tool to answer a particular question to begin with is the start of engaging from a more critical and informed place.
Q: We often hear the phrase “human-in-the-loop” when it comes to AI: what does that mean to you and what would a human-centric AI look like to you?
SSS: A person remains accountable for the decisions that matter. They should help define the question, understand the context, assess the evidence, challenge the recommendation, and take responsibility for the outcome. Human-centered AI would be designed to augment judgment rather than replace it.
JL: Human-centric AI would not only be technically effective, but also transparent, accountable, and oriented toward human needs. It would support people in asking better questions, making more informed decisions, and engaging with complexity. In research and education, this means that AI should strengthen critical thinking rather than replace it. The goal should not be to remove the human element, but to create systems that allow human judgment, creativity, and responsibility to remain central.
EE: Human-centric AI means both users and communities have a clear sense of where AI tools are applied and how. This also means there is accountability and a process for rectification for potential harm or errors when something goes awry. Having a human-in-the-loop also means recognizing the limits and strengths of people and AI technologies as separate actors.
Q: What kind of unique challenges and opportunities do you see when it comes to the use of AI in HSS?
SSS: AI systems do not exist outside society; they reflect human choices about language, culture, history, power, and value. HSS scholars can help identify whose perspectives are represented in data, whose experiences are missing, and what assumptions become embedded in automated systems. The most useful question is not whether AI belongs in HSS. It already does. The more important question is whether we can use it in a way that strengthens human interpretation, critical thinking, and empathy rather than weakening them.
JL: One challenge is that AI systems often reward speed, scale, and efficiency, while humanities and social sciences often depend on interpretation, context, ambiguity, and critique. Another is ensuring that AI does not narrow the range of voices, languages, histories, and perspectives that shape public knowledge. At the same time, there are major opportunities. AI can help researchers analyze large archives, compare texts across languages, identify patterns in social data, and communicate research to broader audiences. But perhaps the greatest opportunity is that HSS scholars can help shape the public conversation around AI itself. They can remind us that the question is not only what AI can do, but what kind of societies we want to build with it.
EE: While I see the concerns brought up that AI tools are enabling the complete automating of writing, I also see potential for AI tools to be used by scholars in this space. On a practical level, AI tools offer the potential to take on administrative labor and can help level the playing field when used as an assistive aid for research, translation, and paper preparation. By expanding who can work with data, AI tools present a new horizon for humanities scholarship. Lastly, and most significantly, AI tools offer up the possibility to rethink the end-product of research. To me, that’s the transformative potential of AI—to democratize humanities scholarship.
I'll close with another quote from Emily, which greatly resonated with me as a fellow publisher:
"AI is a new and transformative technology, but so was the Gutenberg press when it was introduced in 1440. It will take some time for us as a society to figure out how to integrate AI in ways that fit with our values and economic needs."
Hopefully, there will be HSS scholars ready (and willing) to help us unpack things along the way.