A Prehistory of Digital Archaeology

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The EDSAC computer at the University of Cambridge
The EDSAC computer at the Mathematical Laboratory, University of Cambridge (1949-1958)

As a species, we are fascinated by origins – as Maya Angelou, the American poet and civil rights activist is attributed as saying, “You can’t really know where you are going until you know where you have been”. Of course, much of archaeology is concerned one way or another with narratives around origins, be they people or pots, but digital archaeology seems much less interested in its own origins. As part of a recent presentation on the digitalisation of archaeology to the Norwegian Institute at Athens, I took the opportunity to look back at what I loosely called the ‘prehistory’ of digital archaeology as the basis for looking forward. What was digital archaeology like at its beginnings, at least seventy years or more ago? It turns out to be a surprisingly difficult question to answer: other than some important work by Paola Moscati and Francois Djindjian, for example, digital archaeologists are remarkably bad at thinking about our past.

The relative lack of attention to digital archaeology’s past could be seen to be a natural consequence of the ever faster developing digital environment, but it may also be a reflection of the significance that has been given to George Cowgill’s 1967(a,b) review of computer applications in archaeology. According to Cowgill, the earliest archaeological computer application he knew of was by Jean-Claude Gardin and Peter Ihm in France in 1958-1959 (1967a, 17). He also identified James Deetz as responsible for the earliest use in the USA in 1960. Cowgill’s statement has served as the basis for most subsequent reviews (e.g., Wilcock 1999, 35; Watrall 2016, 346; Huggett 2018) and has consequently become something of an origin myth. Not surprisingly, dig a little deeper, and it becomes clear that this is not the whole story.

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AI, Access, and Acknowledgement

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Black and white typewriter with white paper printed with the words Artificial Intelligence
Original photo by Markus Winkler on Unsplash

A number of archaeological/humanities websites are reporting problems with the sheer volume of traffic caused by web crawlers belonging to the various large language model (LLM) generative artificial intelligence systems like ChatGTP, Gemini, CoPilot, and Claude, for instance. Their impact can have the same effect as a Distributed Denial of Service attack, overwhelming the systems. Of late, Knowledge Commons has reported such problems (e.g. Mastodon post), as has Open Context (e.g. Blog post). As Eric Kansa describes it, the AI bots frequently ignore directives designed to protect the site against such activities while Kathleen Fitzpatrick says that dealing with the bots sent out by extractive AI companies threatens the future development of the Knowledge Commons site. Indeed, in the replies to her post, it’s clear that the problem is experienced widely – Reclaim (the company hosting this site) report similar problems, while Tom Elliot suggests the Pleiades Gazetteer might have to require logins even for read-only access. Hosting and internet infrastructure companies can do their best to block excessive traffic, but a blanket ban on access – by, for instance, disallowing an AI’s user agents via a robots.txt file (assuming they take notice of it) or trying to block them at the firewall – isn’t necessarily desirable if you specifically want your information to get wide circulation and use. But if you go down a more permissive route, what then?

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Data in Action

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TAG 2025 conference logo representing a megaphone
TAG 2025 conference logo

I was recently discussant for a TAG conference session on Data Activism (session 14) organised by Jeremy Foot, Joana Valdez-Tullett, and Stu Eve, all of Wessex Archaeology. Their session abstract expressed some dissatisfaction with the current uses to which archaeological data are put and challenged participants to consider a host of questions surrounding our use of data: what more we could do with it, why we might want to do more, and what stands in our way. Their call attracted an interesting variety of papers, ranging across data reuse cases (and associated problems), the sustainability problems associated with unexpected datasets, issues of comparability between different datasets with differing underlying data strategies, the unexpected application of data to unanticipated research questions, and different ways of perceiving and representing temporal dimensions in imagery, for instance.

Reading the abstracts and listening to the papers, it seemed to me that underlying this broad range of issues and approaches were questions relating to our relationships with data and the consequent implications for knowledge creation. From my notes on the presentations, I identified three key areas:

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Changing Interfaces

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Image of Canmore and Trove search pages
Search succession – Canmore (left) and Trove (right)

We seem to be living in interesting times here in the UK, at least as far as changes to major archaeological digital infrastructures providing access to national historic environment records and associated archive data are concerned. The Archaeology Data Service has radically updated ArchSearch with a new interface; in Scotland, the well-established Canmore has been replaced by the new Trove website; in England, the launch of a new Heritage Gateway is due by the end of the year. Novelty can be very unsettling and risky, especially when it involves the replacement of a long-established, familiar, and reliable, digital system. And of course, unwanted novelty can give rise to resistance and complaint. So how is that working out?

