Ready-to-hand Dean Eckles blogs on people and technology

The Wall Street Journal’s Venture Capital Dispatch reports on how Aardvark, the social question asking and answering service recently acquired by Google, used a Wizard of Oz prototype to learn about how their service concept would work without building all the tech before knowing if it was any good.

Aardvark employees would get the questions from beta test users and route them to users who were online and would have the answer to the question. This was done to test out the concept before the company spent the time and money to build it, said Damon Horowitz, co-founder of Aardvark, who spoke at Startup Lessons Learned, a conference in San Francisco on Friday.

“If people like this in super crappy form, then this is worth building, because they’ll like it even more,” Horowitz said of their initial idea.

At the same time it was testing a “fake” product powered by humans, the company started building the automated product to replace humans. While it used humans “behind the curtain,” it gained the benefit of learning from all the questions, including how to route the questions and the entire process with users.

This is a really good idea, as I’ve argued before on this blog and in a chapter for developers of mobile health interventions. What better way to (a) learn about how people will use and experience your service and (b) get training data for your machine learning system than to have humans-in-the-loop run the service?

My friend Chris Streeter wondered whether this was all done by Aardvark employees or whether workers on Amazon Mechanical Turk may have also been involved, especially in identifying the expertise of the early users of the service so that the employees could route the questions to the right place. I think this highlights how different parts of a service can draw on human and non-human intelligence in a variety of ways — via a micro-labor market, using skilled employees who will gain hands-on experience with customers, etc.

I also wonder what UIs the humans-in-the-loop used to accomplish this. It’d be great to get a peak. I’d expect that these were certainly rough around the edges, as was the Aardvark customer-facing UI.

Aardvark does a good job of being a quite sociable agent (e.g., when using it via instant messaging) that also gets out of the way of the human–human interaction between question askers and answers. I wonder how the language used by humans to coordinate and hand-off questions may have played into creating a positive para-social interaction with vark.

The Library of Congress has announced that it will be archiving all Twitter posts (tweets). You can find positive reaction on Twitter. But some have also wondered about privacy concerns. Fred Stutzman, for example, points out how even assuming that only unprotected accounts are being archived this can still be problematic.¹ While some people have Twitter usernames that easily identify their owners and many allow themselves to be found based on an email address that is publicly associated with their identity, there are also many that do not. If at a future time, this account becomes associated with their identity for a larger audience than they desire, they can make their whole account viewable only by approved followers², delete the account, or delete some of the tweets. Of course, this information may remain elsewhere on the Internet for a short or long time. But in contrast, the Library of Congress archive will be much more enduring and likely outside of individual users’ control.³ While I think it is worth examining the strategies that people adopt to cope with inflexible or difficult to use privacy controls in software, I don’t intend to do that here.

Instead, I want to relate this discussion to my continued interest in how activity streams and other information consumption interfaces affect their users’ beliefs and behaviors through the availability heuristic. In response to some comments on his first post, Stutzman argues that people overestimate the degree to which content once public on the Internet is public forever:

So why is it that we all assume that the content we share publicly will be around forever?  I think this is a classic case of selection on the dependent variable.  When we Google ourselves, we are confronted with what’s there as opposed to what’s not there.  The stuff that goes away gets forgotten, and we concentrate on things that we see or remember (like a persistent page about us that we don’t like).  In reality, our online identities decay, decay being a stochastic process.  The internet is actually quite bad at remembering.

This unconsidered “selection on the dependent variable” is one way of thinking about some cases of how the availability heuristic (and use of ease-of-retrievel information more generally). But I actually think the latter is more general and more useful for describing the psychological processes involved. For example, it highlights both that there are many occurrences or interventions can can influence which cases are available to mind and that even if people have thought about cases where their content disappeared at some point, this may not be easily retrieved when making particular privacy decisions or offering opinions on others’ actions.

Stutzman’s example is but one way that the combination of the availability heuristic and existing Internet services combine to affect privacy decisions. For example, consider how activity streams like Facebook News Feed influence how people perceive their audience. News Feed shows items drawn from an individual’s friends’ activities, and they often have some reciprocal access. However, the items in the activity stream are likely unrepresentative of this potential and likely audience. “Lurkers” — people who consume but do not produce — are not as available to mind, and prolific producers are too available to mind for how often they are in the actual audience for some new shared content. This can, for example, lead to making self-disclosures that are not appropriate for the actual audience.

