Digifesto

causal inference in networks is hard

I am trying to make statistically valid inferences about the mechanisms underlying observational networked data and it is really hard.

Here’s what I’m up against:

  • Even though my data set is a complete ecologically valid data set representing a lot of real human communication over time, it (tautologically) leaves out everything that it leaves out. I can’t even count all the latent variables.
  • The best methods for detecting causal mechanism, the potential outcomes framework for Rubin model, depends on the assumption that different members of the sample don’t interfere. But I’m working with networked data. Everything interferes with everything else, at least indirectly. That’s why it’s a network.
  • Did I mention that I’m working with communications data? What’s interesting about human communication is that it’s not really generated at random at all. It’s very deliberately created by people acting more or less intelligently all the time. If the phenomenon I’m studying is not more complex than the models I’m using to study it, then there is something seriously wrong with the people I’m studying.

I think I can deal with the first point here by gracefully ignoring it. It may be true that any apparent causal effect in my data is spurious and due to a common latent cause upstream. It may be true that the variance in the data is largely due to exogenous factors. Fine. That’s noise. I’m looking for a reliable endogenous signal. If there isn’t something there that would suggest that my entire data set is epiphenomal. But I know it’s not. So there’s got to be something there.

For the second point, there are apparently sophisticated methods for extending the potential outcomes framework to handling peer effects. These are gnarly and though I figure I could work with them, I don’t think they are going to be what I need because I’m not really looking for a causal relationship like a statistical relationship between treatment and outcome. I’m not after in the first instance what might be called type causation. I’m rather trying to demonstrate cases of token causation where causation is literally the transfer of information from object to another. And then I’m trying to show regularity in this underlying kind of causation in a layer of abstraction over it.

The best angle I can come up with on this so far is to use emergent properties of the network like degree assortativity to sort through potential mathematically defined graph generation algorithms. These algorithms can act as alternative hypotheses, and the observed emergent properties can theoretically be used to compute the likelihood of the observed data given the generation methods. Then all I need is a prior over graph generation methods! It’s perfectly Bayesian! I wonder if it is at all feasible to execute on. I will try.

It’s not 100% clear how you can take an algorithmically defined process and turn that into a hypothesis about causal mechanisms. Theoretically, as long as a causal network has computable conditional dependencies it can be represented by and algorithm. I believe that any algorithm (in the Church/Turing sense) can be represented as a causal network. Can this be done elegantly, so that the corresponding causal network represents something like what we’d expect from the scientific theory on the matter? This is unclear because, again, Pearl’s causal networks are great at representing type causation but not as expressive of token causation among a large population of uniquely positioned, generatively produced stuff. Pearl is not good at modeling life, I think.

The strategic activity of the actors is a modeling challenge but I think this is actually where there is substantive potential in this kind of research. If effective strategic actors are working in a way that is observably different from naive actors in some way that’s measurable in aggregate behavior, that’s a solid empirical result! I have some hypotheses around this that I think are worth checking. For example, probably the success of an open source community depends in part on whether members of the community act in ways that successfully bring new members in. Strategies that cultivate new members are going to look different from strategies that exclude newcomers or try to maintain a superior status. Based on some preliminary results, it looks like this difference between successful open source projects and most other social networks is observable in the data.

Innovation, automation, and inequality

What is the economic relationship between innovation, automation, and inequality?

This is a recurring topic in the discussion of technology and the economy. It comes up when people are worried about a new innovation (such as data science) that threatens their livelihood. It also comes up in discussions of inequality, such as in Picketty’s Capital in the Twenty-First Century.

For technological pessimists, innovation implies automation, and automation suggests the transfer of surplus from many service providers to a technological monopolist providing a substitute service at greater scale (scale being one of the primary benefits of automation).

For Picketty, it’s the spread of innovation in the sense of the education of skilled labor that is primary force that counteracts capitalism’s tendency towards inequality and (he suggests) the implied instability. For the importance Picketty places on this process, he treats it hardly at all in his book.

