The Moral Consideration of Artificial Entities: a Literature Review
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Man As 'Aggregate of Data'
AI & SOCIETY https://doi.org/10.1007/s00146-018-0852-6 OPEN FORUM Man as ‘aggregate of data’ What computers shouldn’t do Sjoukje van der Meulen1 · Max Bruinsma2 Received: 4 October 2017 / Accepted: 10 June 2018 © The Author(s) 2018 Abstract Since the emergence of the innovative field of artificial intelligence (AI) in the 1960s, the late Hubert Dreyfus insisted on the ontological distinction between man and machine, human and artificial intelligence. In the different editions of his clas- sic and influential book What computers can’t do (1972), he posits that an algorithmic machine can never fully simulate the complex functioning of the human mind—not now, nor in the future. Dreyfus’ categorical distinctions between man and machine are still relevant today, but their relation has become more complex in our increasingly data-driven society. We, humans, are continuously immersed within a technological universe, while at the same time ubiquitous computing, in the words of computer scientist Mark Weiser, “forces computers to live out here in the world with people” (De Souza e Silva in Interfaces of hybrid spaces. In: Kavoori AP, Arceneaux N (eds) The cell phone reader. Peter Lang Publishing, New York, 2006, p 20). Dreyfus’ ideas are therefore challenged by thinkers such as Weiser, Kevin Kelly, Bruno Latour, Philip Agre, and Peter Paul Verbeek, who all argue that humans are much more intrinsically linked to machines than the original dichotomy suggests—they have evolved in concert. Through a discussion of the classical concepts of individuum and ‘authenticity’ within Western civilization, this paper argues that within the ever-expanding data-sphere of the twenty-first century, a new concept of man as ‘aggregate of data’ has emerged, which further erodes and undermines the categorical distinction between man and machine. -
WhatCanNuclearPowerTeachUs
What can nuclear power teach us about the institutional adoption of clean meat? December 28, 2017 Author: J. Mohorčich © 2017 Sentience Institute Edited by Jacy Reese. External review pending. Abstract Studies on clean meat adoption have mostly focused on consumer acceptance, but institutional choices by governments, industries, and news media can also delay or accelerate the adoption of new technologies. This report examines the factors that contributed to nuclear power’s widespread adoption in France and applies those findings to the question of how to advance the adoption of clean meat. Among other conclusions, this report finds that supply constraints on a competing good can accelerate the adoption of a new technology, that technical explanations about why a new product is safe are likely to backfire, that safety incidents that appear to confirm preexisting concerns are especially damaging to a new technology, and that states reliant on imports to meet their needs for a good or service are more promising targets -
Communication and Artificial Intelligence: Opportunities and Challenges for the 21St Century David J
communication +1 Volume 1 Article 1 Issue 1 Futures of Communication August 2012 Communication and Artificial Intelligence: Opportunities and Challenges for the 21st Century David J. Gunkel Northern Illinois University, [email protected] Abstract This essay advocates for a significant reorientation and reconceptualization of communication studies in order to accommodate the opportunities and challenges introduced by increasingly intelligent machines, autonomous decision making systems, and smart devices. Historically the discipline of communication has accommodated new technology by transforming these innovations into a medium of human interaction and message exchange. With the computer, this transaction is particularly evident with the development of computer-mediated communication (CMC) in the later half of the 20th century. In CMC, the computer is understood and investigated as a more-or-less neutral channel of message transfer and instrument of human interaction. This formalization, although not necessarily incorrect, neglects the fact that the computer, unlike previous technological advancements, also occupies the position of participant in communicative exchanges. Evidence of this is already available in the science of AI and has been explicitly described by some of the earliest writings on communication and the computer. The essay therefore 1) demonstrates that the CMC paradigm, although undeniably influential and successful, is insufficient and no longer tenable and 2) argues that communication studies needs to rework its basic framework in order to address and respond to the unique technological challenges and opportunities of the 21st century. Keywords Communication, Artificial Intelligence, Alan Turing, Media Studies, Computer Mediated Communication, Computers Gunkel / Communication and AI Introduction In a now well-known and often reproduced New Yorker cartoon by Peter Steiner, two dogs sit in front of an Internet-connected computer. -
Blame, What Is It Good For?
