By looking at the politics of classification within machine learning systems, this article demonstrates why the automated interpretation of images is an inherently social and political project. We begin by asking what work images do in computer vision systems, and what is meant by the claim that computers can “recognize” an image? Next, we look at the method for introducing images into computer systems and look at how taxonomies order the foundational concepts that will determine how a system interprets the (...) world. Then we turn to the question of labeling: how humans tell computers which words will relate to a given image. What is at stake in the way AI systems use these labels to classify humans, including by race, gender, emotions, ability, sexuality, and personality? Finally, we turn to the purposes that computer vision is meant to serve in our society—the judgments, choices, and consequences of providing computers with these capacities. Methodologically, we call this an archeology of datasets: studying the material layers of training images and labels, cataloguing the principles and values by which taxonomies are constructed, and analyzing how these taxonomies create the parameters of intelligibility for an AI system. By doing this, we can critically engage with the underlying politics and values of a system, and analyze which normative patterns of life are assumed, supported, and reproduced. (shrink)

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the (...) primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce. (shrink)

This paper explores how political theory may help us map algorithmic logics against different visions of the political. Drawing on Chantal Mouffe’s theories of agonistic pluralism, this paper depicts algorithms in public life in ten distinct scenes, in order to ask the question, what kinds of politics do they instantiate? Algorithms are working within highly contested online spaces of public discourse, such as YouTube and Facebook, where incompatible perspectives coexist. Yet algorithms are designed to produce clear “winners” from information contests, (...) often with little visibility or accountability for how those contests are designed. In isolation, many of these algorithms seem the opposite of agonistic: much of the complexity of search, ranking, and recommendation algorithms is nonnegotiable and kept far from view, inside an algorithmic “black box.” But what if we widen our perspective? This paper suggests agonistic pluralism as both a design ideal for engineers and a provocation to understand algorithms in a broader social context: rather than focusing on the calculations in isolation, we need to account for the spaces of contestation where they operate. (shrink)

This essay details the resurgence of wellness program as employed by large corporations with the aim of reducing healthcare costs. The essay narrows in on a discussion of how Big Data collection practices are being utilized in wellness programs and the potential negative impact on the worker in regards to privacy and employment discrimination. The essay offers an ethical framework to be adopted by wellness program vendors in order to conduct wellness programs that would achieve cost-saving goals without undue burdens (...) on the worker. The essay also offers some innovative approaches to wellness that may well better serve the goals of healthcare cost reduction. (shrink)

‘@AP: Breaking: Two Explosions in the White House and Barack Obama is injured’. So read a tweet sent from a hacked Associated Press Twitter account @AP, which affected financial markets, wiping out $136.5 billion of the Standard & Poor’s 500 Index’s value. While the speed of the Associated Press hack crash event and the proprietary nature of the algorithms involved make it difficult to make causal claims about the relationship between social media and trading algorithms, we argue that it helps (...) us to critically examine the volatile connections between social media, financial markets, and third parties offering human and algorithmic analysis. By analyzing the commentaries of this event, we highlight two particular currents: one formed by computational processes that mine and analyze Twitter data, and the other being financial algorithms that make automated trades and steer the stock market. We build on sociology of finance together with media theory and focus on the work of Christian Marazzi, Gabriel Tarde and Tony Sampson to analyze the relationship between social media and financial markets. We argue that Twitter and social media are becoming more powerful forces, not just because they connect people or generate new modes of participation, but because they are connecting human communicative spaces to automated computational spaces in ways that are affectively contagious and highly volatile. (shrink)