Digifesto

Protected: the research lately

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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

https://twitter.com/sandsfish/status/491627317375291393

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 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.

economic theory and intellectual property

I’ve started reading Piketty’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.

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’

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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).

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i’ve started working on my dissertation // diversity in open source // reflexive data science

I’m studying software development and not social media for my dissertation.

That’s a bit of a false dichotomy. Much software development happens through social media.

Which is really the point–that software development is a computer mediated social process.

What’s neat is that it’s a computer mediated social process that, at its best, creates the conditions for it to continue as a social process. c.f. Kelty’s “recursive public”

What’s also neat is that this is a significant kind of labor that is not easy to think about given the tools of neoclassical economics or anything else really.

In particular I’m focusing on the development of scientific software, i.e. software that’s made and used to improve our scientific understanding of the natural world and each other.

The data I’m looking at is communications data between developers and their users. I’m including the code, under version control, as this. In addition to being communication between developers, you might think of source code as a communication between developers and machines. The process of writing code as a collaboration or conversation between people and machines.

There is a lot of this data so I get to use computational techniques to examine it. “Data science,” if you like.

But it’s also legible, readable data with readily accessible human narrative behind it. As I debug my code, I am reading the messages sent ten years ago on a mailing list. Characters begin to emerge serendipitously because their email signatures break my archive parser. I find myself Googling them. “Who is that person?”

One email I found while debugging stood out because it was written, evidently, by a woman. Given the current press on diversity in tech, I thought it was an interesting example from 2001:

From sag at hydrosphere.com Thu Nov 29 15:21:04 2001
From: sag at hydrosphere.com (Sue Giller)
Date: Thu Nov 29 15:21:04 2001
Subject: [Numpy-discussion] Re: Using Reduce with Multi-dimensional Masked array
In-Reply-To: <000201c17917$ac5efec0$3d01a8c0@plstn1.sfba.home.com>
References: <20011129174809062.AAA210@mail.climatedata.com@SUEW2000>
Message-ID: <20011129232011546.AAA269@mail.climatedata.com@SUEW2000>

Paul,

Well, you’re right. I did misunderstand your reply, as well as what
the various functions were supposed to do. I was mis-using the
sum, minimum, maximum as tho they were MA..reduce, and
my test case didn’t point out the difference. I should always have
been doing the .reduce version.

I apologize for this!

I found a section on page 45 of the Numerical Python text (PDF
form, July 13, 2001) that defines sum as
‘The sum function is a synonym for the reduce method of the add
ufunc. It returns the sum of all the elements in the sequence given
along the specified axis (first axis by default).’

This is where I would expect to see a caveat about it not retaining
any mask-edness.

I was misussing the MA.minimum and MA.maximum as tho they
were .reduce version. My bad.

The MA.average does produce a masked array, but it has changed
the ‘missing value’ to fill_value=[ 1.00000002e+020,]). I do find this
a bit odd, since the other reductions didn’t change the fill value.

Anyway, I can now get the stats I want in a format I want, and I
understand better the various functions for array/masked array.

Thanks for the comments/input.

sue

I am trying to approach this project as a quantitative scientist. But the process of developing the software for analysis is putting me in conversation not just with the laptop I run the software on, but also the data. The data is a quantified representation–I count the number of lines, even the number of characters in a line as I construct the regular expression needed to parse the headers properly–but it represents a conversation in the past. As I write the software, I consult documentation written through a process not unlike the one I am examining, as well as Stack Overflow posts written by others who have tried to perform similar tasks. And now I am writing a blog post about this work. I will tweet a link of this out to my followers; I know some people from the Scientific Python community that I am studying follow me on Twitter. Will one of them catch wind of this post? What will they think of it?