Whether or not you buy Picketty’s analysis, the preceding discussion indicates how innovation can cut both for and against inequality. When there is innovation in capital goods, this increases inequality. When there is innovation in a kind of skilled technique that can be broadly taught, that decreases inequality by increasing the relative value of labor to capital (which is generally much more concentrated than labor).

I’m a software engineer in the Bay Area and realize that it’s easy to overestimate the importance of software in the economy at large. This is apparently an easy mistake for other people to make as well. Matthew Rognlie, the economist who has been declared Picketty’s latest and greatest challenger, thinks that software is an important new form of capital and draws certain conclusions based on this.

I agree that software is an important form of capital–exactly how important I cannot yet say. One reason why software is an especially interesting kind of capital is that it exists ambiguously as both a capital good and as a skilled technique. While naively one can consider software as an artifact in isolation from its social environment, in the dynamic information economy a piece of software is only as good as the sociotechnical system in which it is embedded. Hence, its value depends both on its affordances as a capital good and its role as an extension of labor technique. It is perhaps easiest to see the latter aspect of software by considering it a form of extended cognition on the part of the software developer. The human capital required to understand, reproduce, and maintain the software is attained by, for example, studying its source code and documentation.

All software is a form of innovation. All software automates something. There has been a lot written about the potential effects of software on inequality through its function in decision-making (for example: Solon Barocas, Andrew D. Selbst, “Big Data’s Disparate Impact” (link).) Much less has been said about the effects of software on inequality through its effects on industrial organization and the labor market. After having my antennas up for this for many reasons, I’ve come to a conclusion about why: it’s because the intersection between those who are concerned about inequality in society and those that can identify well enough with software engineers and other skilled laborers is quite small. As a result there is not a ready audience for this kind of analysis.

However unreceptive society may be to it, I think it’s still worth making the point that we already have a very common and robust compromise in the technology industry that recognizes software’s dual role as a capital good and labor technique. This compromise is open source software. Open source software can exist both as an unalienated extension of its developer’s cognition and as a capital good playing a role in a production process. Human capital tied to the software is liquid between the software’s users. Surplus due to open software innovations goes first to the software users, then second to the ecosystem of developers who sell services around it. Contrast this with the proprietary case, where surplus goes mainly to a singular entity that owns and sells the software rights as a monopolist. The former case is vastly better if one considers societal equality a positive outcome.

This has straightforward policy implications. As an alternative to Picketty’s proposed tax on capital, any policies that encourage open source software are ones that combat societal inequality. This includes procurement policies, which need not increase government spending. On the contrary, if governments procure primarily open software, that should lead to savings over time as their investment leads to a more competitive market for services. Equivalently, R&D funding to open science institutions results in more income equality than equivalent funding provided to private companies.

going post-ideology

I’ve spent a lot of my intellectual life in the grips of ideology.

I’m glad to be getting past all of that. That’s one reason why I am so happy to be part of Glass Bead Labs.

Glass Bead Labs

There are a lot of people who believe that it’s impossible to get beyond ideology. They believe that all knowledge is political and nothing can be known with true clarity.

I’m excited to have an opportunity to try to prove them wrong.

correcting an error in my analysis

There is an error in my last post where I was thinking through the interpretation of 25,000,000 hit number reported for the Buzzfeed blue/black/white/whatever dress post. In that post I assumed that the distribution of viewers would be the standard one you see in on-line participation: a power law distribution with a long tail. Depending on which way you hold the diagram, the “tail” is either the enormous number of instances that only occur once (in this case, a visitor who goes to the page once and never again) or it’s population of instances that have bizarrely high occurrences (like that one guy who hit refresh on the page 100 times, and the woman that looked at the page 300 times, and…). You can turn one tail into the other by turning the histogram sideways and shaking really hard.

The problem with this analysis is that it ignores the data I’ve been getting from a significant subset of people who I’ve talked to about this in passing, which is that because the page contains some sort of well-crafted optical illusion, lots of people have looked at it once (and seen it as, say, a blue and black dress) and then looked at it again, seeing it as white and gold. In fact the article seems designed to get the reader to do just this.

