Digifesto

Tag: inequality

May 24, 2018

Inequality perceived through implicit factor analysis and its implications for emergent social forms

Vox published an interview with Keith Payne, author of The Broken Ladder.

My understanding is that the thesis of the book is that income inequality has a measurable effect on public health, especially certain kinds of chronic illnesses. The proposed mechanism for this effect is the psychological state of those perceiving themselves to be relatively worse off. This is a hardwired mechanism, it would seem, and one that is being turned on more and more by socioeconomic conditions today.

I’m happy to take this argument for granted until I hear otherwise. I’m interested in (and am jotting notes down here, not having read the book) the physics of this mechanism. It’s part of a larger puzzle about social forms, emergent social properties, and factor analysis that I’ve written about it some other posts.

Here’s the idea: income inequality is a very specific kind of social metric and not one that is easy to directly perceive. Measuring it from tax records, which short be straightforward, is fraught with technicalities. Therefore, it is highly implausible that direct perception of this metric is what causes the psychological impact of inequality.

Therefore, there must be one or more mediating factors between income inequality as an economic fact and psychological inequality as a mental phenomenon. Let’s suppose–because it’s actually what we should see as a ‘null hypothesis’–that there are many, many factors linking these phenomena. Some may be common causes of income inequality and psychological inequality, such as entrenched forms of social inequality that prevent equal access to resources and are internalized somehow. Others may be direct perception of the impact of inequality, such as seeing other people flying in higher class seats, or (ahem) hearing other people talk about flying at all. And yet we seem comfortable deriving from this very complex mess a generalized sense of inequality and its impact, and now that’s one of the most pressing political topics today.

I want to argue that when a person perceives inequality in a general way, they are in effect performing a kind of factor analysis on their perceptions of other people. When we compare ourselves with others, we can do so on a large number of dimensions. Cognitively, we can’t grok all of it–we have to reduce the feature space, and so we come to understand the world through a few blunt indicators that combine many other correlated data points into one.

These blunt categories can suggest that there is structure in the world that isn’t really there, but rather is an artifact of constraints on human perception and cognition. In other words, downward causation would happen in part through a dimensionality reduction of social perception.

On the other hand, if those constraints are regular enough, they may in turn impose a kind of structure on the social world (upward causation). If downward causation and upward causation reinforced each other, then that would create some stable social conditions. But there’s also no guarantee that stable social perceptions en masse track the real conditions. There may be systematic biases.

I’m not sure where this line of inquiry goes, to be honest. It needs more work.

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December 2, 2017

How to promote employees using machine learning without societal bias

Though it may at first read as being callous, a managerialist stance on inequality in statistical classification can help untangle some of the rhetoric around this tricky issue.

Consider the example that’s been in the news lately:

Suppose a company begins to use an algorithm to make decisions about which employees to promote. It uses a classifier trained on past data about who has been promoted. Because of societal bias, women are systematically under-promoted; this is reflected in the data set. The algorithm, naively trained on the historical data, reproduces the historical bias.

This example describes a bad situation. It is bad from a social justice perspective; by assumption, it would be better if men and women had equal opportunity in this work place.

It is also bad from a managerialist perspective. Why? Because if the point of using an algorithm were not to correct for societal biases introducing irrelevancies into the promotion decision, then it would not make managerial sense to change business practices over to using an algorithm. The whole point of using an algorithm is to improve on human decision-making. This is a poor match of an algorithm to a problem.

Unfortunately, what makes this example compelling is precisely what makes it a bad example of using an algorithm in this context. The only variables discussed in the example are the socially salient ones thick with political implications: gender, and promotion. What are more universal concerns than gender relations and socioeconomic status?!

But from a managerialist perspective, promotions should be issued based on a number of factors not mentioned in the example. What factors are these? That’s a great and difficult question. Promotions can reward hard work and loyalty. They can also be issued to those who demonstrate capacity for leadership, which can be a function of how well they get along with other members of the organization. There may be a number of features that predict these desirable qualities, most of which will have to do with working conditions within the company as opposed to qualities inherent in the employee (such as their past education, or their gender).

If one were to start to use machine learning intelligently to solve this problem, then one would go about solving it in a way entirely unlike the procedure in the problematic example. One would rather draw on soundly sourced domain expertise to develop a model of the relationship between relevant, work-related factors. For many of the key parts of the model, such as general relationships between personality type, leadership style, and cooperation with colleagues, one would look outside the organization for gold standard data that was sampled responsibly.

