In this classic text, Kant sets out to articulate and defend the Categorical Imperative - the fundamental principle that underlies moral reasoning - and to lay the foundation for a comprehensive account of justice and human virtues. This new edition and translation of Kant's work is designed especially for students. An extensive and comprehensive introduction explains the central concepts of Groundwork and looks at Kant's main lines of argument. Detailed notes aim to clarify Kant's thoughts and to correct some common (...) misunderstandings of his doctrines. (shrink)

Immanuel Kant's Groundwork of the Metaphysics of Morals ranks alongside Plato's Republic and Aristotle's Nicomachean Ethics as one of the most profound and influential works in moral philosophy ever written. In Kant's own words its aim is to search for and establish the supreme principle of morality, the categorical imperative. Kant argues that every human being is an end in himself or herself, never to be used as a means by others, and that moral obligation is an expression of the (...) human capacity for autonomy or self-government. This edition presents the acclaimed translation of the text by Mary Gregor, together with an introduction by Christine M. Korsgaard that examines and explains Kant's argument. (shrink)

From the book: What is to be done with political ecology? Nothing. What is to be done? Political ecology!

In this book Bruno Latour brings together these different approaches to provide a lively and challenging analysis of science, demonstrating how social context..

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.

Artificial Intelligence in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly (...) invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence. (shrink)

Various Value-Conscious Design frameworks have recently emerged to introduce moral and ethical intelligence into business and technical design contexts, with the goal of proactively influencing the design of technologies to account for moral and ethical values during the conception and design process. Two attempts to insert ethical intelligence into technical design communities to influence the design of technologies in ethical- and value-conscious ways are described, revealing discouraging results. Learning from these failed attempts, the article identifies three key challenges of pragmatic (...) engagement with technical design communities: confronting competing values; identifying the role of the values advocate; and the justification of a value framework. Addressing these challenges must become a priority if one is to be successful in pragmatically engaging with real-world business and design contexts to bring moral and ethical intelligence to bear in the design of emerging information and communication technologies. (shrink)

This trenchant study analyzes the rise and decline in the quality and format of science in America since World War II. Science-Mart attributes this decline to a powerful neoliberal ideology in the 1980s which saw the fruits of scientific investigation as commodities that could be monetized, rather than as a public good.

This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems.” The technical normativity manifests itself through the lineage of technical objects. The norm expresses a technical scheme’s becoming as it mutates through, but also resists, inventions. The genealogy of neural networks shall provide a powerful illustration of this dynamic by engaging with their concrete functioning as well as their unsuspected potentialities. The socio-technical normativity accounts for the manners in (...) which engineers, as actors folded into socio-technical networks, willingly or unwittingly, infuse technical objects with values materialized in the system. Surveillance systems’ design will serve here to instantiate the ongoing mediation through which algorithmic systems are endowed with specific capacities. The behavioral normativity is the normative activity, in which both organic and mechanical behaviors are actively participating, undoing the identification of machines with “norm following,” and organisms with “norminstitution”. This proposition productively accounts for the singularity of machine learning algorithms, explored here through the case of recommender systems. The paper will provide substantial discussions of the notions of “normative” by cutting across history and philosophy of science, legal, and critical theory, as well as “algorithmics,” and by confronting our studies led in engineering laboratories with critical algorithm studies. (shrink)