If I’m being somewhat abstract in my analysis, it’s because I’ve refused to go click on the link myself. I have read too much Adorno. I hear the drumbeat of fascism in all popular culture. I do not want to take part in intelligently designed collective effervescence if I can help it. This is my idiosyncrasy.

But this inferred stickiness of the dress image has consequences for the traffic analysis. I’m sure that whoever is actually looking at the metrics on the article is tracking repeat version unique visitors. I wonder how deliberately the image was created with the idea of maximizing repeat visitations in mind, and the observed correlation between repeat and unique visitors. Repeated visits suggests sustained interest over time, whereas “mere” virality is a momentary spread of information over space. If you see content as a kind of property and sustained traffic over time as the value of that property, it makes sense to try to create things with staying power. Memetic globules forever gunking the crisscrossed manifold of attention. Culture.

Does this require a different statistical distribution to process properly? Is Cosma Shalizi right after all, and are these “power law” distributions just overhyped log-normal distributions? What happens when the generative process has a stickiness term? Is that just reflected in the power law distribution’s exponent? One day I will get a grip on this. Maybe I can do it working with mailing list data.

I’m writing this because over the weekend I was talking with a linguist and a philosopher about collective attention, a subject of great interest to me. It was the linguist who reported having looked at the dress twice and seeing it in different colors. The philosopher had not seen it. The latter’s research specialty was philosophy of mind, a kind of philosophy I care about a lot. I asked him whether in cases of collective attention the mental representation supervenes reductively on many individual minds or on more than that. He said that this is a matter of current debate but that he wants to argue that collective attention means more than my awareness of X, and my awareness of your awareness of X, ad infinitum. Ultimately I’m a mathematical person and am happy to see the limit of the infinite process as itself and its relationship with what it reduces to mediated by the logic of infinitesimals. But perhaps even this is not enough. I gave the philosopher my recommendation of Soren Brier and Ulanowicz, who together I think provide the groundwork needed for an ontology of macroorganic mentality and representation. The operationalization of these theories is the goal of my work at Glass Bead Labs.

25,000,000 re: @ftrain

It was gratifying to read Paul Ford’s reluctant think piece about the recent dress meme epidemic.

The most interesting fact in the article was that Buzzfeed’s dress article has gotten 25 million views:

People are also keenly aware that BuzzFeed garnered 25 million views (and climbing) for its article about the dress. Twenty-five million is a very, very serious number of visitors in a day — the sort of traffic that just about any global media property would kill for (while social media is like, ho hum).

I’ve recently become interested in the question: how important is the Internet, really? Those of us who work closely with it every day see it as central to our lives. Logically, we would tend to extrapolate and think that it is central to everybody’s life. If we are used to sampling from other’s experience using social media, we would see that social media is very important in everybody’s life, confirming this suspicion.

This is obviously a kind of sampling bias though.

This is where the 25,000,000 figure comes in handy. My experience of the dress meme was that it was completely ubiquitous. Literally nobody I was following on Twitter who was tweeting that day was not at least referencing the dress. The meme also got to me via an email backchannel, and came up in a seminar. Perhaps you had a similar experience: you and everyone you knew was aware of this meme.

Let’s assume that 25 million is an indicator of the order of magnitude of people that learned about this meme. If you googled the dress question, you probably clicked the article. Maybe you clicked it twice. Maybe you clicked it twenty times and you are an outlier. Maybe you didn’t click it at all. It’s plausible that it evens out and the actual number of people who were aware of the meme is somewhere between 10 million and 50 million.

That’s a lot of people. But–and this is really my point–it’s not that many people, compared to everybody. There’s about 300 million people in the United States. There’s over 7 billion people on the planet. Who are the tenth of the population who were interested in the dress? If you are reading this blog, they are probably people a lot like you or I. Who are the other ~93% of people in the U.S.?

