Digifesto

things I’ve been doing while not looking at twitter

Twitter was getting me down so I went on a hiatus. I’m still on that hiatus. Instead of reading Twitter, I’ve been:

  • Reading Fred Turner’s The Democratic Surround. This is a great book about the relationship between media and democracy. Since a lot of my interest in Twitter has been because of my interest in the media and democracy, this gives me those kinds of jollies without the soap opera trainwreck of actually participating in social media.
  • Going to arts events. There was a staging of Rhinoceros at Berkeley. It’s an absurdist play in which a small French village is suddenly stricken by an epidemic wherein everybody is transformed into a rhinoceros. It’s probably an allegory for the rise of Communism or Fascism but the play is written so that it’s completely ambiguous. Mainly it’s about conformity in general, perhaps ideological conformity but just as easily about conformity to non-ideology, to a state of nature (hence, the animal form, rhinoceros.) It’s a good play.
  • I’ve been playing Transistor. What an incredible game! The gameplay is appealingly designed and original, but beyond that it is powerfully written an atmospheric. In many ways it can be read as a commentary on the virtual realities of the Internet and the problems with them. Somehow there was more media attention to GamerGate than to this one actually great game. Too bad.
  • I’ve been working on papers, software, and research in anticipation of the next semester. Lots of work to do!

Above all, what’s great about unplugging from social media is that it isn’t actually unplugging at all. Instead, you can plug into a smarter, better, deeper world of content where people are more complex and reasonable. It’s elevating!

I’m writing this because some time ago it was a matter of debate whether or not you can ‘just quit Facebook’ etc. It turns out you definitely can and it’s great. Go for it!

(Happy to respond to comments but won’t respond to tweets until back from the hiatus)

prediction and computational complexity

To the extent that an agent is predictable, it must be:

  • observable, and
  • have a knowable internal structure

The first implies that the predictor has collected data emitted by the agent.

The second implies that the agent has internal structure and that the predictor has the capacity to represent the internal structure of the other agent.

In general, we can say that people do not have the capacity to explicitly represent other people very well. People are unpredictable to each other. This is what makes us free. When somebody is utterly predictable to us, their rigidity is a sign of weakness or stupidity. They are following a simple algorithm.

We are able to model the internal structure of worms with available computing power.

As we build more and more powerful predictive systems, we can ask: is our internal structure in principle knowable by this powerful machine?

This is different from the question of whether or not the predictive machine has data from which to draw inferences. Though of course the questions are related in their implications.

I’ve tried to make progress on modeling this with limited success. Spiros has just told me about binary decision diagrams which are a promising lead.

objective properties of text and robot scientists

One problem with having objectivity as a scientific goal is that it may be humanly impossible.

One area where this comes up is in the reading of a text. To read is to interpret, and it is impossible to interpret without bringing ones own concepts and experience to bear on the interpretation. This introduces partiality.

This is one reason why Digital Humanities are interesting. In Digital Humanities, one is using only the objective properties of the text–its data as a string of characters and its metadata. Semantic analysis is reduced to a study of a statistical distribution over words.

An odd conclusion: the objective scientific subject won’t be a human intelligence at all. It will need to be a robot. Its concepts may never be interpretable by humans because any individual human is too small-minded or restricted in their point of view to understand the whole.

Looking at the history of cybernetics, artificial intelligence, and machine learning, we can see the progression of a science dedicated to understanding the abstract properties of an idealized, objective learner. That systems such as these underly the infrastructure we depend on for the organization of society is a testament to their success.

A troubling dilemma

I’m troubling over the following dilemma:

On the one hand, serendipitous exposure to views unlike your own is good, because that increases the breadth of perspective that’s available to you. You become more cosmopolitan and tolerant.

On the other hand, exposure to views that are hateful, stupid, or evil can be bad, because this can be hurtful, misinforming, or disturbing. Broadly, content can harm.

So, suppose you are deciding what to expose yourself to, or others to, either directly or through the design of some information system.

This requires making a judgment about whether exposure to that perspective will be good or bad.

How is it possible to make that judgment without already having been exposed to it?

Put another way, filter bubbles are sometimes good and sometimes bad. How can you tell the difference, from within a bubble, about whether bridging to another bubble is worthwhile? How could you tell from outside of a bubble? Is there a way to derive this from the nature of bubbles in the abstract?

developing a nuanced view on transparency

I’m a little late to the party, but I think I may at last be developing a nuanced view on transparency. This is a personal breakthrough about the importance of privacy that I owe largely to the education I’m getting at Berkeley’s School of Information.

