Digifesto

responding to @npdoty on ethics in engineering

Nick Doty wrote a thorough and thoughtful response to my earlier post about the Facebook research ethics problem, correcting me on a number of points.

In particular, he highlights how academic ethicists like Floridi and Nissenbaum have an impact on industry regulation. It’s worth reading for sure.

Nick writes from an interesting position. Since he works for the W3C himself, he is closer to the policy decision makers on these issues. I think this, as well as his general erudition, give him a richer view of how these debates play out. Contrast that with the debate that happens for public consumption, which is naturally less focused.

In trying to understand scholarly work on these ethical and political issues of technology, I’m struck by how differences in where writers and audiences are coming from lead to communication breakdown. The recent blast of popular scholarship about ‘algorithms’, for example, is bewildering to me. I had the privilege of learning what an algorithm was fairly early. I learned about quicksort in an introductory computing class in college. While certainly an intellectual accomplishment, quicksort is politically quite neutral.

What’s odd is how certain contemporary popular scholarship seeks to introduce an unknowing audience to algorithms not via their basic properties–their pseudocode form, their construction from more fundamental computing components, their running time–but for their application in select and controversial contexts. Is this good for the public education? Or is this capitalizing on the vagaries of public attention?

My democratic values are being sorely tested by the quality of public discussion on matters like these. I’m becoming more content with the fact that in reality, these decisions are made by self-selecting experts in inaccessible conversations. To hope otherwise is to downplay the genuine complexity of technical problems and the amount of effort it takes to truly understand them.

But if I can sit complacently with my own expertise, this does not seem like a political solution. The FCC’s willingness to accept public comment, which normally does not elicit the response of a mass action, was just tested by Net Neutrality activists. I see from the linked article that other media-related requests for comments were similarly swamped.

The crux, I believe, is the self-referential nature of the problem–that the mechanics of information flow among the public are both what’s at stake (in terms of technical outcomes) and what drives the process to begin with, when it’s democratic. This is a recipe for a chaotic process. Perhaps there are no attractor or steady states.

Following Rash’s analysis of Habermas and Luhmann’s disagreement as to the fate of complex social systems, we’ve got at least two possible outcomes for how these debates play out. On the one hand, rationality may prevail. Genuine interlocutors, given enough time and with shared standards of discourse, can arrive at consensus about how to act–or, what technical standards to adopt, or what patches to accept into foundational software. On the other hand, the layering of those standards on top of each other, and the reaction of users to them as they build layers of communication on top of the technical edifice, can create further irreducible complexity. With that complexity comes further ethical dilemmas and political tensions.

A good desideratum for a communications system that is used to determine the technicalities of its own design is that its algorithms should intelligently manage the complexity of arriving at normative consensus.

This is truly unfortunate

This is truly unfortunate.

In one sense, this indicates that the majority of Facebook users have no idea how computers work. Do these Facebook users also know that their use of a word processor, or their web browser, or their Amazon purchases, are all mediated by algorithms? Do they understand that what computers do–more or less all they ever do–is mechanically execute algorithms?

I guess not. This is a massive failure of the education system. Perhaps we should start mandating that students read this well-written HowStuffWorks article, “What is a computer algorithm?” That would clear up a lot of confusion, I think.

The Facebook ethics problem is a political problem

So much has been said about the Facebook emotion contagion experiment. Perhaps everything has been said.

The problem with everything having been said is that by an large people’s ethical stances seem predetermined by their habitus.

By which I mean: most people don’t really care. People who care about what happens on the Internet care about it in whatever way is determined by their professional orientation on that matter. Obviously, some groups of people benefit from there being fewer socially imposed ethical restrictions on data scientific practice, either in an industrial or academic context. Others benefit from imposing those ethical restrictions, or cultivating public outrage on the matter.

If this is an ethical issue, what system of ethics are we prepared to use to evaluate it?

You could make an argument from, say, a utilitarian perspective, or a deontological perspective, or even a virtue ethics standpoint. Those are classic moves.

But nobody will listen to what a professionalized academic ethicist will say on the matter. If there’s anybody who does rigorous work on this, it’s probably somebody like Luciano Floridi. His work is great, in my opinion. But I haven’t found any other academics who work in, say, policy that embrace his thinking. I’d love to be proven wrong.