I’ve got a bold hypothesis. My hypothesis is that the other 90% of people are people who have lives. I mean this in the sense of the idiom “get a life“, which has fallen out of fashion for some reason. Increasingly, I’m becoming interested in the vast but culturally foreign population of people who followed this advice at some point in their lives and did not turn back. Does anybody know of any good ethnographic work about them? Where do they hang out in the Bay Area?

‘Bad twitter’ : exit, voice, and social media

I made the mistake in the past couple of days of checking my Twitter feed. I did this because there are some cool people on Twitter and I want to have conversations with them.

Unfortunately it wasn’t long before I started to read things that made me upset.

I used to think that a benefit of Twitter was that it allowed for exposure to alternative points of view. Of course you should want to see the other side, right?

But then there’s this: if you do that for long enough, you start to see each “side” make the same mistakes over and over again. It’s no longer enlightening. It’s just watching a train wreck in slow motion on repeat.

Hirschman’s Exit, Voice, and Loyalty is relevant to this. Presumably, over time, those who want a higher level of conversation Exit social media (and its associated news institutions, such as Salon.com) to more private channels, causing a deterioration in the quality of public discourse. Because social media sites have very strong network effects, they are robust to any revenue loss due to quality-sensitive Exiters, leaving a kind of monopoly-tyranny that Hirschman describes vividly thus:

While of undoubted benefit in the case of the exploitative, profit-maximizing monopolist, the presence of competition could do more harm than good when the main concern is to counteract the monopolist’s tendency toward flaccidity and mediocrity. For, in that case, exit-competition could just fatally weaken voice along the lines of the preceding section, without creating a serious threat to the organization’s survival. This was so for the Nigerian Railway Corporation because of the ease with which it could dip into the public treasury in case of deficit. But there are many other cases where competition does not restrain monopoly as it is supposed to, but comforts and bolsters it by unburdening it of its more troublesome customers. As a result, one can define an important and too little noticed type of monopoly-tyranny: a limited type, an oppression of the weak by the incompetent and an exploitation of the poor by the lazy which is the more durable and stifling as it is both unambitious and escapable. The contrast is stark indeed with totalitarian, expansionist tyrannies or the profit-maximizing, accumulation-minded monopolies which may have captured a disproportionate share of our attention.

It’s interesting to compare a Hirschman-inspired view of the decline of Twitter as a function of exit and voice to a Frankfurt School analysis of it in terms of the culture industry. It’s also interesting to compare this with boyd’s 2009 paper on “White flight in networked publics?” in which she chooses to describe the decline of MySpace in terms of the troubled history of race and housing.*

In particular, there are passages of Hirschman in which he addresses neighborhoods of “declining quality” and the exit and voice dynamics around them. It is interesting to me that the narrative of racialized housing policy and white flight is so salient to me lately that I could not read these passages of Hirschman without raising an eyebrow at the fact that he didn’t mention race in his analysis. Was this color-blind racism? Or am I now so socialized by the media to see racism and sexism everywhere that I assumed there were racial connotations when in fact he was talking about a general mechanism. Perhaps the salience of the white flight narrative to me has made me tacitly racist by making me assume that the perceived decline in neighborhood quality is due to race!

The only way I could know for sure what was causing what would be to conduct a rigorous empirical analysis I don’t have time for. And I’m an academic whose job is to conduct rigorous empirical analyses! I’m forced to conclude that without a more thorough understanding of the facts, any judgment either way will be a waste of time. I’m just doing my best over here and when push comes to shove I’m a pretty nice guy, my friends say. Nevertheless, it’s this kind of lazy baggage-slinging that is the bread and butter of the mass journalist today. Reputations earned and lost on the basis of political tribalism! It’s almost enough to make somebody think that these standards matter, or are the basis of a reasonable public ethics of some kind that must be enforced lest society fall into barbarism!