Blame, What is it Good For? Gordon Briggs1 Abstract— Blame is an vital social and cognitive mechanism [6] and reside in a much more ambiguous moral territory. that humans utilize in their interactions with other agents. In Additionally, it is not enough to be able to correctly reason this paper, we discuss how blame-reasoning mechanisms are about moral scenarios to ensure ethical or otherwise desirable needed to enable future social robots to: (1) appropriately adapt behavior in the context of repeated and/or long-term outcomes from human-robot interactions, the robot must interactions and relationships with other social agents; (2) avoid have other social competencies that support this reasoning behaviors that are perceived to be rude due to inappropriate mechanism. Deceptive interaction partners or incorrect per- and unintentional connotations of blame; and (3) avoid behav- ceptions/predictions about morally-charged scenarios may iors that could damage long-term, working relationships with lead even a perfect ethical-reasoner to make choices with other social agents. We also discuss how current computational models of blame and other relevant competencies (e.g. natural unethical outcomes [7]. What these challenges stress is the language generation) are currently insufficient to address these need to look beyond the ability to simply generate the proper concerns. Future work is necessary to increase the social answer to a moral dilemma in theory, and to consider the reasoning capabilities of artificially intelligent agents to achieve social mechanisms needed in practice. these goals. One such social mechanism that others have posited as I. INTRODUCTION being necessary to the construction of a artificial moral agent is blame [8]. -
On How to Build a Moral Machine
On How to Build a Moral Machine Paul Bello [email protected] Human & Bioengineered Systems Division - Code 341, Office of Naval Research, 875 N. Randolph St., Arlington, VA 22203 USA Selmer Bringsjord [email protected] Depts. of Cognitive Science, Computer Science & the Lally School of Management, Rensselaer Polytechnic Institute, Troy, NY 12180 USA Abstract Herein we make a plea to machine ethicists for the inclusion of constraints on their theories con- sistent with empirical data on human moral cognition. As philosophers, we clearly lack widely accepted solutions to issues regarding the existence of free will, the nature of persons and firm conditions on moral agency/patienthood; all of which are indispensable concepts to be deployed by any machine able to make moral judgments. No agreement seems forthcoming on these mat- ters, and we don’t hold out hope for machines that can both always do the right thing (on some general ethic) and produce explanations for its behavior that would be understandable to a human confederate. Our tentative solution involves understanding the folk concepts associated with our moral intuitions regarding these matters, and how they might be dependent upon the nature of hu- man cognitive architecture. It is in this spirit that we begin to explore the complexities inherent in human moral judgment via computational theories of the human cognitive architecture, rather than under the extreme constraints imposed by rational-actor models assumed throughout much of the literature on philosophical ethics. After discussing the various advantages and challenges of taking this particular perspective on the development of artificial moral agents, we computationally explore a case study of human intuitions about the self and causal responsibility. -
The Effects of Animal-Free Food Technology Awareness on Animal Farming Opposition
1 The Effects of Animal-Free Food Technology Awareness on Animal Farming Opposition Jamie Harris Researcher May 14, 2020 © 2019 Sentience Institute Edited by Jacy Reese Anthis. Many thanks to Matti Wilks, Chris Bryant, Jacob Peacock, Pegah Maham, Tom Beggs, Marta Wronska, and Jamie Parry for reviewing and providing feedback. Abstract There is limited research on the effects of animal-free food technologies (AFFT), such as cultured meat and new plant-based foods that accurately mimic animal products, on attitudes toward animal farming. This study found that providing participants with information about AFFT significantly lowered animal farming opposition (AFO) relative to participants who were provided with information about low-technology plant-based foods or about an unrelated topic. The results suggest that including information about AFFT in advocacy materials can be detrimental to attitudinal change. Information about low-technology plant-based foods had no significant effects on AFO relative to information about an unrelated topic, so does not seem to have this downside. The Effects of Animal-Free Food Technology Awareness on Animal Farming Opposition Jamie Harris | Sentience Institute | May 14, 2020 2 Table of Contents Abstract 1 Introduction 2 The effects of AFFT on Animal Farming Opposition 3 Comparison to plausible counterfactuals 8 Moderators and correlates 11 Value for further research 13 Methodology 14 Results 17 Exploratory analysis 20 Discussion 21 Appendix 24 Message Texts 24 Guided Track code 27 -
Which Consequentialism? Machine Ethics and Moral Divergence