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In the Shadow of Digital Machine-Gods?

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Original image by NoName_13 via Pixabay

I recently used a citation analysis of the Computer Applications in Archaeology annual conference proceedings as a proxy for examining the spread of digital archaeology across the broader discipline (Huggett 2024). Admittedly to my surprise, the results seemed to show that the debates and methods of digital archaeology had significantly infiltrated the wider archaeological discipline, at least as evidenced in citations of CAA papers in books and journals outside of digital archaeology (although not conference proceedings).

But as if to burst my newly optimistic bubble about the influence of the work of digital archaeologists across the broader discipline, a chapter recently appeared which sought to examine the influence of digital technologies on archaeology (Ribeiro 2025). It does so almost entirely without reference to any work in the field of digital archaeology: Hacıgüzeller et al. (2021) is the only ‘digital’ reference in the piece. It’s perhaps why Ribeiro can boldly claim that “… little has been said about how technology has influenced the practice of archaeology itself.” (Ribeiro 2025, 45) since discussions and debates within digital archaeology on that topic appear to be entirely overlooked. But to put that aside, it does mean that the chapter provides an interesting viewpoint on digital matters from a perspective specifically outwith digital archaeology itself, and so, in a way, shines a different light on digital technology in archaeology. It’s a perspective that those of us working in digital archaeology can respond to even if, in many cases, we may think we have already done so.

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Ethereal Ethics

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Image by Nick Youngson; CC BY-SA 3.0; attributed to Alpha Stock Images; original image from Picpedia.Org

In certain respects, ethics can be regarded like the air that we breathe: they surround us, whether recognised or not, influencing our decisions, relationships, and institutions. In an upcoming book chapter (Huggett, forthcoming a), I perhaps rather boldly argue that there is no such thing as a coherent digital ethics in archaeology. I’m not suggesting that digital archaeologists are acting unethically. It’s more that digital ethics have a somewhat ethereal quality and are rarely made explicit, let alone comprehensively debated, and, where they are discussed (e.g., Colley 2015; Dennis 2020), this is often at a human rather than technological level (see the Computer Applications in Archaeology ethics policy, for instance). The absence of a more expansive digital ethics could lead us to over-estimate our ethical performance and that of our technologies, and make us blind to our – and their – ethical failings.

Ethical principles are a part of both everyday life and archaeological practice. They are assimilated through education, training, and practical experience, but they do not require to be written down to govern what passes as normative behaviour. The absence of a codified ethics therefore cannot be used to infer a lack of ethics although codification can provide guidance and reassurance. However, ethical expectations are not universal and differences between cultures and contexts can result in misunderstanding and offence. Nor is there a uniform strategy to ethics, as is demonstrated by the number of different approaches to ethics. For instance, utilitarian ethics emphasise the consequences of actions, whereas deontological approaches are concerned with duties and rules of behaviour, while virtue ethics emphasise the moral characteristics of individuals and groups rather than their specific actions. The reasoning involved in arriving at an ethical determination therefore can be quite different and may be more or less helpful in different circumstances. Consequently, Beever et al. (2020, pp. 37–39), for example, propose a pluralistic approach which considers the perspective of multiple ethical theories for a more practical, robust, and inclusive approach to decision-making. In archaeology, Carman (2024, p. 176) and González-Ruibal (2018, p. 355), for example, similarly propose pragmatic rather than instrumental approaches to ethics.

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AI and its Affordances

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Original image by Mike MacKenzie (CC BY 2.0.)

Putting aside the hype associated with artificial intelligence (and generative AI in particular), there’s a great deal of talk surrounding the application of AI within archaeology with numerous examples of AI approaches to automated classification, feature recognition, image analysis, and so on. But much of it seems fairly uncritical – perhaps because it is largely associated with exploratory and experimental approaches seeking to find a place for AI, although there is also something of a publication/presentation bias favouring positive outcomes (e.g., see Sobotkova et al. 2024, 7). AI is typically presented as offering greater efficiencies and productivity gains, and any negative effects, where noted, can be managed out of the equation. In many respects this conforms to the broader context of AI where it is often seen as possessing an almost mythical, ideological status, sold by large organisations as the solution to everything from overflowing mailboxes to the climate crisis.

