New blog-posts around the web: Crowdsourcing and Alan Turing

This blog has been quiet for a while but I had two posts at the CASTAC blog in the past two months.

The first one, titled Crowdsourcing the Expert, points out that computer scientists have now turned their attention to more sophisticated forms of crowdsourcing: not just crowds of uniform homogeneous click-workers, but also crowds of experts.  The crowdsourcing platform is now seen as a manager, not just for the unskilled worker, but also for the creative classes?   And what about the expertise of computer scientists themselves which is left fairly undefined? Anyway, read the whole thing if it strikes your interest.

The second one is about Alan Turing.  I summarize some recent articles on Alan Turing and computer science published by historians Thomas Haigh and Edgar Daylight,  where they suggest that some of the recent commemorations of Alan Turing are not quite historically accurate.  But fascinating nonetheless because they show us how computer science, as a discipline, was constituted.

The unintended consequences of technical tools: the demise of sharing in Google Reader and why I will miss it

One of the first things we learn in fields like STS, HCI or Design Studies is that technologies often get used in ways that their designers have not imagined, and that this is both wonderful and productive, but can also be subversive and frustrating (for different people).  It's particularly trying for a designer: after all, the most important thing about design is to make people behave in a certain way, through certain kinds of artifacts (the button that is the only thing that can be clicked, the door-knob that has to be turned in order to pull the door, etc.) - and yet try as you might, it's never quite possible to get people to behave in exactly the way you want.  However, sometimes, having users do things with your design is a boon; it makes the application more flexible and increases its value.  One of the things about good designers then is that their designs are useful, rigid but not too rigid, and leave just enough room for all those workarounds, tricks and shortcuts that users will come up with.

All of this is a long way to say: I am bummed because Google Reader has disabled its "Sharing" features. I'm still going to continue to use Reader, I still like its interface and I love its "tagging" feature and its keyboard shortcuts.  I'll miss the "Sharing" aspects because I'd built up a group of like-minded contacts (most of whom, I'd never met or talked to, my Reader buddies, so to speak) whose links I often found most interesting and most relevant to my own interests.  But even more than that, the "Sharing" feature of Google Reader allowed me to do what I'd always wanted to do: have one receptacle for everything on the Web that I found interesting, which I could then search through as and when I wanted to.  I don't think the Reader designers imagined that their "Sharing" feature could have other uses but that's what hurts most right now.  I had a workflow which I thought I had perfected and it was all through Reader - and now I have to start all over again and come up with another workflow.  In a way, this goes to show the danger of overly relying on one particular technology. 

Let me start from the beginning.  The discovery of RSS feeds was the best thing that happened to my web reading habits.  I read randomly back then, and one had to actually go to a website to read - and this was difficult to do, I used bookmarks to remember all these sites I found interesting - yet it never quite worked out.  Then came RSS feeds and suddenly, I didn't have to go to a website anymore, instead the website came to me.  I experimented with a number of desktop RSS readers, then shifted to Bloglines (remember Bloglines??!!!) for a long time, before finally taking the plunge into Google Reader. It was great - I loved it.  I could tag items that I thought were interesting and worth storing and it had an excellent "Search" feature which meant that I could look through all my feeds using keywords.  This is most useful when you are blogging or writing: you can look through all your read-and-tagged articles and find the ones that most relate to the argument you are making.

There was one problem.  But to explain that, I have to talk about my long-standing obsession with information.  One of the things about reading on the Web is that you get access to lots of material that you wouldn't have before.  And you don't actually get to read most of these pieces; some of them you skim, some of them you skim, find interesting and then read deeply; some of them you don't even skim but you think these could be potentially useful at a later time.  Which means that you want to store everything that you think is useful - even remotely.  And the great thing about the internet is that storing things is inexpensive and easy, although there are infinite ways to do it.  I tried a variety of options: Google Bookmarks, Evernote, Delicious - but it never worked.  There were just too many options and working across applications was tiring and inefficient and frankly, not very useful.  Storing links and text is only useful if you re-read them and use them; and I found that I was rarely going back to what I had stored.

This started to change as I started reading more and more through Google Reader, I would tag anything I thought remotely useful (the story of what tags I came to use is something I'll tell another time). Thus I had a nice folder of items that I thought might come in handy for me later.
 
But there were other articles that I would read outside Reader (usually links that came from the blogs that I read in Reader).  And I wanted to store these too - but there was never a way to put them into Reader.  So for the longest time, I had two places where I stored interesting things I'd read: in Google Reader and in Evernote - and needless to say, it got pretty unwieldy.

And then I discovered sharing on Reader which was pretty cool.  And somewhere in the midst of this, I discovered Google's "Note in Reader" bookmarklet, which was designed so that you could share interesting things with you Reader friends even if what you were reading was not actually through Reader. 