Once the organization has this model, then it can apply it to its own employees. For this to work, employees would need to provide significant detail about themselves, and the company would need to provide contextual information about the conditions under which employees work, as these may be confounding factors.

Part of the merit of building and fitting such a model would be that, because it is based on a lot of new and objective scientific considerations, it would produce novel results in recommending promotions. Again, if the algorithm merely reproduced past results, it would not be worth the investment in building the model.

When the algorithm is introduced, it ideally is used in a way that maintains traditional promotion processes in parallel so that the two kinds of results can be compared. Evaluation of the algorithm’s performance, relative to traditional methods, is a long, arduous process full of potential insights. Using the algorithm as an intervention at first allows the company to develop a causal understanding its impact. Insights from the evaluation can be factored back into the algorithm, improving the latter.

In all these cases, the company must keep its business goals firmly in mind. If they do this, then the rest of the logic of their method falls out of data science best practices, which are grounded in mathematical principles of statistics. While the political implications of poorly managed machine learning are troubling, effective management of machine learning which takes the precautions necessary to develop objectivity is ultimately a corrective to social bias. This is a case where sounds science and managerialist motives and social justice are aligned.

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December 1, 2017

Enlightening economics reads

Nils Gilman argues that the future of the world is wide open because neoliberalism has been discredited. So what’s the future going to look like?

Given that neoliberalism is for the most part an economic vision, and that competing theories have often also been economic visions (when they have not been political or theological theories), a compelling futurist approach is to look out for new thinking about economics. The three articles below have recently taught me something new about economics:

Dani Rodrik. “Rescuing Economics from Neoliberalism”, Boston Review. (link)

This article makes the case that the association frequently made between economics as a social science and neoliberalism as an ideology is overdrawn. Of course, probably the majority of economists are not neoliberals. Rodrik is defending a view of economics that keeps its options open. I think he overstates the point with the claim, “Good economists know that the correct answer to any question in economics is: it depends.” This is just simply incorrect, if questions have their assumptions bracketed well enough. But since Rodrik’s rhetorical point appears to be that economists should not be dogmatists, he can be forgiven this overstatement.

As an aside, there is something compelling but also dangerous to the view that a social science can provide at best narrowly tailored insights into specific phenomena. These kinds of ‘sciences’ wind up being unaccountable, because the specificity of particular events prevent the repeated testing of the theories that are used to explain them. There is a risk of too much nuance, which is akin to the statistical concept of overfitting.

A different kind of article is:

Seth Ackerman. “The Disruptors” Jacobin. (link)

An interview with J.W. Mason in the smart socialist magazine, Jacobin, that had the honor of a shout out from Matt Levine’s popular “Money Talk” Bloomberg column (column?). On of the interesting topics it raises is whether or not mutual funds, in which many people invest in a fund that then owns a wide portfolio of stocks, are in a sense socialist and anti-competitive because shareholders no longer have an interest in seeing competition in the market.

This is original thinking, and the endorsement by Levine is an indication that it’s not a crazy thing to consider even for the seasoned practical economists in the financial sector. My hunch at this point in life is that if you want to understand the economy, you have to understand finance, because they are the ones whose job it is to profit from their understanding of the economy. As a corollary, I don’t really understand the economy because I don’t have a great grasp of the financial sector. Maybe one day that will change.

Speaking of expertise being enhanced by having ‘skin in the game’, the third article is:

Nassim Nicholas Taleb. “Inequality and Skin in the Game,” Medium. (link)

I haven’t read a lot of Taleb though I acknowledge he’s a noteworthy an important thinker. This article confirmed for me the reputation of his style. It was also a strikingly fresh look at economics of inequality, capturing a few of the important things mainstream opinion overlooks about inequality, namely:

  • Comparing people at different life stages is a mistake when analyzing inequality of a population.
  • A lot of the cause of inequality is randomness (as opposed to fixed population categories), and this inequality is inevitable

He’s got a theory of what kinds of inequality people resent versus what they tolerate, which is a fine theory. It would be nice to see some empirical validation of it. He writes about the relationship between ergodicity and inequality, which is interesting. He is scornful of Piketty and everyone who was impressed by Piketty’s argument, which comes off as unfriendly.

Much of what Taleb writes about the need to understand the economy through a richer understanding of probability and statistics strikes me as correct. If it is indeed the case that mainstream economics has not caught up to this, there is an opportunity here!

