Category: black box

Slow AI

Jeremy Huggett
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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Faith, Trust, and Pixie Dust

Jeremy Huggett
Trust - broken egg
Adapted from original image by Kumar’s Edit, CC BY 2.0 Deed

It’s been some time since I last blogged, largely because my focus has lain elsewhere in recent months writing long-form pieces for more traditional outlets. The most recent of these considers the question of trust in digital things, a topic spurred by the recent (and ongoing) scandal surrounding the Post Office Horizon computer system here in the UK which saw the false conviction for theft, fraud, and false accounting of hundreds of people. One of the things that came to the fore as a result of the scandal was the way that English law presumes the reliability of a computer system:

In effect, the ‘word’ of a computational system was considered to be of a higher evidential value than the opinion of legal professionals or the testimony of witnesses. This was not merely therefore a problem with digital evidence per se, but also the response to it. (McGuire and Renaud 2023: 453)

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Discovery Machines

Jeremy Huggett
A model robot reading a kindle
Adapted from the original by Brian J. Matis (CC BY-NC-SA 2.0)

Michael Brian Schiffer is perhaps best-known (amongst archaeologists of a certain age in the UK at least), for his development of behavioural archaeology, which looked at the changing relationships between people and things as a response to the processual archaeology of Binford et al. (Schiffer 1976; 2010), and for his work on the formation processes of the archaeological record (Schiffer 1987). But Schiffer also has an extensive track record of work on archaeological (and behavioural) approaches to modern technologies and technological change (e.g., Schiffer 1992; 2011) which receives little attention in the digital archaeology arena, in part because despite his interest in a host of other electrical devices involved in knowledge creation (e.g., Schiffer 2013, 81ff) he has little to say about computers beyond observing their use in modelling and simulation or as an example of an aggregate technology constructed from multiple technologies and having a generalised functionality (Schiffer 2011, 167-171).

In his book The Archaeology of Science, Schiffer introduces the idea of the ‘discovery machine’. In applying such an apparatus,

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HARKing to Big Data?

Jeremy Huggett
Aircraft Detection Before Radar
A 1920s aircraft detector

Big Data has been described as revolutionary new scientific paradigm, one in which data-intensive approaches supersede more traditional scientific hypothesis testing. Conventional scientific practice entails the development of a research design with one or more falsifiable theories, followed by the collection of data which allows those theories to be tested and confirmed or rejected. In a Big Data world, the relationship between theory and data is reversed: data are collected first, and hypotheses arise from the subsequent analysis of that data (e.g., Smith and Cordes 2020, 102-3). Lohr described this as “listening to the data” to find correlations that appear to be linked to real world behaviours (2015, 104). Classically this is associated with Anderson’s (in)famous declaration of the “end of theory”:

With enough data, the numbers speak for themselves … Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. (Anderson 2008).

Such an approach to investigation has traditionally been seen as questionable scientific practice, since patterns will always be found in even the most random data, if there’s enough data and powerful enough computers to process it.

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Towards a digital ethics of agential devices

Jeremy Huggett
Image by Rawpixel CC0 1.0 via Creative Commons

Discussion of digital ethics is very much on trend: for example, the Proceedings of the IEEE special issue on ‘Ethical Considerations in the Design of Autonomous Systems’ has just been published (Volume 107 Issue 3), and the Philosophical Transactions of the Royal Society A published a special issue on ‘Governing Artificial Intelligence – ethical, legal and technical opportunities and challenges’ late in 2018. In that issue, Corinne Cath (2018, 3) draws attention to the growing body of literature surrounding AI and ethical frameworks, debates over laws governing AI and robotics across the world and points to an explosion of activity in 2018 with a dozen national strategies published and billions in government grants allocated. She also notes the way that many of the leaders in both debates and the technologies are based in the USA which itself presents an ethical issue in terms of the extent to which AI systems mirror the US culture rather than socio-cultural systems elsewhere around the world (Cath 2018, 4).

Agential devices, whether software or hardware, essentially extend the human mind by scaffolding or supporting our cognition. This broad definition therefore runs the gamut of digital tools and technologies, from digital cameras to survey devices (e.g. Huggett 2017), through software supporting data-driven meta-analyses and their incorporation in machine-learning tools, to remotely controlled terrestrial and aerial drones, remotely operated vehicles, autonomous surface and underwater vehicles, and lab-based robotic devices and semi-autonomous bio-mimetic or anthropomorphic robots. Many of these devices augment archaeological practice, reducing routinised and repetitive work in the office environment and in the field. Others augment work by developing data-driven methods which represent, store, and manipulate information in order to undertake tasks previously thought to be uncomputable or incapable of being automated. In the process, each raises ethical issues of various kinds. Whether agency can be associated with such devices can be questioned on the basis that they have no intent, responsibility or liability, but I would simply suggest that anything we ascribe agency to acquires agency, especially bearing in mind the human tendency to anthropomorphize our tools and devices. What I am not suggesting, however, is that these systems have a mind or consciousness themselves, which represents a whole different ethical set of questions.

