- Frederic Clavert (C²DH, University of Luxembourg) firstname.lastname@example.org
- Sarah Gensburger (CSO, Sciences Po - CNRS) email@example.com
In addition to the publication of the forecast special issue, this CFP ambitions to identify a network of colleagues, across the two fields, who are interested in this issue and the development of co-conceived case studies and research programs. Do not hesitate to contact us beyond the sending of your abstract.
The definition of « collective memory » has been a major topic of discussion and dispute among scholars coming from humanities and social sciences interested in understanding the dynamics of remembrance (Gensburger 2016). However, and besides their different views, most of them agreed on the fact that “collective memory” was nothing like a “computer memory”: it was different from a stock of souvenirs and images of the past. In 2000, still, Liam Bannon suggested that « what has blinded us to the richness of human activities related to the theme of collective remembering has been the dominance of the computer model of mind in more recent cognitive psychology and artificial intelligence research, where models of human memory have been imported from computing” (Bannon 2000 p. 278).
Twenty years later, however, artificial intelligence and machine learning could seem to be the future of collective memory. In the aftermath of Covid, the need for mourning and an immediate visual memorialization of the dead relied on artificial intelligence to produce a memorial of portraits of all the victims of the Pandemic in the US based not on their actual pictures, impossible to gather in a non-biased practical way, but through representative portraits generated by an artificial intelligence using data available about the dead (age, ethnicity, gender, etc.) (Korostoff n.d.). Again, while, with the last witnesses of the Holocaust dying, a crisis of witnessing has been underway (Lothe et al. 2012), artificial intelligence has been increasingly used to give birth (again) to them and enable interaction between their hologram, school students or museum visitors (Makhortykh 2021b; Shur-Ofry and Pessach 2020; Walden 2021). Of course, this “New perspective on testimony”, as says the name of the program which created these holograms, has no reason to be limited to the Holocaust in the future. « Imagine, for example, an AI that integrates a large collection of testimonies from the Vietnam War, thus creating the “ultimate witness” – one that delivers an integrated testimony about the War – or a “virtual Abraham Lincoln” – one that relies on the 40,550 ‘Lincoln papers’ stored at the Library of Congress to answer people’s questions.” (Shur-Ofry and Pessach 2020 p. 988). Meanwhile, other works have highlighted what artificial intelligence and machine learning tools can bring to heritage and archives institutions (Barlindhaug 2022; Bultmann et al. 2022; Jaillant 2022; Pessanha and Salah 2021; Rolan et al. 2019), assessing the extent to which they are “the future of memory institutions” (Horsley 2020). Is artificial intelligence (really) the future of collective memory?
1. How can we meet the ethical challenges raised by the current development of artificial intelligence in the field of memory?
By raising this (slightly) provocative question, this special issue aims at fostering a dialogue between specialist in memory studies and in engineering and machine learning. It is now clear that these two fields could respectively benefit for an informed dialogue (Kvasnička and Pospíchal 2015). The issue of the relationship between collective memory and artificial intelligence has already been addressed by some colleagues. However, it is striking to see that most of these articles have focused on the ethical dimensions of it and on the policy needs implied by the development of artificial intelligence in the field of memory institutions, history and witnessing (Presner 2016).
The memory studies scholars, Victoria Grace Walden, Mykola Markhoryth and Kate Marrison, have coordinated an initiative for “Guidelines for Using Machine Learning and Artifical Intelligence for Holocaust Memory and Education”. The law specialists Shur-Ofry and Pessach have called for « memory fiduciaries » to be imposed to artificial intelligence (2020) and historian Wulf Kansteiner has been dreaming of a GPThistory, an adaptation of GPT3 for it to become an actual auxiliary to the production of history and an from their of collective memory : “If we think that the stories and images we consume influence our memories, identities, and future behavior, we should be very wary about letting AI craft our future entertainment on the basis of our morally and politically deeply flawed cultural heritage” (Kansteiner 2022 p. 124). These reflections on the regulation of the use of machine learning and artificial intelligence in the field of historical narration and collective memory are crucial and moving forward in this area of investigation is one of the ambitions of this special issue. However, we would like to invite contributors to also, and first and foremost, to move beyond these considerations and build some techno-ethnographies through case studies involving collective memory issues and machine learning processes. In doing so, we may investigate IA systems as actual practices of collective memory and, also, question what social impact will have the IA generated collective memory artefacts, such as memorials, in the future?
2. Is AI an indicator or a generator of collective memory?
This second group of questions we would like to address tackle the nature of the relationship between artificial intelligence and collective memory. To what extent AI and Machine Learning tools can help social sciences and humanities to overcome the methodological loopholes social sciences and humanities have been confronted with when trying to grasp and circumscribe “collective memory” in its shared narrative dimensions? Can AI help to make apparent often indivisible social frameworks of collective memory or will the emergence of IA, and the social uses it will lead to, change the very nature and functioning of “collective memory” (Makhortykh 2021a)? Can we speak of “Robotic collective memory” or “Algorithmic Memory” beyond simple metaphors? Should we consider these AI and Machine learning tools as a way to make visible “collective memory” or should we acknowledge the fact that they generate and promote a new form of “collective memory”?