MIRI MACHINE INTELLIGENCE RESEARCH INSTITUTE Which Consequentialism? Machine Ethics and Moral Divergence Carl Shulman, Henrik Jonsson MIRI Visiting Fellows Nick Tarleton Carnegie Mellon University, MIRI Visiting Fellow Abstract Some researchers in the field of machine ethics have suggested consequentialist or util- itarian theories as organizing principles for Artificial Moral Agents (AMAs) (Wallach, Allen, and Smit 2008) that are ‘full ethical agents’ (Moor 2006), while acknowledging extensive variation among these theories as a serious challenge (Wallach, Allen, and Smit 2008). This paper develops that challenge, beginning with a partial taxonomy ofconse- quentialisms proposed by philosophical ethics. We discuss numerous ‘free variables’ of consequentialism where intuitions conflict about optimal values, and then consider spe- cial problems of human-level AMAs designed to implement a particular ethical theory, by comparison to human proponents of the same explicit principles. In conclusion, we suggest that if machine ethics is to fully succeed, it must draw upon the developing field of moral psychology. Shulman, Carl, Nick Tarleton, and Henrik Jonsson. 2009. “Which Consequentialism? Machine Ethics and Moral Divergence.” In AP-CAP 2009: The Fifth Asia-Pacific Computing and Philosophy Conference, October 1st-2nd, University of Tokyo, Japan, Proceedings, edited by Carson Reynolds and Alvaro Cassinelli, 23–25. AP-CAP 2009. http://ia-cap.org/ap-cap09/proceedings.pdf. This version contains minor changes. Carl Shulman, Henrik Jonsson, Nick Tarleton 1. Free Variables of Consequentialism Suppose that the recommendations of a broadly utilitarian view depend on decisions about ten free binary variables, where we assign a probability of 80% to our favored option for each variable; in this case, if our probabilities are well-calibrated and our errors are not correlated across variables, then we will have only slightly more than a 10% chance of selecting the correct (in some meta-ethical framework) specification. -
Philosophy of Artificial Intelligence
David Gray Grant Philosophy of Artificial Intelligence Note to the reader: this syllabus is for an introductory undergraduate lecture course with no prerequisites. Course Description In this course, we will explore the philosophical implications of artificial intelligence. Could we build machines with minds like ours out of computer chips? Could we cheat death by uploading our minds to a computer server? Are we living in a software-based simulation? Should a driverless car kill one pedestrian to save five? Will artificial intelligence be the end of work (and if so, what should we do about it)? This course is also a general introduction to philosophical problems and methods. We will dis- cuss classic problems in several areas of philosophy, including philosophy of mind, metaphysics, epistemology, and ethics. In the process, you will learn how to engage with philosophical texts, analyze and construct arguments, investigate controversial questions in collaboration with others, and express your views with greater clarity and precision. Contact Information Instructor: David Gray Grant Email: [email protected] Office: 303 Emerson Hall Office hours: Mondays 3:00-5:00 and by appointment Assignments and Grading You must complete all required assignments in order to pass this course. Your grade will be determined as follows: • 10%: Participation • 25%: 4 short written assignments (300-600 words) • 20%: Paper 1 (1200-1500 words) • 25%: Paper 2 (1200-1500 words) • 20%: Final exam Assignments should be submitted in .docx or .pdf format (.docx is preferred), Times New Roman 12 point font, single-spaced. Submissions that exceed the word limits specified above (including footnotes) will not be accepted. -
Scale-Up Economics for Cultured Meat
Scale-Up Economics for Cultured Meat Techno-Economic Analysis and Due Diligence David Humbird DWH Process Consulting LLC Centennial, Colorado USA Prepared for Open Philanthropy San Francisco, California USA December 28 2020 Executive Summary “Cultured meat” technologies aim to replace conventional meat with analogous or alternative bioproducts from animal cell culture. Developers of these technologies claim their products, also known as “cell-based” or “cultivated” meat, will be safer and more environmentally friendly than conventional meat while offering improved farm-animal welfare. To these ends, Open Philanthropy commissioned this assessment of cultured meat’s potential to measurably displace the consumption of conventional meat. Recognizing that the scalability of any cultured-meat products must in turn depend on the scale and process intensity of animal cell production, this study draws on techno-economic analysis and due-diligence perspectives in industrial fermentation and upstream biopharmaceuticals to assess the extent to which animal cell culture could be scaled like a fermentation process. The analysis identifies a number of significant barriers to the scale-up of animal cell culture. Bioreactor design principles indicate a variety of issues associated with bulk cell growth in culture: Low growth rate, metabolic inefficiency, catabolite and CO2 inhibition, and bubble-induced cell damage will all limit practical bioreactor volume and attainable cell density. With existing bioreactor designs and animal cell lines, a significant engineering effort would be required to address even one of these issues. Economic challenges are further examined. Equipment and facilities with adequate microbial contamination safeguards are expected to have high capital costs. Suitable formulations of amino acids and protein growth factors are not currently produced at scales consistent with food production, and their projected costs at scale are likewise high. -