I would stop here except that I am painfully aware that as much as I know it to be true that there is a portion of the population that has exited the morass of social media and put it to one side, I know that many people have not. In particular, a lot of very smart, accomplished friends of mine are still wrapped up in a lot of stupid shit on the interwebs! (Pardon my language!) This is partly due to the fact that networked publics now mediate academic discourse, and so a lot of aspiring academics now feel they have to be clued in to social media to advance their careers. Suddenly, everybody who is anybody is a content farmer! There’s a generation who are looking up to jerks like us! What the hell?!?!

This has a depressing consequence. Since politically divisive content is popular content, and there is pressure for intellectuals to produce popular content, this means that intellectuals have incentives to propagate politically divisive narratives instead of working towards reconciliation and the greater good. Or, alternatively, there is pressure to aim for the lowest common denominator as an audience.

At this point, I am forced to declare myself an elitist who is simply against provocation of any kind. It’s juvenile, is the problem. (Did I mention I just turned 30? I’m an adult now, swear to god.) I would keep this opinion to myself, but at that point I’m part of the problem by not exercising my Voice option. So here’s to blogging.

* I take a particular interest in danah boyd’s work because in addition to being one of the original Internet-celebrity-academics-talking-about-the-Internet and so aptly doubles as both the foundational researcher and just slightly implicated subject matter for this kind of rambling about social media and intellectualism (see below), she also shares an alma mater with me (Brown) and is the star graduate of my own department (UC Berkeley’s School of Information) and so serves as a kind of role model.

I feel the need to write this footnote because while I am in the scholarly habit of treating all academic writers I’ve never met abstractly as if they are bundles of text subject to detached critique, other people think that academics are real people(!), especially academics themselves. Suddenly the purely intellectual pursuit becomes personal. Multiple simultaneous context collapses create paradoxes on the level of pragmatics that would make certain kinds of communication impossible if they are not ignored. This can be awkward but I get a kind of perverse pleasure out of leaving analytic puzzles to whoever comes next.

I’m having a related but eerier intellectual encounter with an Internet luminary in some other work I’m doing. I’m writing software to analyze a mailing list used by many prominent activists and professionals. Among the emails are some written by the late Aaron Swartz. In the process of working on the software, I accepted a pull request from a Swiss programmer I had never met which has the Python package html2text as a dependency. Who wrote the html2text package? Aaron Swartz. Understand I never met the guy, am trying to map out how on-line communication mediates the emergent structure of the sociotechnical ecosystem of software and the Internet, and obviously am interested reflexively in how my own communication and software production fits into that larger graph. (Or multigraph? Or multihypergraph?) Power law distributions of connectivity on all dimensions make this particular situation not terribly surprising. But it’s just one of many strange loops.

Hirschman, Nigerian railroads, and poor open source user interfaces

Hirschman says he got the idea for Exit, Voice, and Loyalty when studying the failure of the Nigerian railroad system to improve quality despite the availability of trucking as a substitute for long-range shipping. Conventional wisdom among economists at the time was that the quality of a good would suffer when it was provisioned by a monopoly. But why would a business that faced healthy competition not undergo the management changes needed to improve quality?

Hirschman’s answer is that because the trucking option was so readily available as an alternative, there wasn’t a need for consumers to develop their capacity for voice. The railroads weren’t hearing the complaints about their service, they were just seeing a decline in use as their customers exited. Meanwhile, because it was a monopoly, loss in revenue wasn’t “of utmost gravity” to the railway managers either.

The upshot of this is that it’s only when customers are locked in that voice plays a critical role in the recuperation mechanism.

This is interesting for me because I’m interested in the role of lock-in in software development. In particular, one argument made in favor of open source software is that because it is not technology held by a single firm, users of the software are not locked-in. Their switching costs are reduced, making the market more liquid and, in theory favorable.

You can contrast this with proprietary enterprise software, where vendor lock-in is a principle part of the business model as this establishes the “installed base” and customer support armies are necessary for managing disgruntled customer voice. Or, in the case of social media such as Facebook, network effects create a kind of perceived consumer lock-in and consumer voice gets articulated by everybody from Twitter activists to journalists to high-profile academics.