When I was an undergrad, I also was a student activist around campaign finance reform. Money in politics was the root of all evil. We were told by our older, wiser activist mentors that we were supposed to lay the groundwork for our policy recommendation and then wait for journalists to expose a scandal. That way we could move in to reform.

Then I worked on projects involving open source, open government, open data, open science, etc. The goal of those activities is to make things more open/transparent.

My ideas about transparency as a political, organizational, and personal issue originated in those experiences with those movements and tactics.

There is a “radically open” wing of these movements which thinks that everything should be open. This has been debunked. The primary way to debunk this is to point out that less privileged groups often need privacy for reasons that more privileged advocates of openness have trouble understanding. Classic cases of this include women who are trying to evade stalkers.

This has been expanded to a general critique of “big data” practices. Data is collected from people who are less powerful than people that process that data and act on it. There has been a call to make the data processing practices more transparent to prevent discrimination.

A conclusion I have found it easy to draw until relatively recently is: ok, this is not so hard. What’s important is that we guarantee privacy for those with less power, and enforce transparency on those with more power so that they can be held accountable. Let’s call this “openness for accountability.” Proponents of this view are in my opinion very well-intended, motivated by values like justice, democracy, and equity. This tends to be the perspective of many journalists and open government types especially.

Openness for accountability is not a nuanced view on transparency.

Here are some examples of cases where an openness for accountability view can go wrong:

  • Arguably, the “Gawker Stalker” platform for reporting the location of celebrities was justified by an ‘opennes for accountability’ logic. Jimmy Kimmel’s browbeating of Emily Gould indicates how this can be a problem. Celebrity status is a form of power but also raises ones level of risk because there is a small percentage of the population that for unfathomable reasons goes crazy and threatens and even attacks people. There is a vicious cycle here. If one is perceived to be powerful, then people will feel more comfortable exposing and attacking that person, which increases their celebrity, increasing their perceived power.
  • There are good reasons to be concerned about stereotypes and representation of underprivileged groups. There are also cases where members of those groups do things that conform to those stereotypes. When these are behaviors that are ethically questionable or manipulative, it’s often important organizationally for somebody to know about them and act on them. But transparency about that information would feed the stereotypes that are being socially combated on a larger scale for equity reasons.
  • Members of powerful groups can have aesthetic taste and senses of humor that are offensive or even triggering to less powerful groups. More generally, different social groups will have different and sometimes mutually offensive senses of humor. A certain amount of public effort goes into regulating “good taste” and that is fine. But also, as is well known, art that is in good taste is often bland and fails to probe the depths of the human condition. Understanding the depths of the human condition is important for everybody but especially for powerful people who have to take more responsibility for other humans.
  • This one is based on anecdotal information from a close friend: one reason why Congress is so dysfunctional now is that it is so much more transparent. That transparency means that politicians have to be more wary of how they act so that they don’t alienate their constituencies. But bipartisan negotiation is exactly the sort of thing that alienates partisan constituencies.

If you asked me maybe two years ago, I wouldn’t have been able to come up with these cases. That was partly because of my positionality in society. Though I am a very privileged man, I still perceived myself as an outsider to important systems of power. I wanted to know more about what was going on inside important organizations and was frustrated by my lack of access to it. I was very idealistic about wanting a more fair society.

Now I am getting older, reading more, experiencing more. As I mature, people are trusting me with more sensitive information, and I am beginning to anticipate the kinds of positions I may have later in my career. I have begun to see how my best intentions for making the world a better place are at odds with my earlier belief in openness for accountability.

I’m not sure what to do with this realization. I put a lot of thought into my political beliefs and for a long time they have been oriented around ideas of transparency, openness, and equity. Now I’m starting to see the social necessity of power that maintains its privacy, unaccountable to the public. I’m starting to see how “Public Relations” is important work. A lot of what I had a kneejerk reaction against now makes more sense.

I am in many ways a slow learner. These ideas are not meant to impress anybody. I’m not a privacy scholar or expert. I expect these thoughts are obvious to those with less of an ideological background in this sort of thing. I’m writing this here because I see my current role as a graduate student as participating in the education system. Education requires a certain amount of openness because you can’t learn unless you have access to information and people who are willing to teach you from their experience, especially their mistakes and revisions.

I am also perhaps writing this now because, who knows, maybe one day I will be an unaccountable, secretive, powerful old man. Nobody would believe me if I said all this then.

writing about writing

Years ago on a now defunct Internet forum, somebody recommended that I read a book about the history of writing and its influence on culture.

