The confession quoted above by way of introduction reveals with tragic sincerity the fatal passion of an overly avid reader, unlimited in curiosity certainly but fully conscious of the demanding finality of the work he had to accomplish: the scientific critique of an international system of social organization, “in which man is a humiliated, enslaved, abandoned and scornful being” (1844). Cultivating poetry and philosophy in a world felt to be unlivable meant becoming an accomplice of those individuals and institutions principally (...) responsible for the barbarity called modern civilization. But to combat this, it was necessary to scour the murky historical horizon issued from a distant past where only great social revolutions marked the stages of a progression seen as ambiguous because realized by means of unspeakable phenomena of regression. Only one revolution took place under the contradictory sign of an emancipation with universal ambitions and of a decline with indelible consequences: the French Revolution. Conclusion as well as beginning, it was studied by Marx as a unique event, both in relation to its antecedents as well as its liberating promises. Although it is true that Marx “never wrote a history of the ancien régime”, a hardly significant remark, it is no less true that he studied and compiled, with the eagerness of a schoolboy enthralled by history, an enormous mass of documentation that can “help understand how (the ancien regime) gave birth to the Revolution”, despite what François Furet might think (Marx et la Révolution française, Paris, 1986, p. 79). Moreover, the author of Das Kapital drew from the revolutionary history of France the inspiration for a “poetry of the future” in which the following vision was designed:“The old bourgeois society, with its classes and its class antagonism, gave way to an association in which the free development of each individual is the condition for the free development of the whole” (1848). (shrink)

Cette édition qui réunit l'ensemble des textes philosophiques écrits par Marx jusqu'en 1848 permettra de redécouvrir sa critique de l'aliénation de la conscience, de la réification du travail, du culte de l'Etat, des abstractions de la philosophie du droit.

Many military organizations use the code, ?Leave no one behind?. This phrase creates a deep individual commitment among fighters which will, in turn, strengthen the fighting spirit and morale of a unit. It helps to assure the families of the fighters that their relative will not be left behind?alive or dead, they will be brought home. But this code also places a heavy moral burden on the Commanding Officer. He or she must ask: How many healthy fighters will I risk (...) to bring home one wounded fighter or a body? This becomes one of the most difficult moral decisions of Command; losing an unknown number of lives of your people to uphold the important code, ?Leave no one behind?. This case is a peace-time scenario, which probably occurs much more frequently than the war-time scenario, and has the exact same moral decision at its core. (shrink)

Psychometrics firms such as Cambridge Analytica (CA) and troll factories such as the Internet Research Agency (IRA) have had a significant effect on democratic politics, through narrow targeting of political advertising (CA) and concerted disinformation campaigns on social media (IRA) (U.S. Department of Justice 2019; Select Committee on Intelligence, United States Senate 2019; DiResta et al. 2019). It is natural to think that such activities manipulate individuals and, hence, are wrong. Yet, as some recent cases illustrate, the moral concerns with (...) these activities cannot be reduced simply to the effects they have on individuals. Rather, we will argue, the wrongness of these activities relates to the threats they present to the legitimacy of political orders. This occurs primarily through a mechanism we call “emergent manipulation,” rather than through the sort of manipulation that involves specific individuals. (shrink)

New media (highly interactive digital technology for creating, sharing, and consuming information) affords users a great deal of control over their informational diets. As a result, many users of new media unwittingly encapsulate themselves in epistemic bubbles (epistemic structures, such as highly personalized news feeds, that leave relevant sources of information out (Nguyen forthcoming)). Epistemically paternalistic alterations to new media technologies could be made to pop at least some epistemic bubbles. We examine one such alteration that Facebook has made in (...) an effort to fight fake news and conclude that it is morally permissible. We further argue that many epistemically paternalistic policies can (and should) be a perennial part of the internet information environment. (shrink)

This paper examines the practice of using predictive systems to lengthen the prison sentences of convicted persons when the systems forecast a higher likelihood of re-offense or re-arrest. There has been much critical discussion of technologies used for sentencing, including questions of bias and opacity. However, there hasn’t been a discussion of whether this use of predictive systems makes sense in the first place. We argue that it does not by showing that there is no plausible theory of punishment that (...) supports it. (shrink)

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning (...) analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these (...) case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

