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Attention-enhancing information retrieval

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Last week I was at SWIRL, the occasional talkshop on the future of information retrieval. To me the most important of the presentations was Diane Kelly’s “Rage against the Machine Learning”, in which she observed the way information retrieval currently works has changed the way people think. In particular, she proposed that the combination of short query with snippet response has reworked peoples’ plastic brains to focus on working memory, and forgo the processing of information required for it to lay its tracks down in our long term memory. In short, it makes us transactionally adept, but stops us from learning.

Unfortunately, Diane’s observation did not make it into one of the half-dozen “big tasks” in the future of IR. (There was a session on information literacy and “smart IR” that I thought was heading in that direction but ended up being about e-learning.) In fact, while there was the obligatory talk of the importance of “understanding the user”, there was scarcely any talk (aside from Diane) about trying to understand the user’s—our—true predicament in the contemporary information age—harassed, distracted, incessantly processing disparate information with little of it sinking in to us and little of us permeating into it. (How often do you end a day of work, or an evening of web surfing, with little recollection of what you actually did, saw, or thought?)

Information retrieval scientists think of the user like lab scientists think of rats (Diane’s example), observing their reactions to various stimuli; or at best as horse trainers think of horses, figuring out how to optimize their performance. In other words, they are viewed from the outside, through their behaviour. It is rare to think about matters from the user’s—the sentient, autonomous human’s—perspective. We only feel we are being serious if we approach the user through observational, repeatable, and measurable, studies. That, I think, is misguided: understanding the user should involve understanding ourselves. We need less information science, and more information humanities. That is why my proposed catch-cry for the next forty years of information retrieval talkfests is “fewer user studies!”.

As if to underline the inability to think of the user as an autonomous, sentient being, rather than as a repository for stimuli and behaviours—the failure, in Kantian terms, to think of people as ends, rather than mean—one of the grand goals for the future of IR that did get up at SWIRL was zero-query and less-than-zero-query search—the notion that it is not you that should be querying the retrieval system, but the retrieval system that should proactively be telling you what to do. Added to all the distractions that we’re currently subjected to from other human-initiated sources—emails, phone calls, instant messages—would be carefully personalized machine-generated prompts. Various fanciful scenarios were given, but the ultimate end-point of such a research direction is that you walk into the shopping mall, and then your mobile phone leads you round telling you what to buy.

When I suggested this at the meeting, various people said “this is just an information tool, it is up to you what you do with it” and “you underestimate people, they are not stupid”; but the reality of it is that the technology we have today has shaped our way of thinking and behaving, without our choosing it. If a convincing, well-tuned, sympathetic machine learning algorithm starts directing people what to do, and directs people to do things that they get (short-term) satisfaction from, then people will initially choose, and then gradually learn, to let their mobile phone tell them what to do.

Edit, 2012-02-27: fixed Dianne to Diane

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