In other words, can scholars’ works on collective memory use these new AI products as innovative research tools to critically and experimentally engage with the often given for granted “historical analogies” mechanism or the relationship between moral values and collective memory. Could we imagine that the very biased nature of AI – which is the reason why comments have so far been focusing on ethical issues -could make it a promising tool for exploring “collective memory” dynamics when defined as a socially structured and spatially located point of view of the past? The debate about ethics, collective memory and AI invites to concretely and explicitly list what is left of the concept of collective memory if it is separated from all its moral, and often left implicit, implications. In other words, this special issue considers that thinking critically about the relationship between collective memory and artificial intelligence can help the memory studies field to move forward. Here, we encourage theoretical precision about the notions of “collective memory” which contributors consider when addressing IA and Machine learning practices and development.
3. What memory studies can bring to the field of Artificial Intelligence?
This special issue also considers that memory studies can bring to the field of artificial intelligence. IA and Machine Learning tools are first and foremost memory products. However, most of the current remarks on the implications of this simple fact are very rarely informed by the knowledge memory studies works have established these past twenty years. Crossing these two literatures could help works on AI to move beyond the very notion of bias to pay attention to the organizational, spatially, and socially framed nature of all memory dynamics. Some different AI relied on different conceptions of memory and internal memorialization dynamics (Romero 2021). In this matter, memory studies have a lot to teach people developing “multi-agent systems” meant to deal with memory, but so far very few works have crossed the two literatures. We call for this cross-fertilization in order, for example, to build some typologies in this matter. Samely, the articulation between accuracy, authenticity, and exemplarity as between fictional and non-fictional has been at the core of public history and memory studies work for some time. However, it has not so far nourished the reflection about the future of artificial intelligence and its social uses.
Moreover, artificial intelligence memory is peculiar given the fact that the data used during the machine learning process have their own temporality and spatiality (Clavert et al. 2022). The more we go in the past or the more we go in “data poor” regions of the world, the less data are available to “nourish” the IA system. This very simple fact makes that the first and main “bias” of any IA system is one of temporality and spatiality, topics which has been at the core of memory studies since the very beginning.
Finally, the collective memory of artificial intelligence - as both an economic product and a symbolic object/actor -, its mythologies and its narratives are an important part of the investigation this special issue wants to foster. The way “history” of AI has been written through times has only recently started to be studied (Gefen 2022). This special issue ambition to have these promising and still ongoing work dialogue with the ones dealing with the effects of AI and Machine learning tools on the future of collective memory.
Frédéric Clavert is assistant professor at the Centre for Contemporary and Digital History (C²DH, University of Luxembourg). A member of the digital humanities community, he turned his attention to the study of the relationship between historians and their primary sources in the digital age on the one hand, and the use of massive data from web platforms in memory studies on the other. He led the #ww1 project around the Centenary of the Great War on Twitter. With Caroline Muller (Université Rennes 2), he is co-editor of the online book Le Goût de l’archive à l’ère numérique. He is managing editor of the Journal of Digital History.
Sarah Gensburger is Full Professor of sociology and political science at the French National Centre for Scientific Research and Sciences Po-Paris. She is the author of Beyond Memory. Can we really learn from the past? (with Sandrine Lefranc, Palgrave, 2020, also in Arabic, French and Spanish), Memory on my doorstep. Chronicles of the Bataclan Neighborhood (Paris, 2015-2016) (Leuven University Press, 2019) and the co-editor of Administrations of Memory (with Sara Dybris McQuaid (Springer, 2022). Her current research interests concern the relationship between neoliberalism, the crisis of the welfare state and the contemporary memory boom as well as the impact assessment of memory policies and the inequal access to participatory archiving. In 2021, she was elected President of the international Memory Studies Association.
Please send your abstract (up to two pages), highlighting your claims, your methodology and materials and including a short biography to firstname.lastname@example.org and email@example.com by 15 May 2023. Answers will be given to the authors by 22 May 2023 and the final texts, between 5 and 7 000 words long, will be expected by 1 October 2023. A first review by the editors will first be processed and the final submission on the Brill platform will take place in a second time.
Memory Studies Review has a specific focus on the relationship between memory and environments. The editors are interested in publishing work that explores memory in environments of different kinds, as well as research which considers how memory relates to our understanding of ecosystems and the Anthropocene. This current special issue will be the second volume of this new journal and will tackle AI as a new environment of and made of memory.
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Bultmann, D, Makhortykh, M, Simon, D, Ulloa, R, and Zucker, EM (2022) Digital Archive of Memorialization of Mass Atrocities (DAMMA) Workshop Whitepaper. Yale University Genocide Studies Program: Mass Atrocities in the Digital Era Working Paper, 3. Retrieved from Retrieved from https://gsp.yale.edu/digital-archive-memorialization-mass-atrocities-damma-workshop-whitepaper.
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