Cobb’s survey of generative AI in archaeology (Cobb 2023) focusses on key application areas where genAI may be of meaningful use: research (e.g., Spenneman 2024), writing, illustration (e.g., Magnani and Clindaniel 2023), teaching, and programming (e.g., Ciccone 2024), although Cobb sees programming as the most useful with mixed results elsewhere. However, Spenneman (2024, 3602-3) predicts that genAI will affect how cultural heritage is documented, managed, practiced, and presented, primarily through its provision of analysis and decision-making tools. Likewise, Magnani and Clindaniel (2023, 459) boldly declare genAI to be a powerful illustrative tool for depicting and interpreting the past, enabling multiple perspectives and reinterpretations, but at the same time, admit that

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Slow AI

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Slow Down road sign
Image by Tristan Schmurr (CC 2.0)

None of us can be unaware of the hype surrounding artificial intelligence at the moment. We see multi-billion-dollar companies being built around generative AI while older more established companies are placing multi-billion-dollar bets on the development of generative AI to ensure they don’t miss out. Many of the chief executives of those same companies have warned of the imminent danger of artificial general intelligence when human cognition is surpassed by machines, arguing for greater regulation while at the same time ensuring that they invest in attempts to develop precisely what they warn about. Others are more alarmist still: Yuval Harari, the historian and writer, dramatically claimed last year that “What nukes are to the physical world…AI is to the virtual and symbolic world”, and that artificial intelligence is an alien threat potentially resulting in the end of human history. We’re told we have to take the welfare rights of artificial intelligence seriously because it will become conscious or at least robustly agentic within the next ten years (Long et al. 2024), even if a Google engineer was fired in 2022 for claiming its AI technology had become sentient and had a soul. Even the notorious failures – the tendencies of ChatGTP and its ilk to hallucinate and simply make things up, for instance – do little more than introduce a brief pause while such predispositions are inevitably programmed around.

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The Data Interface

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Painting of woman looking out of window by Friedrich
Detail from Woman at a Window, by Casper David Friedrich (1822)

We understand knowledge construction to be social and combinatorial: we build on the knowledge of others, we create knowledge from data collected by ourselves and others, and so on. Although we pay a lot of attention to the processes behind the collection, recording, and archiving of our data, and are concerned about ensuring its findability, accessibility, interoperability, and reusability into the future, we pay much less attention to the technological mediation between ourselves and those same data. How do the search interfaces which we customarily employ in our archaeological data portals influence our use of them, and consequently affect the knowledge we create through them? How do they both enable and constrain us? And what are the implications for future interface designs?

As if to underline the lack of attention to interfaces, it’s often difficult to trace their history and development. It’s not something that infrastructure providers tend to be particularly interested in, and the Internet Archive’s Wayback Machine doesn’t capture interfaces which use dynamically scripted pages, which writes off the visual history of the first ten years or more of development of the Archaeology Data Service’s ArchSearch interface, for example. The focus is, perhaps inevitably, on maintaining the interfaces we do have and looking forward to developing the next ones, but with relatively little sense of their history. Interfaces are all too often treated as transparent, transient – almost disposable – windows on the data they provide access to.

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Is Now the Winter of AI Discontent?

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Snow-covered road at Kanangra-Boyd National Park, NSW, Australia. Image by Toby Hudson CC BY-SA 3.0 via Wikimedia Commons

With Google’s introduction of ‘AI Overviews’ beginning to replace its traditional search engine, Apple launching its ‘Apple Intelligence’ system embedded in its latest variants of iOS, Adobe incorporating an AI Photo Editor in Photoshop, and so on, it’s fair to say that artificial intelligence – in the form of generative AI, at least – is infiltrating many of the digital tools and resources we are accustomed to rely upon. While many uncritically embrace such developments, others are asking whether such developments are desirable or even useful. Indeed, John Naughton (2023) suggests that we are currently in the euphoric stage of AI development and adoption which he predicts will soon be followed by a period of profit-taking before the AI bubble bursts.

In many ways, we’ve been here before. Haigh (2023, 35) describes AI as “… born in hype, and its story is usually told as a series of cycles of fervent enthusiasm followed by bitter disappointment”, and similarly,

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