As much as it pains me to admit it, this is one good explanation for why the user interfaces of a lot of open source software projects are so bad specifically if you combine this mechanism with the idea that user-centered design is important for user interfaces. Open source projects generally make it easy to complain about the software. If they know what they are doing at all, they make it clear how to engage the developers as a user. There is a kind of rumor out there that open source developers are unfriendly towards users and this is perhaps true when users are used to the kind of customer support that’s available on a product for which there is customer lock-in. It’s precisely this difference between exit culture and voice culture, driven by the fundamental economics of the industry, that creates this perception. Enterprise open source business models (I’m thinking about models like the Pentaho ‘beekeeper’) theoretically provide a corrective to this by being an intermediary between consumer voice and developer exit.

A testable hypothesis is whether and to what extent a software project’s responsiveness to tickets scales with the number of downstream dependent projects. In software development, technical architecture is a reasonable proxy for industrial organization. A widely used project has network effects that increasing switching costs for its downstream users. How do exit and voice work in this context?

The node.js fork — something new to think about

For Classics we are reading Albert Hirschman’s Exit, Voice, and Loyalty. Oddly, though normally I hear about ‘voice’ as an action from within an organization, the first few chapters of the book (including the introduction of the Voice concept itselt), are preoccupied with elaborations on the neoclassical market mechanism. Not what I expected.

I’m looking for interesting research use cases for BigBang, which is about analyzing the sociotechnical dynamics of collaboration. I’m building it to better understand open source software development communities, primarily. This is because I want to create a harmonious sociotechnical superintelligence to take over the world.

For a while I’ve been interested in Hadoop’s interesting case of being one software project with two companies working together to build it. This is reminiscent (for me) of when we started GeoExt at OpenGeo and Camp2Camp. The economics of shared capital are fascinating and there are interesting questions about how human resources get organized in that sort of situation. In my experience, there becomes a tension between the needs of firms to differentiate their products and make good on their contracts and the needs of the developer community whose collective value is ultimately tied to the robustness of their technology.

Unfortunately, building out BigBang to integrate with various email, version control, and issue tracking backends is a lot of work and there’s only one of me right now to both build the infrastructure, do the research, and train new collaborators (who are starting to do some awesome work, so this is paying off.) While integrating with Apache’s infrastructure would have been a smart first move, instead I chose to focus on Mailman archives and git repositories. Google Groups and whatever Apache is using for their email lists do not publish their archives in .mbox format, which is pain for me. But luckily Google Takeout does export data from folks’ on-line inbox in .mbox format. This is great for BigBang because it means we can investigate email data from any project for which we know an insider willing to share their records.

Does a research ethics issue arise when you start working with email that is openly archived in a difficult format, then exported from somebody’s private email? Technically you get header information that wasn’t open before–perhaps it was ‘private’. But arguably this header information isn’t personal information. I think I’m still in the clear. Plus, IRB will be irrelevent when the robots take over.

All of this is a long way of getting around to talking about a new thing I’m wondering about, the Node.js fork. It’s interesting to think about open source software forks in light of Hirschman’s concepts of Exit and Voice since so much of the activity of open source development is open, virtual communication. While you might at first think a software fork is definitely a kind of Exit, it sounds like IO.js was perhaps a friendly fork of just somebody who wanted to hack around. In theory, code can be shared between forks–in fact this was the principle that GitHub’s forking system was founded on. So there are open questions (to me, who isn’t involved in the Node.js community at all and is just now beginning to wonder about it) along the lines of to what extent a fork is a real event in the history of the project, vs. to what extent it’s mythological, vs. to what extent it’s a reification of something that was already implicit in the project’s sociotechnical structure. There are probably other great questions here as well.

A friend on the inside tells me all the action on this happened (is happening?) on the GitHub issue tracker, which is definitely data we want to get BigBang connected with. Blissfully, there appear to be well supported Python libraries for working with the GitHub API. I expect the first big hurdle we hit here will be rate limiting.