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November 26, 2017

Recap

Sometimes traffic on this blog draws attention to an old post from years ago. This can be a reminder that I’ve been repeating myself, encountering the same themes over and over again. This is not necessarily a bad thing, because I hope to one day compile the ideas from this blog into a book. It’s nice to see what points keep resurfacing.

One of these points is that liberalism assumes equality, but this challenged by society’s need for control structures, which creates inequality, which then undermines liberalism. This post calls in Charles Taylor (writing about Hegel!) to make the point. This post makes the point more succinctly. I’ve been drawing on Beniger for the ‘society needs control to manage its own integration’ thesis. I’ve pointed to the term managerialism as referring to an alternative to liberalism based on the acknowledgement of this need for control structures. Managerialism looks a lot like liberalism, it turns out, but it justifies things on different grounds and does not get so confused. As an alternative, more Bourdieusian view of the problem, I consider the relationship between capital, democracy, and oligarchy here. There are some useful names for what happens when managerialism goes wrong and people seem disconnected from each other–anomie–or from the control structures–alienation.

A related point I’ve made repeatedly is the tension between procedural legitimacy and getting people the substantive results that they want. That post about Hegel goes into this. But it comes up again in very recent work on antidiscrimination law and machine learning. What this amounts to is that attempts to come up with a fair, legitimate procedure are going to divide up the “pie” of resources, or be perceived to divide up the pie of resources, somehow, and people are going to be upset about it, however the pie is sliced.

A related theme that comes up frequently is mathematics. My contention is that effective control is a technical accomplishment that is mathematically optimized and constrained. There are mathematical results that reveal necessary trade-offs between values. Data science has been misunderstood as positivism when in fact it is a means of power. Technical knowledge and technology are forms of capital (Bourdieu again). Perhaps precisely because it is a rare form of capital, science is politically distrusted.

To put it succinctly: lack of mathematics education, due to lack of opportunity or mathophobia, lead to alienation and anomie in an economy of control. This is partly reflected in the chaotic disciplinarity of the social sciences, especially as they react to computational social science, at the intersection of social sciences, statistics, and computer science.

Lest this all seem like an argument for the mathematical certitude of totalitarianism, I have elsewhere considered and rejected this possibility of ‘instrumentality run amok‘. I’ve summarized these arguments here, though this appears to have left a number of people unconvinced. I’ve argued this further, and think there’s more to this story (a formalization of Scott’s arguments from Seeing Like a State, perhaps), but I must admit I don’t have a convincing solution to the “control problem” yet. However, it must be noted that the answer to the control problem is an empirical or scientific prediction, not a political inclination. Whether or not it is the most interesting or important question regarding technological control has been debated to a stalemate, as far as I can tell.

As I don’t believe singleton control is a likely or interesting scenario, I’m more interested in practical ways of offering legitimacy or resistance to control structures. I used to think the “right” political solution was a kind of “hacker class consciousness“; I don’t believe this any more. However, I still think there’s a lot to the idea of recursive publics as actually existing alternative power structures. Platform coops are interesting for the same reason.

All this leads me to admit my interest in the disruptive technology du jour, the blockchain.

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November 19, 2017

On achieving social equality

When evaluating a system, we have a choice of evaluating its internal functions–the inside view–or evaluating its effects situated in a larger context–the outside view.

Decision procedures (whether they are embodied by people or performed in concert with mechanical devices–I don’t think this distinction matters here) for sorting people are just such a system. If I understand correctly, the question of which principles animate antidiscrimination law hinge on this difference between the inside and outside view.

We can look at a decision-making process and evaluate whether as a procedure it achieves its goals of e.g. assigning credit scores without bias against certain groups. Even including processes of the gathering of evidence or data in such a system, it can in principle be bounded and evaluated by its ability to perform its goals. We do seem to care about the difference between procedural discrimination and procedural nondiscrimination. For example, an overtly racist policy that ignores truly talent and opportunity seems worse than a bureaucratic system that is indifferent to external inequality between groups that then gets reflected in decisions made according to other factors that are merely correlated with race.

The latter case has been criticized in the outside view. The criticism is captured by the phrasing that “algorithms can reproduce existing biases”. The supposedly neutral algorithm (which can, again, be either human or machine) is not neutral in its impact because in making its considerations of e.g. business interest are indifferent to the conditions outside it. The business is attracted to wealth and opportunity, which are held disproportionately by some part of the population, so the business is attracted to that population.