That was the breakthrough.  But not in the way the Google designers imagined.  True, I used the bookmarklet to share more links.  But once I clicked on "Note in Reader," I had the option of not just sharing the article, but also tagging it so that it would be accessible from within Google Reader.  So there were a lot of pieces that I used the bookmarklet to just save and tag, and not necessarily to share with others.  I had my one application to store everything on the Web that I found interesting: it was Google Reader.  At some point, I stopped using Evernote.

Which is why the loss of the Sharing functions in Google Reader is depressing.  Now when I click on "Note in Reader" here is what I get:

So forget sharing, I can't even tag and web-page and move it into my Google Reader folders.  Which means I have to start my search for one storage application all over again - or content myself with two.  I'm hoping that even as Google has disabled Sharing through Reader, they will at least allow us to import web-content into Reader folders.  But we'll see.

As we learn all the time as designers, when  you take away a feature, you take away practices that users have been relying on, practices that may not have been what you intended.  It's a good lesson to learn as a user - hopefully it'll be something I'll remember when I do any kind of design work.

Algorithmic culture and the bias of algorithms

Via Alan Jacobs, I came across a thought-provoking blog-post by Ted Striphas on "algorithmic culture."  The issue is the algorithm behind Amazon's "Popular Highlights" feature.  (In short, Amazon collects all the passages in its Kindle books that have been marked, collates this information and displays it on its website and/or on the Kindle.  So you can now see what other people have found interesting in a book and compare it with what you found interesting.)

Striphas brings up two problems, one minor, one major.  The minor one:

When Amazon uploads your passages and begins aggregating them with those of other readers, this sense of context is lost. What this means is that algorithmic culture, in its obsession with metrics and quantification, exists at least one level of abstraction beyond the acts of reading that first produced the data.

This is true but it could easily be remedied.  Kindle readers can also annotate passages in the text and if they feel like it, they could upload their annotations along with the passages they have marked.  That should supply the context of why the passages were highlighted. (Of course, this would bring up another thorny question: what algorithm to use to aggregate these annotations.) 

But he brings up another far more important point:

What I do fear, though, is the black box of algorithmic culture. We have virtually no idea of how Amazon’s Popular Highlights algorithm works, let alone who made it. All that information is proprietary, and given Amazon’s penchant for secrecy, the company is unlikely to open up about it anytime soon.

This is a very good point and it brings up what I often call the "bias" of algorithms.  Algorithms, after all, are made by people and they show all the biases that their designers put into them.  In fact, it's wrong to call them "biases" since these actually make the algorithm work!  Consider Google's search engine.  You type in a query and Google claims to return the links that you will find most "relevant."  But "relevant" here means something different from the way you use it in  your day-to-day life.  "Relevant" here means "relevant in the context of Google's algorithm" (a.k.a. PageRank). 

The problem is that this distinction is lost on people who just don't use Google all that much.  I spend a lot of time programming and Google is indispensable to me when I run into bugs.  So it is fair to say that I am something of an "expert" when it comes to using Google.  I understand that to use Google optimally, I need to use the right keywords, often the right combination of keywords along with the various operators that Google provides.  I am able to do this because:

1. I am in the computer science business, and I have some idea of how the PageRank algorithm works (although I suspect not all that much) and 
2. because I use Google a lot in my day-to-day life.  

I suspect that (1) isn't at all important but (2) is.  

But (2) also has a silver lining.  In his post, Striphas comments:

In the old paradigm of culture — you might call it “elite culture,” although I find the term “elite” to be so overused these days as to be almost meaningless — a small group of well-trained, trusted authorities determined not only what was worth reading, but also what within a given reading selection were the most important aspects to focus on. The basic principle is similar with algorithmic culture, which is also concerned with sorting, classifying, and hierarchizing cultural artifacts. [...] 

In the old cultural paradigm, you could question authorities about their reasons for selecting particular cultural artifacts as worthy, while dismissing or neglecting others. Not so with algorithmic culture, which wraps abstraction inside of secrecy and sells it back to you as, “the people have spoken.”

Well, yes and no.  There's a big difference between the black box of algorithms and the black box of elite preferences. Algorithms may be opaque but they are still rule-based.  You can still figure out how to use Google to your own advantage by playing with it.  For any query you give to it, Google will give the exact same response (well, for a certain period of time at least).    So you can play with it and find out what works for you and what doesn't.  The longer you play with it, the longer you use it, the more you become familiar with its features, the less opaque it seems.

Not so with what Striphas calls "elite culture," which, if anything, is far more opaque and far less amenable to this kind of trial-and-error practice.  (That's because the actions of experts aren't really rule-based.)

I am not sure where I am going with this and I am certainly not sure whether Amazon's Kindle aggregation mechanism will become as transparent as Google's search algorithm by trial-and-error but my point is that it's too soon to give up on algorithmic culture.


Postscript: My deeper worry is that when we actually reach the point when algorithms are used far more than they are now, the world will be divided into two types of people.  Those who can exploit the biases of the algorithm to make it work well for them (like I do PageRank).  And those who can't.  It's a scary thought although since I have no clue about how such a world will look like, this is still an empty worry.