But since Floridi does serious work on information ethics, that’s mainly an inconvenience to pundits. Instead we get heat, not light.

If this process resolves into anything like policy change–either governmental or internally at Facebook–it will because of a process of agonistic politics. “Agonistic” here means fraught with conflicted interests. It may be redundant to modify ‘politics’ with ‘agonistic’ but it makes the point that the moves being made are strategic actions, aimed at gain for ones person or group, more than they are communicative ones, aimed at consensus.

Because e.g. Facebook keeps public discussion fragmented through its EdgeRank algorithm, which even in its well-documented public version is full of apparent political consequences and flaws, there is no way for conversation within the Facebook platform to result in consensus. It is not, as has been observed by others, a public. In a trivial sense, it’s not a public because the data isn’t public. The data is (sort of) private. That’s not a bad thing. It just means that Facebook shouldn’t be where you go to develop a political consensus that could legitimize power.

Twitter is a little better for this, because it’s actually public. Facebook has zero reason to care about the public consensus of people on Twitter though, because those people won’t organize a consumer boycott of Facebook, because they can only reach people that use Twitter.

Facebook is a great–perhaps the greatest–example of what Habermas calls the steering media. “Steering,” because it’s how powerful entities steer public opinion. For Habermas, the steering media control language and therefore culture. When ‘mass’ media control language, citizens no longer use language to form collective will.

For individualized ‘social’ media that is arranged into filter bubbles through relevance algorithms, language is similarly controlled. But rather than having just a single commanding voice, you have the opportunity for every voice to be expressed at once. Through homophily effects in network formation, what you’d expect to see are very intense clusters of extreme cultures that see themselves as ‘normal’ and don’t interact outside of their bubble.

The irony is that the critical left, who should be making these sorts of observations, is itself a bubble within this system of bubbles. Since critical leftism is enacted in commercialized social media which evolves around it, it becomes recuperated in the Situationist sense. Critical outrage is tapped for advertising revenue, which spurs more critical outrage.

The dependence of contemporary criticality on commercial social media for its own diffusion means that, ironically, none of them are able to just quit Facebook like everyone else who has figured out how much Facebook sucks.

It’s not a secret that decentralized communication systems are the solution to this sort of thing. Stanford’s Liberation Tech group captures this ideology rather well. There’s a lot of good work on censorship-resistant systems, distributed messaging systems, etc. For people who are citizens in the free world, many of these alternative communication platforms where we are spared from algorithmic control are very old. Some people still use IRC for chat. I’m a huge fan of mailing lists, myself. Email is the original on-line social media, and ones inbox is ones domain. Everyone who is posting their stuff to Facebook could be posting to a WordPress blog. WordPress, by the way, has a lovely user interface these days and keeps adding “social” features like “liking” and “following”. This goes largely unnoticed, which is too bad, because Automattic, the company the runs WordPress, is really not evil at all.

So there are plenty of solutions to Facebook being bad for manipulative and bad for democracy. Those solutions involve getting people off of Facebook and onto alternative platforms. That’s what a consumer boycott is. That’s how you get companies to stop doing bad stuff, if you don’t have regulatory power.

Obviously the real problem is that we don’t have a less politically problematic technology that does everything we want Facebook to do only not the bad stuff. There are a lot of unsolved technical accomplishments to getting that to work. I think I wrote a social media think piece about this once.

I think a really cool project that everybody who cares about this should be working on is designing and executing on building that alternative to Facebook. That’s a huge project. But just think about how great it would be if we could figure out how to fund, design, build, and market that. These are the big questions for political praxis in the 21st century.

Theorizing the Web and SciPy conferences compared

I’ve just been through two days of tutorials at SciPy 2014–that stands for Scientific Python (the programming language). The last conference I went to was Theorizing the Web 2014. I wonder if I’m the first person to ever go to both conferences. Since I see my purpose in grad school as being a bridge node, I think it’s worthwhile to write something comparing the two.

