Achieving Ethical Algorithmic Behaviour in the Internet of Things: a Review
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Artificial Intelligence and the Ethics of Self-Learning Robots Shannon Vallor Santa Clara University, [email protected]
Santa Clara University Scholar Commons Philosophy College of Arts & Sciences 10-3-2017 Artificial Intelligence and the Ethics of Self-learning Robots Shannon Vallor Santa Clara University, [email protected] George A. Bekey Follow this and additional works at: http://scholarcommons.scu.edu/phi Part of the Philosophy Commons Recommended Citation Vallor, S., & Bekey, G. A. (2017). Artificial Intelligence and the Ethics of Self-learning Robots. In P. Lin, K. Abney, & R. Jenkins (Eds.), Robot Ethics 2.0 (pp. 338–353). Oxford University Press. This material was originally published in Robot Ethics 2.0 edited by Patrick Lin, Keith Abney, and Ryan Jenkins, and has been reproduced by permission of Oxford University Press. For permission to reuse this material, please visit http://www.oup.co.uk/academic/rights/permissions. This Book Chapter is brought to you for free and open access by the College of Arts & Sciences at Scholar Commons. It has been accepted for inclusion in Philosophy by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. ARTIFICIAL INTELLIGENCE AND 22 THE ETHICS OF SELF - LEARNING ROBOTS Shannon Va ll or and George A. Bekey The convergence of robotics technology with the science of artificial intelligence (or AI) is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors.1 Recent advances in machine learning techniques have produced significant gains in the ability of artificial agents to perform or even excel in activities for merly thought to be the exclusive province of human intelligence, including abstract problem-solving, perceptual recognition, social interaction, and natural language use. -
An Unconventional Solution for a Sociotechnical
1 RESOLVING THE PROBLEM OF ALGORITHMIC DISSONANCE: AN UNCONVENTIONAL SOLUTION FOR A SOCIOTECHNICAL PROBLEM Shiv Issar | Department of Sociology, University of Wisconsin-Milwaukee | [email protected] June 29, 2021 Abstract In this paper, I propose the concept of “algorithmic dissonance”, which characterizes the inconsistencies that emerge through the fissures that lie between algorithmic systems that utilize system identities, and sociocultural systems of knowledge that interact with them. A product of human-algorithm interaction, algorithmic dissonance builds upon the concepts of algorithmic discrimination and algorithmic awareness, offering greater clarity towards the comprehension of these sociotechnical entanglements. By employing Du Bois’ concept of “double consciousness” and black feminist theory, I argue that all algorithmic dissonance is racialized. Next, I advocate for the use of speculative methodologies and art for the creation of critically informative sociotechnical imaginaries that might serve a basis for the sociological critique and resolution of algorithmic dissonance. Algorithmic dissonance can be an effective check against structural inequities, and of interest to scholars and practitioners concerned with running “algorithm audits”. Keywords: Algorithmic Dissonance, Algorithmic Discrimination, Algorithmic Bias, Algorithm Audits, System Identity, Speculative methodologies. 2 Introduction This paper builds upon Aneesh’s (2015) notion of system identities by utilizing their nature beyond their primary function of serving algorithmic systems and the process of algorithmic decision- making. As Aneesh (2015) describes, system identities are data-bound constructions built by algorithmic systems for the (singular) purpose of being used by algorithmic systems. They are meant to simulate a specific dimension (or a set of dimensions) of personhood , just as algorithmic systems and algorithmic decision-making are meant to simulate the agents of different social institutions, and the decisions that those agents would make. -
An Ethical Framework for Smart Robots Mika Westerlund
An Ethical Framework for Smart Robots Mika Westerlund Never underestimate a droid. Leia Organa Star Wars: The Rise of Skywalker This article focuses on “roboethics” in the age of growing adoption of smart robots, which can now be seen as a new robotic “species”. As autonomous AI systems, they can collaborate with humans and are capable of learning from their operating environment, experiences, and human behaviour feedback in human-machine interaction. This enables smart robots to improve their performance and capabilities. This conceptual article reviews key perspectives to roboethics, as well as establishes a framework to illustrate its main ideas and features. Building on previous literature, roboethics has four major types of implications for smart robots: 1) smart robots as amoral and passive tools, 2) smart robots as recipients of ethical behaviour in society, 3) smart robots as moral and active agents, and 4) smart robots as ethical impact-makers in society. The study contributes to current literature by suggesting that there are two underlying ethical and moral dimensions behind these perspectives, namely the “ethical agency of smart robots” and “object of moral judgment”, as