Digifesto

Tag: transparency

The Crevasse: a meditation on accountability of firms in the face of opacity as the complexity of scale

To recap:

(A1) Beneath corporate secrecy and user technical illiteracy, a fundamental source of opacity in “algorithms” and “machine learning” is the complexity of scale, especially scale of data inputs. (Burrell, 2016)

(A2) The opacity of the operation of companies using consumer data makes those consumers unable to engage with them as informed market actors. The consequence has been a “free fall” of market failure (Strandburg, 2013).

(A3) Ironically, this “free” fall has been “free” (zero price) for consumers; they appear to get something for nothing without knowing what has been given up or changed as a consequence (Hoofnagle and Whittington, 2013).

Comments:

(B1) The above line of argument conflates “algorithms”, “machine learning”, “data”, and “tech companies”, as is common in the broad discourse. That this conflation is possible speaks to the ignorance of the scholarly position on these topics, and ignorance that is implied by corporate secrecy, technical illiteracy, and complexity of scale simultaneously. We can, if we choose, distinguish between these factors analytically. But because, from the standpoint of the discourse, the internals are unknown, the general indication of a ‘black box’ organization is intuitively compelling.

(B1a) Giving in to the lazy conflation is an error because it prevents informed and effective praxis. If we do not distinguish between a corporate entity and its multiple internal human departments and technical subsystems, then we may confuse ourselves into thinking that a fair and interpretable algorithm can give us a fair and interpretable tech company. Nothing about the former guarantees the latter because tech companies operate in a larger operational field.

(B2) The opacity as the complexity of scale, a property of the functioning of machine learning algorithms, is also a property of the functioning of sociotechnical organizations more broadly. Universities, for example, are often opaque to themselves, because of their own internal complexity and scale. This is because the mathematics governing opacity as a function of complexity and scale are the same in both technical and sociotechnical systems (Benthall, 2016).

(B3) If we discuss the complexity of firms, as opposed the the complexity of algorithms, we should conclude that firms that are complex due to scale of operations and data inputs (including number of customers) will be opaque and therefore have strategic advantage in the market against less complex market actors (consumers) with stiffer bounds on rationality.

(B4) In other words, big, complex, data rich firms will be smarter than individual consumers and outmaneuver them in the market. That’s not just “tech companies”. It’s part of the MO of every firm to do this. Corporate entities are “artificial general intelligences” and they compete in a complex ecosystem in which consumers are a small and vulnerable part.

Twist:

(C1) Another source of opacity in data is that the meaning of data come from the causal context that generates it. (Benthall, 2018)

(C2) Learning causal structure from observational data is hard, both in terms of being data-intensive and being computationally complex (NP). (c.f. Friedman et al., 1998)

(C3) Internal complexity, for a firm, is not sufficient to be “all-knowing” about the data that is coming it; the firm has epistemic challenges of secrecy, illiteracy, and scale with respect to external complexity.

(C4) This is why many applications of machine learning are overrated and so many “AI” products kind of suck.

(C5) There is, in fact, an epistemic crevasse between all autonomous entities, each containing its own complexity and constituting a larger ecological field that is the external/being/environment for any other autonomy.

To do:

The most promising direction based on this analysis is a deeper read into transaction cost economics as a ‘theory of the firm’. This is where the formalization of the idea that what the Internet changed most are search costs (a kind of transaction cost) should be.

It would be nice if those insights could be expressed in the mathematics of “AI”.

There’s still a deep idea in here that I haven’t yet found the articulation for, something to do with autopoeisis.

References

Benthall, Sebastian. (2016) The Human is the Data Science. Workshop on Developing a Research Agenda for Human-Centered Data Science. Computer Supported Cooperative Work 2016. (link)

Sebastian Benthall. Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics. Ph.D. dissertation. Advisors: John Chuang and Deirdre Mulligan. University of California, Berkeley. 2018.

Burrell, Jenna. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data & Society 3.1 (2016): 2053951715622512.

Friedman, Nir, Kevin Murphy, and Stuart Russell. “Learning the structure of dynamic probabilistic networks.” Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 1998.

Hoofnagle, Chris Jay, and Jan Whittington. “Free: accounting for the costs of the internet’s most popular price.” UCLA L. Rev. 61 (2013): 606.

Strandburg, Katherine J. “Free fall: The online market’s consumer preference disconnect.” U. Chi. Legal F. (2013): 95.

Advertisements

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.