Theorizing the Web was held in a “gorgeous warehouse space” in Williamsburg, the neighborhood of Brooklyn, New York that was full of hipsters ten years ago and now is full of baby carriages but still has gorgeous warehouse spaces and loft apartments. The warehouse spaces are actually gallery spaces that only look like warehouses from the outside. On the inside of the one where TtW was held, whole rooms with rounded interior corners were painted white, perhaps for a photo shoot. To call it a “warehouse” is to appeal to the blue color and industrial origins that Brooklyn gentrifiers appeal to in order to distinguish themselves from the elites in Manhattan. During my visit to New York for the conference, I crashed on a friend’s air mattress in the Brooklyn neighborhood I had been gentrifying just a few years earlier. The speakers included empirical scientific researchers, but these were not the focus of the event. Rather, the emphasis was on theorizing in a way that is accessible to the public. The most anticipated speaker was a porn actress. Others were artists or writers of one sort or another. One was a sex worker who then wrote a book. Others were professors of sociology and communications. Another was a Buzzfeed editor.

SciPy is taking place in the AT&T Education and Conference Center in Austin, Texas, near the UT Austin campus. I’m writing from the adjoining hotel. The conference rooms we are using are in the basement; they seat many in comfortable mesh rolling chairs on tiers so everybody can see the dual projector screens. The attendees are primarily scientists who do computationally intensive work. One is a former marine biologist who now does bioinformatics mainly. Another team does robotics. Another does image processing on electron microscope of chromosomes. They are not trying to be accessible to the public. What they are trying to teach is hard enough to get across to others with similar expertise. It is a small community trying to enlarge itself by teaching others its skills.

At Theorizing the Web, the rare technologist spoke up to talk about the dangers of drones. In the same panel, it was pointed out how the people designing medical supply drones for use in foreign conflict zones were considering coloring them white, not black, to make them less intimidating. The implication was that drone designers are racist.

It’s true that the vast majority of attendees of the conference are white and male. To some extent, this is generational. Both tutorials I attended today–including the one one on software for modeling multi-body dynamics, useful for designing things like walking robots–were interracial and taught by guys around my age. The audience has some older folks. These are not necessarily academics, but may be industry types or engineers whose firms are paying them to attend to train on cutting edge technology.

The afterparty first night of Theorizing the Web was in a dive bar in Williamsburg. Brooklyn’s Williamsburg has dive bars the same way Virginia’s Williamsburg has a colonial village–they are a cherished part of its cultural heritage. But the venue was alienating for some. One woman from abroad confided to me that they were intimidated by how cool the bar felt. It was my duty as an American and a former New Yorker to explain that Williamsburg stopped being cool a long time ago.

I’m an introvert and am initially uneasy in basically any social setting. Tonight’s SciPy afterparty was in the downtown office of Enthought, in the Bank of America building. Enthought’s digs are on the 21st floor, with spatious personal offices and lots of whiteboards which display serious use. As an open source product/consulting/training company, it appears to be doing quite well. I imagine really cool people would find it rather banal.

I don’t think it’s overstating things to say that Theorizing the Web serves mainly those skeptical of the scientific project. Knowledge is conceived of as a threat to the known. One panelist at TtW described the problem of “explainer” sites–web sites whose purpose is to explain things that are going on to people who don’t understand them–when they try to translate cultural phenomena that they don’t understand. It was argued that even in cases where these cultural events are public, to capture that content and provide a interpretation or narration around it can be exploitative. Later, Kate Crawford, a very distinguished scholar on civic media, spoke to a rapt audience about the “conjoint anxieties” of Big Data. The anxieties of the watched are matched by the anxieties of the watchmen–like the NSA and, more implicitly, Facebook–who must always seek out more data in order to know things. The implication is that their political or economic agenda is due to a psychological complex–damning if true. In a brilliant rhetorical move that I didn’t quite follow, she tied this in to normcore, which I’m pretty sure is an Internet meme about a fake “fashion” trend in New York. Young people in New York go gaga for irony like this. For some reason earlier this year hipsters ironically wearing unstylish clothing became notable again.

I once met somebody from L.A. who told me their opinion of Brooklyn was that all nerds gathered in one place and thought they could decide what cool was just by saying so. At the time I had only recently moved to Berkeley and was still adjusting. Now I realize how parochial that zeitgeist is, however much I may still identify with it some.