1. This might not be the case, see Michael Zimmer and this New York Times article. [↩]
2. Why don’t people do this in the first place? Many may not be aware of the feature, but even if they are, there are reasons not to use it. For example, it makes any participation in topical conversations (e.g., around a hashtag) difficult or impossible. [↩]
3. Or at least this control would have to be via Twitter, likely before archiving: “We asked them [Twitter] to deal with the users; the library doesn’t want to mediate that.” [↩]

Search queries have been used recently to “predict the present“, as Hal Varian has called it. Now some initial use of Twitter chatter to predict the future:

The chatter in Twitter can accurately predict the box-office revenues of upcoming movies weeks before they are released. In fact, Tweets can predict the performance of films better than market-based predictions, such as Hollywood Stock Exchange, which have been the best predictors to date. (Kevin Kelley)

Here is the paper by Asur and Huberman from HP Labs. Also see a similar use of online discussion forums.

But the obvious question from my previous post is, how much improvement do you get by adding more inputs to the model? That is, how does the combined Hollywood Stock Exchange and Twitter chatter model perform? The authors report adding the number of theaters the movie opens in to both models, but not combining them directly.

Simplicity has its draws. A simple model of some phenomena can be quick to understand and test. But with the resources we have today for theory building and prediction, it is worth recognizing that many phenomena of interest (e.g., in social sciences, epidemiology) are very, very complex. Using a more complex model can help. It’s great to try many simple models along the way — as scaffolding — but if you have a large enough N in an observational study, a larger model will likely be an improvement.

One obvious way a model gets more complex is by adding predictors. There has recently been a good deal of attention on using the frequency of search terms to predict important goings-on — like flu trends. Sharad Goel et al. (blog post, paper) temper the excitement a bit by demonstrating that simple models using other, existing public data sets outperform the search data. In some cases (music popularity, in particular), adding the search data to the model improves predictions: the more complex combined model can “explain” some of the variance not handled by the more basic non-search-data models.

This echos one big takeaway from the Netflix Prize competition: committees win. The top competitors were all large teams formed from smaller teams and their models were tuned combinations of several models. That is, the strategy is, take a bunch of complex models and combine them.

One way of doing this is just taking a weighted average of the predictions of several simpler models. This works quite well when your measure of the value of your model is root mean squared error (RMSE), since RMSE is convex.

While often the larger model “explains” more of the variance, what “explains” means here is just that the R-squared is larger: less of the variance is error. More complex models can be difficult to understand, just like the phenomena they model. We will continue to need better tools to understand, visualize, and evaluate our models as their complexity increases. I think the committee metaphor will be an interesting and practical one to apply in the many cases where the best we can do is use a weighted average of several simpler, pretty good models.

TechCrunch and others have been joking about Apple’s rejection of an app because it uses shiny chat bubbles, which the Apple representative claimed were trademarked:

Chess Wars was being rejected after the six week wait [because] the bubbles in its chat rooms are too shiny, and Apple has trademarked that bubbly design. [...] The representative said Stump needed to make the bubbles “less shiny” and also helpfully suggested that he make the bubbles square, just to be sure.

One thing that is quite striking in this situation is that it is at odds with Apple’s long history of strongly encouraging third-party developers to follow many UI guidelines — guidelines that when followed make third-party apps blend in like they’re native.¹

It’s important to not read too much into this (especially since we don’t know what Apple’s more considered policy on this will end up being), but it is interesting to think about how responsibility gets spread around among mobile applications, services, and devices — and how this may be different than existing models on the desktop.My sense is that experienced desktop computer users understand at least the most important ways sources of their good and bad experiences are distinguished. For example, “locomotion” is a central metaphor in using the Web, as opposed to the conversation and manipulation metaphors of the command line / natural language interfaces and WIMP: we “go to” a site (see this interview with Terry Winograd, full .mov here). The locomotion metaphor helps people distinguish what my computer is contributing and what some distant, third-party “site” is contributing.

This is complex even on the Web, but many of these genre rules are currently being all mixed up. Google has Gmail running in your browser but on your computer. Cameraphones are recognizing objects you point them at — some by analyzing the image on the device and some by sending the device to a server to be analyzed.

This issue is sometimes identified by academics as one of source orientation and source equivocality. Though there has been some great research in this area, there is a lot we don’t know and the field is in flux: people’s beliefs about systems are changing and the important technologies and genres are still emerging.

If there’s one important place to start thinking about the craziness of the current situation of ubiquitous source equivocality is “Personalization of mass media and the growth of pseudo-community” (1987) by James Beniger that predates much of the tech at issue.

1. I was led to think this by a commenter on TechCrunch, Dan Grossman, pointing out this long history. [↩]