Though we haven’t been able to make integration work yet, I’m still hoping there’s some way we can work with MetricsGrimoire. They’ve been a super inviting community so far. But our software stacks and architecture are just different enough, and the layers we’ve built so far thin enough, that it’s hard to see how to do the merge. A major difference is that while MetricsGrimoire tools are built to provide application interfaces around a MySQL data backend, since BigBang is foremost about scientific analysis our whole data pipeline is built to get things into Pandas dataframes. Both projects are in Python. This too is a weird microcosm of the larger sociotechnical ecosystem of software production, of which the “open” side is only one (important) part.

data science and the university

This is by now a familiar line of thought but it has just now struck me with clarity I wanted to jot down.

  1. Code is law, so the full weight of human inquiry should be brought to bear on software system design.
  2. (1) has been understood by “hackers” for years but has only recently been accepted by academics.
  3. (2) is due to disciplinary restrictions within the academy.
  4. (3) is due to the incentive structure of the academy.
  5. Since there are incentive structures for software development that are not available for subjects whose primary research project is writing, the institutional conditions that are best able to support software work and academic writing work are different.
  6. Software is a more precise and efficious way of communicating ideas than writing because its interpretation is guaranteed by programming language semantics.
  7. Because of (6), there is selective pressure to making software the lingua franca of scholarly work.
  8. (7) is inducing a cross-disciplinary paradigm shift in methods.
  9. (9) may induce a paradigm shift in theoretical content, or it may result in science whose contents are tailored to the efficient execution of adaptive systems. (This is not to say that such systems are necessarily atheoretic, just that they are subject to different epistemic considerations).
  10. Institutions are slow to change. That’s what makes them institutions.
  11. By (5), (7), and (9), the role of universities as the center of research is being threatened existentially.
  12. But by (1), the myriad intellectual threads currently housed in universities are necessary for software system design, or are at least potentially important.
  13. With (11) and (12), a priority is figuring out how to manage a transition to software-based scholarship without information loss.

a brief comment on feminist epistemology

One funny thing about having a blog is that I can tell when people are interested in particular posts through the site analytics. To my surprise, this post about Donna Haraway has been getting an increasing number of hits each month since I posted it. That is an indication that it has struck a chord, since steady exogenous growth like that is actually quite rare.

It is just possible that this means that people interested in feminist epistemology have been reading my blog lately. They probably have correctly guessed that I have not been the biggest fan of feminist epistemology because of concerns about bias.

But I’d like to take the opportunity to say that my friend Rachel McKinney has been recommending I read Elizabeth Anderson‘s stuff if I want to really get to know this body of theory. Since Rachel is an actual philosopher and I am an amateur who blogs about it on weekends, I respect her opinion on this a great deal.

So today I started reading through Anderson’s Stanford Encyclopedia of Philosophy article on Feminist Epistemology and I have to say I think it’s very good. I like her treatment of the situated knower. It’s also nice to learn that there are alternative feminist epistemologies to certain standpoint theories that I think are troublesome. In particular, it turns out that those standpoint theories are now considered by feminist philosophers to from a brief period in the 80’s that they’ve moved past already! Now subaltern standpoints are considered privileged in terms of discovery more than privileged in terms of justification.

This position is certainly easier to reconcile with computational methods. For example, it’s in a sense just mathematically mathematically correct if you think about it in terms of information gain from a sample. This principle appears to have been rediscovered in a way recently by the equity-in-data-science people when people talk about potential classifier error.

I’ve got some qualms about the articulation of this learning principle in the absence of a particular inquiry or decision problem because I think there’s still a subtle shift in the argumentation from logos to ethos embedded in there (I’ve been seeing things through the lens of Aristotelian rhetoric lately and it’s been surprisingly illuminating). I’m on the lookout for a concrete application of where this could apply in a technical domain, as opposed to as an articulation of a political affinity or anxiety in the language of algorithms. I’d be grateful for links in the comments.

Edit:

Wait, maybe I already built one. I am not sure if that really counts.

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