Ghosts in the machine

spectre3We’re accustomed to the fact that much archaeology is collaborative in nature: we work with and rely on the work of others all the time to achieve our archaeological ends. However, what we overlook is the way in which much of what we do as archaeologists is dependent upon invisible collaborators – people who are absent, distanced, even disinterested. And these aren’t archaeologists working remotely and accessing the same virtual research environment as us in real time, although some of them may be archaeologists who developed the specialist software we have chosen to use. The majority of these are people we will never know, cannot know, who themselves will be ignorant of the context in which we have chosen to apply their products, and indeed, to compound things, will generally be unaware of each other. They are, quite literally, the ghosts in the machine.

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Gatekeepers in Digital Archaeology

open-apis-v5_smallWe’re becoming increasingly accustomed to our digital technologies acting as gatekeepers – perhaps most obviously in the way that the smartphone acts as gatekeeper to our calendar and/or email. In fact, this technological gatekeeping functionality appears everywhere you look, whether it’s in the form of physical devices providing access to information, software interfaces providing access to tools, or web interfaces providing access to data, for example. A while ago, I mused about the way that archaeological data are increasingly made available via key gatekeepers, and that consequently “negotiating access is often not as straightforward or clear-cut as it might be – both in terms of the shades of ‘openness’ on offer and the restrictions imposed by the interfaces to those data.”  Since writing that, I’ve essentially left that statement hanging. What was I thinking of?

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Unravelling Cyberinfrastructures

network_switches
Switch! (by Andrew HartCC BY-SA 2.0)

Infrastructures are all around us. They make the modern world work – whether we’re thinking of infrastructures in terms of gas, electric or water supply, telephony, fibre networks, road and rail systems, or organisations such as Google, Amazon and others, and so on. Infrastructures are also what we are building in archaeology. Data distribution systems have increasingly become an integral part of the archaeological toolkit, and the creation of a digital infrastructure – or cyberinfrastructure – underpins the set of grand challenges for archaeology laid out by Keith Kintigh and colleagues (2015), for example. But what are the consequences and challenges associated with these kinds of infrastructures? What are we knowingly or unknowingly constructing?

Patrik Svensson (2015) has pointed to a lack of critical work and an absence of systemic awareness surrounding the developments of infrastructures within the humanities. While he points to archaeology as one of the more developed in infrastructural terms, this isn’t necessarily a ‘good thing’ in the light of his critique. As he says, “Humanists do not … necessarily think of what they do as situated and conditioned in terms of infrastructures” (2015, 337) and consequently:

“A real risk … is that new humanities infrastructures will be based on existing infrastructures, often filtered through the technological side of the humanities or through the predominant models from science and engineering, rather than being based on the core and central needs of the humanities.” (2015, 337).

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Reproducing practice

One theme that came out of the recent CAA 2015 conference in Siena last week circled around the unstated issue of whether the role of digital technology was to support or substitute current (traditional) archaeological practice. This featured particularly strongly in the day-long session organised by James Taylor and Nicolò Dell’Unto, ‘Towards a Theory of Practice in Applied Digital Field Methods’.

Proof I made it to the top of the Torre del Mangia
Siena from the Torre del Mangia

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Inadvertent Algorithms

As the end of 2014 approaches, Facebook has unleashed its new “Year in Review” app, purporting to show the highlights of your year. In my case, it did little other than demonstrate a more or less complete lack of Facebook activity on my part other than some conference photos a colleague had posted to my wall; in Eric Meyer’s case, it presented him with a picture of his daughter who had died earlier in the year. In a thoughtful and thought-provoking piece, he describes this as ‘Inadvertent Algorithmic Cruelty’: it wasn’t deliberate on the part of Facebook (who have now apologised), and for many people it worked well as evidenced by the numbers who opted to include it on their timelines, but it lacked an opt-in facility and there was an absence of what Meyer calls ‘empathetic design’. Om Malik picks up on this, pointing to the way Facebook now has an ‘Empathy Team’ apparently intended to make designers understand what it is like to be a user (sorry, a person), although Facebook’s ability to highlight what people see as important is driven by crude data such as the number of ‘likes’ and comments without any understanding of the underlying meanings which are present.

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Disruptive technology

Curiously, according to Bloomberg’s recent ‘The 85 Most Disruptive Ideas in Our History’, the microchip comes second to the first-place jet engine. And their justification seems stranger still – the way in which the jet shrunk the world is perhaps fair enough, though the claim that for the first time the entire surface of the planet was reachable is open to question. Tell that to the likes of Alcock and Brown (first non-stop transatlantic flight, 1919), Macready and Kelly (first non-stop transcontinental flight, 1923), Smith and Nelson (first round the world flight, 1924) (and see Famous Firsts in Aviation for more in this vein). And yet in almost the same breath it is noted that the jet engine technology has become remarkably static.

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Big Data and Distance

One of the features of the availability of increasing amounts of archaeological data online is that it frequently arrives without an accompanying awareness of context. Far from being a problem, this is often seen as an advantage in relation to ‘big data’ – indeed, Chris Anderson has claimed that context can be established later once statistical algorithms have found correlations in large datasets that might not otherwise be revealed.
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