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A Push Button Archaeology

Jeremy Huggett
Adapted from original by włodi (via Wikimedia Commons) CC-BY-SA 2.0

Buttons figure large in the world around us. Just in the last year we’ve seen everything from presidents boasting about the size of their nuclear buttons to Apple being faced with a class action over the failure of their new ‘improved’ butterfly keys to Amazon’s Dash buttons being barred in Germany for not providing information about price prior to being pressed. In archaeology, we’ve become accustomed to buttons and button-presses generating data, performing analyses, and presenting results, ranging across the digital instruments we employ and the software tools we rely on. So, to pick a random example, “researchers will be able to compare ceramics across thousands of sites with a click of the button.” (Smith et al 2014, 245).

Rachel Plotnick has recently discussed the place of buttons in our cultural imaginary:

… push a button and something magical begins. A sound erupts that seems never to have existed before. A bomb explodes. A vote registers. A machine animates, whirling and processing. A trivial touch of the finger sets these forces in motion. The user is all powerful, sending the signal that turns on a television, a mobile phone, a microwave. She makes everything go. Whether or not she understands how the machine works, she determines the fate of the universe. (Plotnick 2018, xiv).

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Explainability in digital systems

Jeremy Huggett
Created via http://www.hetemeel.com/

Some time ago, I suggested that machine-learning systems in archaeology ought to be able to provide human-scale explanations in support of their conclusions, noting that many of the techniques used in ML were filtering down into automated methods used to classify, extract and abstract archaeological data. I concluded: “We would expect an archaeologist to explain their reasoning in arriving at a conclusion; why should we not expect the same of a computer system?”.

This seemed fair enough at the time, if admittedly challenging. What I hadn’t appreciated, though, was the controversial nature of such a claim. For sure, in that piece I referred to Yoshua Bengio’s argument that we don’t understand human experts and yet we trust them, so why should we not extend the same degree of trust to an expert computer (Pearson 2016)? But it transpires this is quite a common argument posited against claims that systems should be capable of explaining themselves, not least among high-level Google scientists. For example, Geoff Hinton recently suggested in an interview that to require that you can explain how your AI systems works (as, for example, the GDPR regulations do) would be a disaster:

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Cyborg Archaeology

Jeremy Huggett
The Mechanical Mind
CC0 public domain. Original image by aytuguluturk

When we hear of augmentation in digital terms, these days we more often than not think of augmented or mixed reality, where digital information, imagery etc. is overlain on our view of the real world around us. This is, as yet, a relatively specialised field in archaeology (e.g. see Eve 2012). But digital augmentation of archaeology goes far beyond this. Our archaeological memory is augmented by digital cameras and data archives; our archaeological recording is augmented by everything from digital measuring devices through to camera drones and laser scanners; our archaeological illustration is augmented by a host of tools including CAD, GIS, and – potentially – neural networks to support drawing (e.g. Ha and Eck 2017); our archaeological authorship is augmented by a battery of writing aids, if not (yet) to the extent that data structure reports and their like are written automatically for us (for example).

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Looking for explanations

Jeremy Huggett
miracle_cure
(US Food and Drug Administration – Public Domain)

In 2014 the European Union determined that a person’s ‘right to be forgotten’ by Google’s search was a basic human right, but it remains the subject of dispute. If requested, Google currently removes links to an individual’s specific search result on any Google domain that is accessed from within Europe and on any European Google domain from wherever it is accessed. Google is currently appealing against a proposed extension to this which would require the right to be forgotten to be extended to searches across all Google domains regardless of location, so that something which might be perfectly legal in one country would be removed from sight because of the laws of another. Not surprisingly, Google sees this as a fundamental challenge to accessibility of information.

As if the ‘right to be forgotten’ was not problematic enough, the EU has recently published its General Data Protection Regulation 2016/679 to be introduced from 2018 which places limits on the use of automated processing for decisions taken concerning individuals and requires explanations to be provided where an adverse effect on an individual can be demonstrated (Goodman and Flaxman 2016). This seems like a good idea on the face of it – shouldn’t a self-driving car be able to explain the circumstances behind a collision? Why wouldn’t we want a computer system to explain its reasoning, whether it concerns access to credit or the acquisition of an insurance policy or the classification of an archaeological object?

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Unconscious Bias

Jeremy Huggett
stencil
Modified from the original by grahamc99. CC-BY-2.0

My employer has decided to send all those of us involved in recruitment and promotion on Unconscious Bias training, in recognition that unconscious bias may affect our decisions in one way or another. Unconscious bias in our dealings with others may be triggered by both visible and invisible characteristics, including gender, age, skin colour, sexual orientation, (dis)ability, accent, education, class, professional group etc.. That started me thinking – what about unconscious bias in relation to digital archaeology?

‘Unconscious bias’ isn’t a term commonly encountered within archaeology, although Sara Perry and others have written compellingly about online sexism and abuse experienced in academia and archaeology (Perry 2014, Perry et al 2015, for example). ‘Bias’, on the other hand, is rather more frequently referred to, especially in the context of our relationship to data. Most of us are aware, for instance, that as archaeologists we bring a host of preconceptions, assumptions, as well as cultural, gender and other biases to bear on our interpretations, and recognising this, seek means to reduce if not avoid it altogether. Nevertheless, there may still be bias in the sites we select, the data we collect, and the interpretations we place upon them. But what happens when the digital intervenes?

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