There is great wisdom in recognizing that institutions that are neutral in their inside view will often reproduce bias in the outside view. But it is incorrect to therefore conflate neutrality in the inside view with a biased inside view, even though their effects may be under some circumstances the same. When I say it is “incorrect”, I mean that they are in fact different because, for example, if the external conditions of procedurally neutral institution change, then it will reflect those new conditions. A procedurally biased institution will not reflect those new conditions in the same way.

Empirically it is very hard to tell when an institution is being procedurally neutral and indeed this is the crux of an enormous amount of political tension today. The first line of defense of an institution blamed of bias is to claim that their procedural neutrality is merely reflecting environmental conditions outside of its control. This is unconvincing for many politically active people. It seems to me that it is now much more common for institutions to avoid this problem by explicitly declaring their bias. Rather than try to accomplish the seemingly impossible task of defending their rigorous neutrality, it’s easier to declare where one stands on the issue of resource allocation globally and adjust ones procedure accordingly.

I don’t think this is a good thing.

One consequence of evaluating all institutions based on their global, “systemic” impact as opposed to their procedural neutrality is that it hollows out the political center. The evidence is in that politics has become more and more polarized. This is inevitable if politics becomes so explicitly about maintaining or reallocating resources as opposed to about building neutrally legitimate institutions. When one party in Congress considers a tax bill which seems designed mainly to enrich ones own constituencies at the expense of the other’s things have gotten out of hand. The idea of a unified idea of ‘good government’ has been all but abandoned.

An alternative is a commitment to procedural neutrality in the inside view of institutions, or at least some institutions. The fact that there are many different institutions that may have different policies is indeed quite relevant here. For while it is commonplace to say that a neutral institution will “reproduce existing biases”, “reproduction” is not a particularly helpful word here. Neither is “bias”. What we can say more precisely is that the operations of procedurally neutral institution will not change the distribution of resources even though they are unequal.

But if we do not hold all institutions accountable for correcting the inequality of society, isn’t that the same thing as approving of the status quo, which is so unequal? A thousand times no.

First, there’s the problem that many institutions are not, currently, procedurally neutral. Procedural neutrality is a higher standard than what many institutions are currently held to. Consider what is widely known about human beings and their implicit biases. One good argument for transferring decision-making authority to machine learning algorithms, even standard ones not augmented for ‘fairness’, is that they will not have the same implicit, inside, biases as the humans that currently make these decisions.

Second, there’s the fact that responsibility for correcting social inequality can be taken on by some institutions that are dedicated to this task while others are procedurally neutral. For example, one can consistently believe in the importance of a progressive social safety net combined with procedurally neutral credit reporting. Society is complex and perhaps rightly has many different functioning parts; not all the parts have to reflect socially progressive values for the arc of history to bend towards justice.

Third, there is reason to believe that even if all institutions were procedurally neutral, there would eventually be social equality. This has to do with the mathematically bulletproof but often ignored phenomenon of regression towards the mean. When values are sampled from a process at random, their average will approach the mean of the distribution as more values are accumulated. In terms of the allocation of resources in a population, there is some random variation in the way resources flow. When institutions are fair, inequality in resource allocation will settle into an unbiased distribution. While their may continue to be some apparent inequality due to disorganized heavy tail effects, these will not be biased, in a political sense.

Fourth, there is the problem of political backlash. Whenever political institutions are weak enough to be modified towards what is purported to be a ‘substantive’ or outside view neutrality, that will always be because some political coalition has attained enough power to swing the pendulum in their favor. The more explicit they are about doing this, the more it will mobilize the enemies of this coallition to try to swing the pendulum back the other way. The result is war by other means, the outcome of which will never be fair, because in war there are many who wind up dead or injured.

I am arguing for a centrist position on these matters, one that favors procedural neutrality in most institutions. This is not because I don’t care about substantive, “outside view” inequality. On the contrary, it’s because I believe that partisan bickering that explicitly undermines the inside neutrality of institutions undermines substantive equality. Partisan bickering over the scraps within narrow institutional frames is a distraction from, for example, the way the most wealthy avoid taxes while the middle class pays even more. There is a reason why political propaganda that induces partisan divisions is a weapon. Agreement about procedural neutrality is a core part of civic unity that allows for collective action against the very most abusively powerful.

References

Zachary C. Lipton, Alexandra Chouldechova, Julian McAuley. “Does mitigating ML’s disparate impact require disparate treatment?” 2017

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March 31, 2015

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 Piketty’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 Piketty, 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 Piketty places on this process, he treats it hardly at all in his book.

Whether or not you buy Piketty’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 Piketty’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 Piketty’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.

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