I just spent ten minutes searching through my email archives trying to find the reference. I didn’t find it.

I’ve been thinking about writing a lot lately. And I’ve been thinking about writing especially tonight, because I was reading this essay that is in a narrow sense about Emily Gould but in a broad sense is about writing.*

I used to find writing about writing insufferable because I thought it was lazy. Only writers with nothing to say about anything else write about writing.

I don’t disagree with that sentiment tonight. Instead I’ve succumbed to the idea that actually writing is a rather specialized activity that is perhaps special because it affords so much of an opportunity to scrutinize and rescrutinize in ways that everyday social interaction does not. By everyday social interaction, I mean specifically the conversations I have with people that are physically present. I am not referring to the social interactions that I conduct through writing with sometimes literally hundreds of people at a time, theoretically, but actually more on the order of I don’t know twenty, every day.

The whole idea that you are supposed to edit what you write before you send it presupposes a reflective editorial process where text, as a condensed signal, is the result of an optimization process over possible interpretations that happens before it is ever emitted. The conscious decision to not edit text as one writes it is difficult if not impossible for some people but for others more…natural. Why?

The fluidity with which writing can morph genres today–it’s gossip, it’s journalism, it’s literature, it’s self expression reflective of genuine character, it’s performance of an assumed character, it’s…–is I think something new.


* Since writing this blog post, I have concluded that this article is quite evil.

It all comes back to Artificial Intelligence

I am blessed with many fascinating conversations every week. Because of the field I am in, these conversations are mainly about technology and people and where they intersect.

Sometimes they are about philosophical themes like how we know anything, or what is ethical. These topics are obviously relevant to an academic researcher, especially when one is interested in computational social science, a kind of science whose ethics have lately been called into question. Other times they are about the theoretical questions that such a science should or could address, like: how do we identify leaders? Or determine what are the ingredients for a thriving community? What is creativity, and how can we mathematically model how it arises from social interaction?

Sometimes the conversations are political. Is it a problem that algorithms are governing more of our political lives and culture? If so, what should we do about it?

The richest and most involved conversations, though, are about artificial intelligence (AI). As a term, it has fallen out of fashion. I was very surprised to see it as a central concept in Bengio et al.’s “Representation Learning: A Review and New Perspectives” [arXiv]. In most discussion scientific computing or ‘data science’ for the most part people have abandoned the idea of intelligent machines. Perhaps this is because so many of the applications of this technology seem so prosaic now. Curating newsfeeds, for example. That can’t be done intelligently. That’s just an algorithm.

Never mind that the origins of all of what we now call machine learning was in the AI research program, which is as old as computer science itself and really has grown up with it. Marvin Minsky famously once defined artificial intelligence as ‘whatever humans still do better than computers.’ And this is the curse of the field. With every technological advance that is at the time mind-blowingly powerful, performing a task that it used to require hundreds of people to perform, it very shortly becomes mere technology.

It’s appropriate then that representation learning, the problem of deriving and selecting features from a complex data set that are valuable for other kinds of statistical analysis in other tasks, is brought up in the context of AI. Because this is precisely the sort of thing that people still think they are comparatively good at. A couple years ago, everyone was talking about the phenomenon of crowdsourced image tagging. People are better at seeing and recognizing objects in images than computers, so in order to, say, provide the data for Google’s Image search, you still need to mobilize lots of people. You just have to organize them as if they were computer functions so that you can properly aggregate their results.

On of the earliest tasks posed to AI, the Turing Test, proposed and named after Alan Turing, the inventor of the fricking computer, is the task of engaging in conversation as if one is a human. This is harder than chess. It is harder than reading handwriting. Something about human communication is so subtle that it has withstood the test of time as an unsolved problem.

Until June of this year, when a program passed the Turing Test in the annual competition. Conversation is no longer something intelligent. It can be performed by a mere algorithm. Indeed, I have heard that a lot of call centers now use scripted dialog. An operator pushes buttons guiding the caller through a conversation that has already been written for them.

So what’s next?

I have a proposal: software engineering. We still don’t have an AI that can write its own source code.

How could we create such an AI? We could use machine learning, training it on data. What’s amazing is that we have vast amounts of data available on what it is like to be a functioning member of a software development team. Open source software communities have provided an enormous corpus of what we can guess is some of the most complex and interesting data ever created. Among other things, this software includes source code for all kinds of other algorithms that were once considered AI.

