↳ Algorithmic_decisionmaking

October 7th, 2019

↳ Algorithmic_decisionmaking

The Balloon

DIFFERENCE ENGINE

Labor and mechanized calculation

Breathless media coverage of machine learning tools and their applications often obscures the processes that allow them to function. Time and again, services billed or understood by users as automatic are revealed to rely on undervalued, deskilled human labor.

There is rich historical precedent for the presence of these "ghosts in the machine." In a 2017 lecture, Director Emirata of the Max Planck Institute for the History of Science LORRAINE DASTON examines the emergence of mechanical calculation, revealing a fascinating history of the interaction between new technologies and the methods of routinizing and dividing intellectual labor that emerges alongside them.

From the introduction:

"The intertwined histories of the division of labor and mechanical intelligence neither began nor ended with this famous three-act story from pins to computers via logarithms. Long before Prony thought of applying Adam Smith’s political economy to monumental calculation projects, astronomical observatories and nautical almanacs were confronted with mountains of computations that they accomplished by the ingenious organization of work and workers. What mechanization did change was the organization of Big Calculation: integrating humans and machines dictated different algorithms, different skills, different personnel, and above all different divisions of labor. These changes in turn shaped new forms of intelligence at the interface between humans and machines."

Link to the paper version of the lecture. (And stay tuned to the Phenomenal World for our upcoming interview with Daston.)

  • A 1994 paper by Daston entitled "Enlightenment Calculations" gives specific attention to the logarithmic tables of Gaspard de Prony, which sought to demonstrate the usefulness of the newly-invented metric system: "The tables marked an epoch in the history of calculation but also one in the history of intelligence and work." Link.
  • Matthew L. Jones, an historian at Columbia University, studies the history of calculation and computing. His 2016 book Reckoning with Matter: Calculating Machines, Innovation, and Thinking about Thinking from Pascal to Babbage traces the history of attempts to routinize, mechanize and apply the power of calculation. Link to the book, link to Lorraine Daston's review in Critical Inquiry.
  • Simon Schaffer's 1996 paper on the relationship between Charles Babbage's calculating engine and the contemporaneously emerging factory system. Link.
  • A syllabus prepared by Mary L. Gray and Siddharth Suri, authors of Ghost Work—a book about the "hidden" labor force behind many tech services—surveys the tech platform subcontracting labor market. Link.
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September 9th, 2019

Original & Forgery

MULTIPLY EFFECT

The difficulties of causal reasoning and race

While the thorny ethical questions dogging the development and implementation of algorithmic decision systems touch on all manner of social phenomena, arguably the most widely discussed is that of racial discrimination. The watershed moment for the algorithmic ethics conversation was ProPublica's 2016 article on the COMPAS risk-scoring algorithm, and a huge number of ensuing papers in computer science, law, and related disciplines attempt to grapple with the question of algorithmic fairness by thinking through the role of race and discrimination in decision systems.

In a paper from earlier this year, ISSA KOHLER-HAUSMAN of Yale Law School examines the way that race and racial discrimination are conceived of in law and the social sciences. Challenging the premises of an array of research across disciplines, Kolher-Hausmann argues for both a reassessment of the basis of reasoning about discrimination, and a new approach grounded in a social constructivist view of race.

From the paper:

"This Article argues that animating the most common approaches to detecting discrimination in both law and social science is a model of discrimination that is, well, wrong. I term this model the 'counterfactual causal model' of race discrimination. Discrimination, on this account, is detected by measuring the 'treatment effect of race,' where treatment is conceptualized as manipulating the raced status of otherwise identical units (e.g., a person, a neighborhood, a school). Discrimination is present when an adverse outcome occurs in the world in which a unit is 'treated' by being raced—for example, black—and not in the world in which the otherwise identical unit is 'treated' by being, for example, raced white. The counterfactual model has the allure of precision and the security of seemingly obvious divisions or natural facts.

Currently, many courts, experts, and commentators approach detecting discrimination as an exercise measuring the counterfactual causal effect of race-qua-treatment, looking for complex methods to strip away confounding variables to get at a solid state of race and race alone. But what we are arguing about when we argue about whether or not statistical evidence provides proof of discrimination is precisely what we mean by the concept DISCRIMINATION."

Link to the article. And stay tuned for a forthcoming post on the Phenomenal World by JFI fellow Lily Hu that grapples with these themes.

  • For an example of the logic Kohler-Hausmann is writing against, see Edmund S. Phelps' 1972 paper "The Statistical Theory of Racism and Sexism." Link.
  • A recent paper deals with the issue of causal reasoning in an epidemiological study: "If causation must be defined by intervention, and interventions on race and the whole of SeS are vague or impractical, how is one to frame discussions of causation as they relate to this and other vital issues?" Link.
  • From Kohler-Hausmann's footnotes, two excellent works informing her approach: first, the canonical book Racecraft by Karen Fields and Barbara Fields; second, a 2000 article by Tukufu Zuberi, "Decracializing Social Statistics: Problems in the Quantification of Race." Link to the first, link to the second.
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