Back in Austin, I have interesting conversations with folks at the SciPy party. One conversation is with two social scientists (demographic observation: one man, one woman) from New York that work on statistical analysis of violent crime in service to the city. They talk about the difficulty of remaining detached from their research subjects, who are eager to assist with the research somehow, though this would violate the statistical rigor of their study. Since they are doing policy research, objectivity is important. They are painfully aware of the limitations of their methods and the implications this has on those their work serves.

Later, I’m sitting alone when I’m joined by an electrical engineer turned programmer. He’s from Tennessee. We talk shop for a bit but the conversation quickly turns philosophical–about the experience of doing certain kinds of science, the role of rationality in human ethics, whether religion is an evolved human impulse and whether that mattes. We are joined by a bioinformatics researcher from Paris. She tells us later that she has an applied math/machine learning background.

The problem in her field, she explains, is that for rare diseases it is very hard to find genetic causes because there isn’t enough data to do significant inference. Genomic data is very highly dimensional–thousands of genes–and for some diseases there may be less than fifty cases to study. Machine learning researchers are doing their best to figure out ways for researchers to incorporate “prior knowledge”–theoretical understanding from beyond the data available–to improve their conclusions.

Over meals the past couple days I’ve been checking Twitter, where a lot of the intellectuals who organize Theorizing the Web or are otherwise prominent in that community are active. One conversation extended conversation is about the relative failure of the open source movement to produce compelling consumer products. My theory is that this has to do with business models and the difficulty of coming up with upfront capital investment. But emotionally my response to that question is that it is misplaced: consumer products are trivial. Who cares?

Today, folks on Twitter are getting excited about using Adorno’s concept of the culture industry to critique Facebook’s emotional contagion experiment and other media manipulation. I find this both encouraging–it’s about time the Theorizing the Web community learned to embrace Frankfurt School thought–and baffling, because I believe they are misreading Adorno. The culture industry is that sector of the economy that produces cultural products, like Hollywood and television productions companies. On the Internet, the culture industry is Buzzfeed, the Atlantic, and to a lesser extent (though this is surely masked by it’s own ideology) The New Inquiry. My honest opinion for a long time has been that the brand of “anticapitalist” criticality indulged in on-line is a politically impotent form of entertainment equivalent to the soap opera. A concept more appropriate for understanding Facebook’s role in controlling access to news and the formation of culture is Habermas’ idea of steering media.

He gets into this in Theory of Communicative Action, vol. 2, which is underrated in America probably due to its heaviness.

economic theory and intellectual property

I’ve started reading Picketty’s Capital. His introduction begins with an overview of the history of economic theory, starting with Ricardo and Marx.

Both these early theorists predicted the concentration of wealth into the hands of the owners of factors of production that are not labor. For Ricardo, land owners extract rents and dominate the economy. For Marx, capitalists–owners of private capital–accumulate capital and dominate the economy.

Since those of us with an eye on the tech sector are aware of a concentration of wealth in the hands of the owners of intellectual property, it’s a good question what kind of economic theory ought to apply to those cases.

One one sense, intellectual property is a kind of capital. It is a factor of production that is made through human labor.

On the other hand, we talk about ideas being ‘discovered’ like land is discovered, and we imagine that intellectual property can in principle be ‘shared’ like a ‘commons’. If we see intellectual property as a position in a space of ideas, it is not hard to think of it like land.

Like land, a piece of intellectual property is unique and gains in value due to further improvements–applications or innovations–built upon it. In a world where intellectual property ownership never expires and isn’t shared, you can imagine that whoever hold some critical early work in some field could extract rents for perpetuity. Owning a patent would be like owning a land estate.

Like capital, intellectual property is produced by workers and often owned by those investing in the workers with pre-existing capital. The produced capital is then owned by the initiating capitalist, and accumulates.

Open source software is an important exception to this pattern. This kind of intellectual property is unalienated from those that produce it.