Machine Learning Ethics in the Context of Justice Intuition
SHS Web of Conferences 69, 00150 (2019) https://doi.org/10.1051/shsconf/20196900150 CILDIAH-2019 Machine Learning Ethics in the Context of Justice Intuition Natalia Mamedova1,*, Arkadiy Urintsov1, Nina Komleva1, Olga Staroverova1 and Boris Fedorov1 1Plekhanov Russian University of Economics, 36, Stremyanny lane, 117997, Moscow, Russia Abstract. The article considers the ethics of machine learning in connection with such categories of social philosophy as justice, conviction, value. The ethics of machine learning is presented as a special case of a mathematical model of a dilemma - whether it corresponds to the “learning” algorithm of the intuition of justice or not. It has been established that the use of machine learning for decision making has a prospect only within the limits of the intuition of justice field based on fair algorithms. It is proposed to determine the effectiveness of the decision, considering the ethical component and given ethical restrictions. The cyclical nature of the relationship between the algorithmic algorithms subprocesses in machine learning and the stages of conducting mining analysis projects using the CRISP methodology has been established. The value of ethical constraints for each of the algorithmic processes has been determined. The provisions of the Theory of System Restriction are applied to find a way to measure the effect of ethical restrictions on the “learning” algorithm 1 Introduction Accepting the freedom of thought is authentic intuition of justice of one individual in relation to The intuition of justice is regarded as a sincere, himself and others. But for artificial intelligence emotionally saturated belief in the justice of some (hereinafter - AI) freedom of thought is not supposed - position. -
Why Machine Ethics?
www.computer.org/intelligent Why Machine Ethics? Colin Allen, Indiana University Wendell Wallach, Yale University Iva Smit, E&E Consultants Vol. 21, No. 4 July/August 2006 This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. For more information, please see www.ieee.org/portal/pages/about/documentation/copyright/polilink.html. Machine Ethics Why Machine Ethics? Colin Allen, Indiana University Wendell Wallach, Yale University Iva Smit, E&E Consultants runaway trolley is approaching a fork in the tracks. If the trolley runs on its cur- A rent track, it will kill a work crew of five. If the driver steers the train down the other branch, the trolley will kill a lone worker. If you were driving the trolley, what would you do? What would a computer or robot do? Trolley cases, first introduced by philosopher which decisions must be made? It’s easy to argue Philippa Foot in 19671 and a staple of introductory from a position of ignorance that such a goal is impos- Machine ethics isn’t ethics courses, have multiplied in the past four sible to achieve. -
APA Newsletter on Philosophy and Computers, Vol. 14, No. 2
NEWSLETTER | The American Philosophical Association Philosophy and Computers SPRING 2015 VOLUME 14 | NUMBER 2 FROM THE GUEST EDITOR John P. Sullins NOTES FROM THE COMMUNITY ON PAT SUPPES ARTICLES Patrick Suppes Patrick Suppes Autobiography Luciano Floridi Singularitarians, AItheists, and Why the Problem with Artificial Intelligence is H.A.L. (Humanity At Large), not HAL Peter Boltuc First-Person Consciousness as Hardware D. E. Wittkower Social Media and the Organization Man Niklas Toivakainen The Moral Roots of Conceptual Confusion in Artificial Intelligence Research Xiaohong Wang, Jian Wang, Kun Zhao, and Chaolin Wang Increase or Decrease of Entropy: To Construct a More Universal Macroethics (A Discussion of Luciano Floridi’s The Ethics of Information) VOLUME 14 | NUMBER 2 SPRING 2015 © 2015 BY THE AMERICAN PHILOSOPHICAL ASSOCIATION ISSN 2155-9708 APA NEWSLETTER ON Philosophy and Computers JOHN P. SULLINS, GUEST EDITOR VOLUME 14 | NUMBER 2 | SPRING 2015 but here we wish to celebrate his accomplishments in the FROM THE GUEST EDITOR fields of philosophy and computing one last time. John P. Sullins To accomplish that goal I have compiled some interesting SONOMA STATE UNIVERSITY pieces from an autobiography that Pat wrote some years ago but that he added to a bit for an event held in his honor November 17, 2014, marked the end of an inspiring at Stanford. In this document he explains his motivations career. On that day Patrick Suppes died quietly at the and accomplishments in various fields of study that are age of ninety-two in his house on the Stanford Campus, of interest to our community. In that section you will see which had been his home both physically and intellectually just how ambitious Pat was in the world of computer since 1950.