One reason why I am building BigBang, a toolkit for the scientific analysis of software communities, is because I believe it’s the first step to a better understanding of this very complex and still intelligent process.

While above I have framed AI pessimistically–as what we delegate away from people to machines, that is unnecessarily grim. In fact, with every advance in AI we have come to a better understanding of our world and how we see, hear, think, and do things. The task of trying to scientifically understand how we create together and the task of developing an AI to create with us is in many ways the same task. It’s just a matter of how you look at it.

objectivity is powerful

Like “neoliberal”, “objectivity” in contemporary academic discourse is only used as a term of disparagement. It has fallen out of fashion to speak about “objectivity” in scientific language. It remains in fashion to be critical of objectivity in those disciplines that have been criticizing objectivity since at least the 70’s.

This is too bad because objectivity is great.

The criticism goes like this: scientists and journalists both used to say that they were being objective. There was a lot to this term. It could mean ‘disinterested’ or it could mean so rigorous as to be perfectly inter-subjective. It sounded good. But actually, all the scientists and journalists who claimed to be objective were sexist, racist, and lapdogs of the bourgeoisie. They used ‘objectivity’ as a way to exclude those who were interested in promoting social justice. Hence, anyone who claims to be objective is suspicious.

There are some more sophisticated arguments than this but their sophistication only weakens the main emotional thrust of the original criticism. The only reason for this sophistication is to be academically impressive, which is fundamentally useless, or to respond in good faith to criticisms, which is politically unnecessary and probably unwise.

Why is it unwise to respond in good faith to criticisms of a critique of objectivity? Because to concede that good faith response to criticism is epistemically virtuous would be to concede something to the defender of objectivity. Once you start negotiating with the enemy in terms of reasons, you become accountable to some kind of shared logic which transcends your personal subjectivity, or the collective subjectivity of those whose perspectives are channeled in your discourse.

In a world in which power is enacted and exerted through discourse, and in which cultural logics are just rules in a language game provisionally accepted by players, this rejection of objectivity is true resistance. The act of will that resists logical engagement with those in power will stymie that power. It’s what sticks it to the Man.

The problem is that however well-intentioned this strategy may be, it is dumb.

It is dumb because as everybody knows, power isn’t exerted mainly through discourse. Power is exerted through violence. And while it may be fun to talk about “cultural logics” if you are a particular kind of academic, and even fun to talk about how cultural logics can be violent, that is vague metaphorical poetry compared to something else that they could be talking about. Words don’t kill people. Guns kill people.

Put yourself in the position of somebody designing and manufacturing guns. What do you talk about with your friends and collaborators? If you think that power is about discourse, then you might think that these people talk about their racist political agenda, wherein they reproduce the power dynamics that they will wield to continue their military dominance.

They don’t though.

Instead what they talk about is the mechanics of how guns work and the technicalities of supply chain management. Where are they importing their gunpowder from and how much does it cost? How much will it go boom?

These conversations aren’t governed by “cultural logics.” They are governed by logic. Because logic is what preserves the intersubjective validity of their claims. That’s important because to successful build and market guns, the gun has to go boom the same amount whether or not the person being aimed at shares your cultural logic.

This is all quite grim. “Of course, that’s the point: objectivity is the language of violence and power! Boo objectivity!”

But that misses the point. The point is that it’s not that objectivity is what powerful people dupe people into believing in order to stay powerful. The point is that objectivity is what powerful people strive for in order to stay powerful. Objectivity is powerful in ways that more subjectively justified forms of knowledge are not.

This is not a popular perspective. There a number of reasons for this. One is that attain objective understanding is a lot of hard work and most people are just not up for it. Another is that there are a lot of people who have made their careers arguing for a much more popular perspective, which is that “objectivity” is associated with evil people and therefor we should reject it as an epistemic principal. There will always be an audience for this view, who will be rendered powerless by it and become the self-fulfilling prophecy of the demagogues who encourage their ignorance.

technical work

Dipping into Julian Orr’s Talking about Machines, an ethnography of Xerox photocopier technicians, has set off some light bulbs for me.

First, there’s Orr’s story: Orr dropped out of college and got drafted, then worked as a technician in the military before returning to school. He paid the bills doing technical repair work, and found it convenient to do his dissertation on those doing photocopy repair.

Orr’s story reminds me of my grandfather and great-uncle, both of whom were technicians–radio operators–during WWII. Their civilian careers were as carpenters, building houses.