When agents insert technological systems into their decision-making processes, they can obscure moral responsibility for the results. This can give rise to a distinct moral wrong, which we call “agency laundering.” At root, agency laundering involves obfuscating one’s moral responsibility by enlisting a technology or process to take some action and letting it forestall others from demanding an account for bad outcomes that result. We argue that the concept of agency laundering helps in understanding important moral problems in a number (...) of recent cases involving automated, or algorithmic, decision-systems. We apply our conception of agency laundering to a series of examples, including Facebook’s automated advertising suggestions, Uber’s driver interfaces, algorithmic evaluation of K-12 teachers, and risk assessment in criminal sentencing. We distinguish agency laundering from several other critiques of information technology, including the so-called “responsibility gap,” “bias laundering,” and masking. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in (...) which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). -/- In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to exercise (...) power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. -/- The link below is to an open-access copy of the chapter. (shrink)

Abstract Many political theorists have argued that religious reasons should play a rather limited role in public or political settings. So, for example, according to the Doctrine of Religious Restraint, citizens and legislators ought not allow religious reasons to play a decisive role in justifying public policies. Many military professionals seem to believe that some version of that doctrine applies in military settings, that is, that military professionals should not allow their religious convictions to determine how they exercise command authority. (...) We explain why the Doctrine of Religious Restraint should not apply to military professionals and articulate an alternative understanding of the justificatory role legitimately played by religious considerations in military settings. This alternative understanding assumes that military professionals will exercise command authority with tact, wisdom, and due respect for both the spirit and letter of the law, even when they are guided by robust faith commitments. (shrink)

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment (...) and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. -/- We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods. (shrink)

Questions of privacy have become particularly salient in recent years due, in part, to information-gathering initiatives precipitated by the 2001 World Trade Center attacks, increasing power of surveillance and computing technologies, and massive data collection about individuals for commercial purposes. While privacy is not new to the philosophical and legal literature, there is much to say about the nature and value of privacy. My focus here is on the nature of informational privacy. I argue that the predominant accounts of privacy (...) are unsatisfactory and offer an alternative: for a person to have informational privacy is for there to be limits on the particularized judgments that others are able to reasonably make about that person. (shrink)

Church's thesis, that all reasonable definitions of “computability” are equivalent, is not usually thought of in terms of computability by acontinuouscomputer, of which the general-purpose analog computer (GPAC) is a prototype. Here we prove, under a hypothesis of determinism, that the analytic outputs of aC∞GPAC are computable by a digital computer.In [POE, Theorems 5, 6, 7, and 8], Pour-El obtained some related results. (The proof there of Theorem 7 depends on her Theorem 2, for which the proof in [POE] is (...) incorrect, but for which a correct proof is given in [LIR]. Also, the proof in [POE] of Theorem 8 depends on the unproved assertion that a solution of an algebraic differential equation must be analytic on an open subset of its domain. However, this assertion was later proved in [BRR].) As in [POE], we reduce the problem to a problem about solutions of certain systems of algebraic differential equations (ADE's). If such a system is nonsingular (i.e. if the “separant” does not vanish along the given solution), then the argument is very easy (see [VSD] for an even simpler situation), so that the essential difficulties arise fromsingularsystems. Our main tools in handling these difficulties are drawn from the excellent (and difficult) paper [DEL] by Denef and Lipshitz. The author especially wants to thank Leonard Lipshitz for his kind help in the preparation of the present paper.We emphasize here that our proof of the simulation result applies only to the GPAC as described below. The GPAC's form a natural subclass of the class of all analog computers, and are based on certain idealized components (“black boxes”), mostly associated with the technology of past decades. One can easily envisage other kinds of black boxes of an input-output character that would lead to different kinds of analog computers. (For example, one could incorporate delays, or spatial integrators in addition to the present temporal integrators, etc.) Whether digital simulation is possible for these “extended” analog computers poses a rich and challenging set of research questions. (shrink)

Neural engineers and clinicians are starting to translate advances in electrodes, neural computation, and signal processing into clinically useful devices to allow control of wheelchairs, spellers, prostheses, and other devices. In the process, large amounts of brain data are being generated from participants, including intracortical, subdural and extracranial sources. Brain data is a vital resource for BCI research but there are concerns about whether the collection and use of this data generates risk to privacy. Further, the nature of BCI research (...) involves understanding and making inferences about device users’ mental states, thoughts, and intentions. This, too, raises privacy concerns by providing otherwise unavailable direct or privileged access to individuals mental lives. And BCI-controlled prostheses may change the way clinical care is provided and the type of physical access caregivers have to patients. This, too, has important privacy implications. I In this chapter we examine several of these privacy concerns in light of prominent views of the nature and value of privacy. We argue that increased scrutiny needs to be paid to privacy concerns arising from Big Data and decoding of mental states, but that BCI research may also provide opportunity for individuals to enhance their privacy. (shrink)