Preparing for SciPy 2014

I’ve been instructed to focus my attention on mid-level concepts rather than grand theory as I begin my empirical work.is

This is difficult for me, as I tend to oscillate between thinking very big and thinking very narrowly. This is an occupational hazard of a developer. Technical minutiae accumulate into something durable and powerful. To sustain ones motivation one has to be able to envision ones tiny tasks (correcting the spelling of some word in a program) stepping towards a larger project.

I’m working in my comfort zone. I’ve got my software project open on GitHub and I’m preparing to present my preliminary results at SciPy 2014 next week. A colleague and mentor I met with today told me it’s not a conference for people marking up career points. It’s a conference for people to meet each other, get an update on how their community is doing as a whole, and to learn new skills from each other.

It’s been a few years since I’ve been to a developer conference. In my past career I went to FOSS4G, the open source geospatial conference, a number of times. In 2008, the conference was in South Africa. I didn’t know anybody, so I blogged about it, and got chastised for being too divisive. I wasn’t being sensitive to the delicate balance between the open source developer geospatial community and their greatest proprietary coopetitor, ESRI. I was being an ideologue at a time when the open source model was in that industry just in its inflection point and becoming mainstream. Obviously I didn’t understand the subtlety of the relationships, business and personal, threaded through the conference.

Later I attended FOSS4G in 2010 to pitch the project my team had recently launched, GeoNode. It was a very exciting time for me personally. I was very personally invested in the project, and I was so proud of my team and myself for pulling through on the beta release. In retrospect, building a system for serving spatial data modeled on a content management system seems like a no-brainer. Today there are plenty of data management startups and services out there, some industrial, some academic. But at the time we were ahead of the curve, thanks largely to the vision of Chris Holmes, who at the time the wunderkind visionary president of OpenGeo.

Cholmes always envisioned OpenGeo turning into an anti-capitalist organization, a hacker coop with as much transparency as it could handle. If only it could get its business model right. It was incubating in a pre-crash bubble that thinned out over time. I was very into the politics of the organization when I joined it, but over time I became more cynical and embraced the economic logic I was being taught by the mature entrepreneurs who had been attracted to OpenGeo’s promise and standing in the geospatial world. While trying to wrap my head around managing developers, clients, and the budget around GeoNode, I began to see why businesses are the way they are, and how open source plays out in the industrial organization of the tech industry as a whole.

GeoNode, the project, remains a success. There is glory to that, though in retrospect I can claim little of it. I made many big mistakes and the success of the project has always been due to the very intelligent team working on it, as well as its institutional positioning.

I left OpenGeo because I wanted to be a scientist. I had spent four years there, and had found my way onto a project where we were building data plumbing for disaster reduction scientists and the military. OpenGeo had become a victim of its own success and outgrown its non-profit incubator, buckling under the weight of the demand for its services. I had deferred enrollment at Berkeley for a year to see GeoNode through to a place where it couldn’t get canned. My last major act was to raise funding for a v1.1 release that fixed the show-stopping bugs in the v1.0 version.

OpenGeo is now Boundless, a for-profit company. It’s better that way. It’s still doing revolutionary work.

I’ve been under the radar in the open source world for the three years I’ve been in grad school. But as I begin this dissertation work, I feel myself coming back to it. My research questions, in one framing, are about software ecosystem sustainability and management. I’m drawing from my experience participating in and growing open source communities and am trying to operationalize my intuitions from that work. At Berkeley I’ve discovered the scientific Python community, which I feel at home with since I learned about how to do open source from the inimitable Whit Morris, a Pythonista of the Plone cohort, among others.

After immersing myself in academia, I’m excited to get back into the open source development world. Some of the most intelligent and genuine people I’ve ever met work in that space. Like the sciences, it is a community of very smart and creative people with the privilege to pursue opportunity but with goals that go beyond narrow commercial interests. But it’s also in many ways a more richly collaborative and constructive community than the academic world. It’s not a prestige economy, where people are rewarded with scarce attention and even scarcer titles. It’s a constructive economy, where there is always room to contribute usefully, and to be recognized even in a small way for that contribution.