My own dissertation research is motivated by my work background as an open source engineer, and my own desire to maintain and improve my technical chops. I’d like to learn to be a data scientist; I’m also studying data scientists at work.

Further fascinating was Orr’s discussion of the Xerox technician’s identity as technicians as opposed to customers:

The distinction between technician and customer is a critical division of this population, but for technicians at work, all nontechnicians are in some category of other, including the corporation that employs the technicians, which is seen as alien, distant, and only sometimes an ally.

It’s interesting to read about this distinction between technicians and others in the context of Xerox photocopiers when I’ve been so affected lately by the distinction between tech folk and others and data scientists and others. This distinction between those who do technical work and those who they serve is a deep historical one that transcends the contemporary and over-computed world.

I recall my earlier work experience. I was a decent engineer and engineering project manager. I was a horrible account manager. My customer service skills were abysmal, because I did not empathize with the client. The open source context contributes to this attitude, because it makes a different set of demands on its users than consumer technology does. One gets assistance with consumer grade technology by hiring a technician who treats you as a customer. You get assistance with open source technology by joining the community of practice as a technician. Commercial open source software, according to the Pentaho beekeeper model, is about providing, at cost, that customer support.

I’ve been thinking about customer service and reflecting on my failures at it a lot lately. It keeps coming up. Mary Gray’s piece, When Science, Customer Service, and Human Subjects Research Collide explicitly makes the connection between commercial data science at Facebook and customer service. The ugly dispute between Gratipay (formerly Gittip) and Shanley Kane was, I realized after the fact, a similar crisis between the expectations of customers/customer service people and the expectations of open source communities. When “free” (gratis) web services display a similar disregard for their users as open source communities do, it’s harder to justify in the same way that FOSS does. But there are similar tensions, perhaps. It’s hard for technicians to empathize with non-technicians about their technical problems, because their lived experience is so different.

It’s alarming how much is being hinged on the professional distinction between technical worker and non-technical worker. The intra-technology industry debates are thick with confusions along these lines. What about marketing people in the tech context? Sales? Are the “tech folks” responsible for distributional justice today? Are they in the throws of an ideology? I was reading a paper the other day suggesting that software engineers should be held ethically accountable for the implicit moral implications of their algorithms. Specifically the engineers; for some reason not the designers or product managers or corporate shareholders, who were not mentioned. An interesting proposal.

Meanwhile, at the D-Lab, where I work, I’m in the process of navigating my relationship between two teams, the Technical Team, and the Services Team. I have been on the Technical team in the past. Our work has been to stay on top of and assist people with data science software and infrastructure. Early on, we abolished regular meetings as a waste of time. Naturally, there was a suspicion expressed to me at one point that we were unaccountable and didn’t do as much work as others on the Services team, which dealt directly with the people-facing component of the lab–scheduling workshops, managing the undergraduate work-study staff. Sitting in on Services meetings for the first time this semester, I’ve been struck by how much work the other team does. By and large, it’s information work: calendering, scheduling, entering into spreadsheets, documenting processes in case of turnover, sending emails out, responding to emails. All important work.

This is exactly the work that information technicians want to automate away. If there is a way to reduce the amount of calendering and entering into spreadsheets, programmers will find a way. The whole purpose of computer science is to automate tasks that would otherwise be tedious.

Eric S. Raymond’s classic (2001) essay How to Become a Hacker characterizes the Hacker Attitude, in five points:

  1. The world is full of fascinating problems waiting to be solved.
  2. No problem should ever have to be solved twice.
  3. Boredom and drudgery are evil.
  4. Freedom is good.
  5. Attitude is no substitute for competence.

There is no better articulation of the “ideology” of “tech folks” than this, in my opinion, yet Raymond is not used much as a source for understanding the idiosyncracies of the technical industry today. Of course, not all “hackers” are well characterized by Raymond (I’m reminded of Coleman’s injunction to speak of “cultures of hacking”) and not all software engineers are hackers (I’m sure my sister, a software engineer, is not a hacker. For example, based on my conversations with her, it’s clear that she does not see all the unsolved problems with the world to be intrinsically fascinating. Rather, she finds problems that pertain to some human interest, like children’s education, to be most motivating. I have no doubt that she is a much better software engineer than I am–she has worked full time at it for many years and now works for a top tech company. As somebody closer to the Raymond Hacker ethic, I recognize that my own attitude is no substitute for that competence, and hold my sister’s abilities in very high esteem.)

As usual, I appear to have forgotten where I was going with this.

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