Advancements in novel neurotechnologies, such as brain computer interfaces and neuromodulatory devices such as deep brain stimulators, will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make (...) recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, new measures, including “Neurorights,” for data privacy, security, and consent to empower neurotechnology users’ control over their data, new methods of identifying and preventing bias, and the adoption of public guidelines for safe and equitable distribution of neurotechnological devices. (shrink)

Civil liberty and privacy advocates have criticized the USA PATRIOT Act (Act) on numerous grounds since it was passed in the wake of the World Trade Center attacks in 2001. Two of the primary targets of those criticisms are the Act’s sneak-and-peek search provision, which allows law enforcement agents to conduct searches without informing the search’s subjects, and the business records provision, which allows agents to secretly subpoena a variety of information – most notoriously, library borrowing records. Without attending to (...) all of the ways that critics claim the Act burdens privacy, I examine whether those two controversial parts of the Act, the section 213 sneak-and-peak search and the section 215 business records gag-rule provisions, burden privacy as critics charge. I begin by describing the two provisions. Next, I explain why those provisions don’t burden privacy on standard philosophical accounts. Moreover, I argue that they need not conflict with the justifications for people’s claims to privacy, nor do they undermine the value of privacy on the standard accounts. However, rather than simply concluding that the sections don’t burden privacy, I argue that those provisions are problematic on the grounds that they undermine the value of whatever rights to privacy people have. Specifically, I argue that it is important to distinguish rights themselves from the value that those rights have to the rights-holders, and that an essential element of privacy rights having value is that privacy right-holders be able to tell the extent to which they actually have privacy. This element, which is justified by the right-holders’ autonomy interests, is harmed by the two provisions. (shrink)

The current debate about labeling genetically engineered (GE) food focuses on food derived from GE crops, neglecting food derived from GE animals. This is not surprising, as GE animal products have not yet reached the market. Participants in the debate may also be assuming that conclusions about GE crops automatically extend to GE animals. But there are two GE animals - the Enviropig and the AquAdvantage Bred salmon - that are approaching the market, animals raise more ethical issues than plants, (...) and U.S. regulations treat animal products differently from crops. This paper therefore examines the specific question of whether there should be mandatory labeling on all food products derived from GE animals. We examine the likely regulatory pathways, salient differences between GE animals and GE crops, and relevant social science research on consumers’ attitudes. We argue that on any of the likely pathways, the relevant agency has a democratic obligation to require labeling for all GE animal food products. (shrink)

In her recent article, Does autonomy count in favor of labeling genetically modified food?, Kirsten Hansen argues that in Europe, voluntary negative labeling of non-GM foods respects consumer autonomy just as well as mandatory positive labeling of foods with GM content. She also argues that because negative labeling places labeling costs upon those consumers that want to know whether food is GM, negative labeling is better policy than positive labeling. In this paper, we argue that Hansens arguments are mistaken in (...) several respects. Most importantly, she underestimates the demands of respecting autonomy and overestimates the cost of positive labeling. Moreover, she mistakenly implies that only a small minority of people desire information about GM content. We also explore the extent to which her arguments would apply to the US context, and argue that any discussion of the relationship between autonomy and labeling should include not just considerations of consumer autonomy but also considerations of what we call citizen autonomy. (shrink)

This paper has two aims. The first is to explain a type of wrong that arises when agents obscure responsibility for their actions. Call it “agency laundering.” The second is to use the concept of agency laundering to understand the underlying moral issues in a number of recent cases involving algorithmic decision systems. From the Proceedings of the 14th International Conference, iConference 2019, Washington D.C., March 31-April 3, 2019.

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is (...) students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

Surveillance plays a crucial role in public health, and for obvious reasons conflicts with individual privacy. This paper argues that the predominant approach to the conflict is problematic, and then offers an alternative. It outlines a Basic Interests Approach to public health measures, and the Unreasonable Exercise Argument, which sets forth conditions under which individuals may justifiably exercise individual privacy claims that conflict with public health goals. The view articulated is compatible with a broad range conceptions of the value of (...) health. (shrink)