On Friday 12 January 2018, curators Julie Freeman and Hannah Redler Hawes left work at London鈥檚 Open Data Institute confident that, come Monday morning, there would be at least a few packets of crisps in the office.
Artist 鈥荣 Vending Machine (2009; pictured below) sits in the ODI鈥檚 kitchen, one of the more venerable exhibits to have been acquired over the institute鈥檚 five-year programme celebrating data as culture. It has been hacked to dispense a packet of salty snacks whenever the BBC鈥檚 RSS feed carries a news item containing financial misfortune.
No one could have guessed that, come 7 am on Monday morning, Carillion, the UK government鈥檚 giant services contractor, would have gone into liquidation. There were so many packets in the hopper, no one could open the door, say staff.
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Such apparently silly anecdotes are the stuff of this year鈥檚 show, the fifth in the ODI鈥檚 annual exhibition series 鈥淒ata as Culture鈥�. This year, humour and absurdity are being harnessed to ask big questions about internet culture, privacy and artificial intelligence.
Looking at the world through algorithmic lenses may bring occasional insight, but what really matters here are the pratfalls as, time and again, our machines misconstrue a world they cannot possibly comprehend.
In 2017, artist Pip Thornton fed famous poems to Google鈥檚 online advertising service, Google AdWords, and printed the monetised results on till receipts. The framed results value the word 鈥渃loud鈥� (as in I Wandered Lonely as a Cloud by William Wordsworth) highly, at 拢4.73, presumably because Google鈥檚 algorithm was dreaming of internet servers. It had no time at all for Wilfred Owen: 鈥淔roth-corrupted鈥� (Dulce et Decorum Est) earned exactly 拢0.00.
You can, of course, reverse this game and ask what happens to people when they over-interpret machine-generated data, seeing patterns that aren鈥檛 there.
This is what Lee Montgomery has done with Stupidity Tax (2017). In an effort to understand his father鈥檚 mild but unaccountably secretive gambling habit, Montgomery has used a variety of data analysis techniques to attempt to predict the UK National Lottery. The sting in this particular tale is , which implies (mischievously, I hope) that the whole tongue-in-cheek effort has driven the artist ever so slightly mad.
Cute and scary
Watching over the whole exhibition 鈥� literally because it鈥檚 peeking through a hole in a ceiling tile 鈥� is Franco and Eva Mattes鈥檚 , a taxidermied realisation of the internet meme, and a comment on the nature of surveillance beliefs (pictured top). 鈥淚t鈥檚 cute and scary at the same time,鈥� the artists say, 鈥渓ike the internet.鈥�
Co-curator Freeman is a data artist herself. If you visited last year鈥檚 you may well have seen her naked mole-rat surveillance project. The 7.5 million data points acquired by the project are now keeping network analysts busy at Queen Mary University of London. 鈥淲e want to know if mole-rats make good encryption objects,鈥� says Freeman. Their nest behaviours might generate true random numbers, handy for data security. 鈥淏ut the mole-rat queens are far too predictable鈥� Crisp?鈥�
Through a mouthful of salt and vinegar, I ask Freeman where her playfulness comes from. And as I suspected, there鈥檚 intellectual steel beneath: 鈥淒ata is being constantly visualised so we can comprehend it,鈥� she says, 鈥渁nd those visualisations are often done in a very short space of time, for a particular purpose, in a particular context, for a particular audience. Then they acquire this afterlife. All of a sudden, they鈥檙e the lenses we鈥檙e looking through. If you start thinking about data as something rigid and objective and bearing the weight of truth, then you鈥檝e stopped discerning what is right and what is wrong.鈥�
Freeman wants us to analyse data, not abandon it, and her exhibition is an act of tough love. 鈥淲hen we fetishise data, we end up with what鈥檚 happening in social media,鈥� she says. 鈥淪o many people drowning in metadata, pointing to pointers, and never acquiring any knowledge that鈥檚 deep and valuable. There should be some words to express that glut, that need to roll back a little bit. Here, have another crisp.鈥�
, Open Data Institute, London