I’m going to introduce my research on the SciPy communities themselves. In the wake of the backlash against Facebook’s “manipulative” data science research, I’m relieved to be studying a community that has from the beginning wanted to be open about its processes. My hope is that my data scientific work will be a contribution to, not an exploitation of, the community I’m studying. It’s an exciting opportunity that I’ve been preparing for for a long time.

metaphorical problems with logical solutions

There are polarizing discourses on the Internet about the following four dichotomies:

  • Public vs. Private (information)
  • (Social) Inclusivity vs. Exclusivity.
  • Open vs. Closed (systems, properties, communities).

Each of these pairings enlists certain metaphors and intuitions. Rarely are they precisely defined.

Due to their intuitive pull, it’s easy to draw certain naive associations. I certainly do. But how do they work together logically?

To what extent can we fill in other octants of this cube? Or is that way of modeling it too simplistic as well?

If privacy is about having contextual control over information flowing out of oneself, then that means that somebody must have the option of closing off some access to their information. To close off access is necessarily to exclude.

PRIVATE => ¬OPEN => ¬INCLUSIVE

But it has been argued that open sociotechnical systems exclude as well by being inhospitable to those with greater need for privacy.

OPEN => ¬PRIVATE => ¬INCLUSIVE

These conditionals limit the kinds of communities that can exist.

PRIVATE OPEN INCLUSIVE POSSIBLE?
T T T F
T T F F
T F T F
T F F T
F T T F
F T F T
F F T F
F F F T

Social inclusivity in sociotechnical systems is impossible. There is no such thing as a sociotechnical system that works for everybody.

There are only three kinds of systems: open systems, private systems, or systems that are neither open nor private. We can call the latter leaky systems.

These binary logical relations capture only the limiting properties of these systems. If there has ever been an open system, it is the Internet; but everyone knows that even the Internet isn’t truly open because of access issues.

The difference between a private system and a leaky system is participant’s ability to control how their data escapes the system.

But in this case, systems that we call ‘open’ are often private systems, since participants choose whether or not to put information into the open.

So is the only question whether and when information is disclosed vs. leaked?

Protected: some ruminations regarding ‘openness’

This content is password protected. To view it please enter your password below:

turns out network backbone markets in the US are competitive after all

I’ve been depressed lately about the oligopolistic control of telecommunications for a while now. There’s the Web We’ve Lost; there’s Snowden leaks; there’s the end of net neutrality. I’ll admit a lot of my moodiness about this has been just that–moodiness. But it was moodiness tied to a particular narrative.

In this narrative, power is transmitted via flows of information. Media is, if not determinative of public opinion, determinative of how that opinion is acted up. Surveillance is also an information flow. Broadly, mid-20th century telecommunications enabled mass culture due to the uniformity of media. The Internet’s protocols allowed it to support a different kind of culture–a more participatory one. But monetization and consolidation of the infrastructure has resulted in a society that’s fragmented but more tightly controlled.

There is still hope of counteracting that trend at the software/application layer, which is part of the reason why I’m doing research on open source software production. One of my colleagues, Nick Doty, studies the governance of Internet Standards, which is another piece of the puzzle.

But if the networking infrastructure itself is centrally controlled, then all bets are off. Democracy, in the sense of decentralized power with checks and balances, would be undermined.

Yesterday I learned something new from Ashwin Mathew, another colleague who studies Internet governance at the level of network administration. The man is deep in the process of finishing up his dissertation, but he looked up from his laptop for long enough to tell me that the network backbone market is in fact highly competitive at the moment. Apparently, there was a lot of dark fiberoptic cable (“dark fiber“–meaning, no light’s going through it) laid during the first dot-com boom, which has been laying fallow and getting bought up by many different companies. Since there are many routes from A to B and excess capacity, this market is highly competitive.

Phew! So why the perception of oligopolistic control of networks? Because the consumer-facing telecom end-points ARE an oligopoly. Here there’s the last-mile problem. When wire has to be laid to every house, the economies of scale are such that it’s hard to have competitive markets. Enter Comcast etc.

I can rest easier now, because I think that this means there’s various engineering solutions to this (like AirJaldi networks? though I think those still aren’t last mile…; mesh networks?) as well as political solutions (like a local government running its last mile network as a public utility).

Protected:

This content is password protected. To view it please enter your password below:

Follow

Get every new post delivered to your Inbox.

Join 829 other followers