well as what this could look like as smart robots become more widespread in society. The article concludes by suggesting how scientists and smart robot designers can benefit from a framework, discussing the limitations of the present study, and proposing avenues for future research. Introduction capabilities (Lichocki et al., 2011; Petersen, 2007). Hence, Lin et al. (2011) define a “robot” as an Robots are becoming increasingly prevalent in our engineered machine that senses, thinks, and acts, thus daily, social, and professional lives, performing various being able to process information from sensors and work and household tasks, as well as operating other sources, such as an internal set of rules, either driverless vehicles and public transportation systems programmed or learned, that enables the machine to (Leenes et al., 2017). -
Nudging for Good: Robots and the Ethical Appropriateness of Nurturing Empathy and Charitable Behavior
Nudging for Good: Robots and the Ethical Appropriateness of Nurturing Empathy and Charitable Behavior Jason Borenstein* and Ron Arkin** Predictions are being commonly voiced about how robots are going to become an increasingly prominent feature of our day-to-day lives. Beyond the military and industrial sectors, they are in the process of being created to function as butlers, nannies, housekeepers, and even as companions (Wallach and Allen 2009). The design of these robotic technologies and their use in these roles raises numerous ethical issues. Indeed, entire conferences and books are now devoted to the subject (Lin et al. 2014).1 One particular under-examined aspect of human-robot interaction that requires systematic analysis is whether to allow robots to influence a user’s behavior for that person’s own good. However, an even more controversial practice is on the horizon and warrants attention, which is the ethical acceptability of allowing a robot to “nudge” a user’s behavior for the good of society. For the purposes of this paper, we will examine the feasibility of creating robots that would seek to nurture a user’s empathy towards other human beings. We specifically draw attention to whether it would be ethically appropriate for roboticists to pursue this type of design pathway. In our prior work, we examined the ethical aspects of encoding Rawls’ Theory of Social Justice into robots in order to encourage users to act more socially just towards other humans (Borenstein and Arkin 2016). Here, we primarily limit the focus to the performance of charitable acts, which could shed light on a range of socially just actions that a robot could potentially elicit from a user and what the associated ethical concerns may be. -
Algorithmic Awareness
ALGORITHMIC AWARENESS CONVERSATIONS WITH YOUNG CANADIANS ABOUT ARTIFICIAL INTELLIGENCE AND PRIVACY Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy This report can be downloaded from: mediasmarts.ca/research-policy Cite as: Brisson-Boivin, Kara and Samantha McAleese. (2021). “Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy.” MediaSmarts. Ottawa. Written for MediaSmarts by: Kara Brisson-Boivin, PhD Samantha McAleese MediaSmarts 205 Catherine Street, Suite 100 Ottawa, ON Canada K2P 1C3 T: 613-224-7721 F: 613-761-9024 Toll-free line: 1-800-896-3342 [email protected] mediasmarts.ca @mediasmarts This research was made possible by the financial contributions from the Office of the Privacy Commissioner of Canada’s contributions program. Algorithmic Awareness: Conversations with Young Canadians about Artificial Intelligence and Privacy. MediaSmarts © 2021 2 Table of Contents Key Terms .................................................................................................................................................... 4 Introduction ................................................................................................................................................. 6 Algorithms and Artificial Intelligence: ............................................................................................... 9 What We (Don't) Know ......................................................................................................................... -
Algorithm Bias Playbook Presentation
Algorithmic Bias Playbook Ziad Obermeyer June, 2021 Rebecca Nissan Michael Stern Stephanie Eaneff Emily Joy Bembeneck Sendhil Mullainathan ALGORITHMIC BIAS PLAYBOOK Is your organization using biased algorithms? How would you know? What would you do if so? This playbook describes 4 steps your organization can take to answer these questions. It distills insights from our years of applied work helping others diagnose and mitigate bias in live algorithms. Algorithmic bias is everywhere. Our work with dozens of organizations—healthcare providers, insurers, technology companies, and regulators—has taught us that biased algorithms are deployed throughout the healthcare system, influencing clinical care, operational workflows, and policy. This playbook will teach you how to define, measure, and mitigate racial bias in live algorithms. By working through concrete examples—cautionary tales—you’ll learn what bias looks like. You’ll also see reasons for optimism—success stories—that demonstrate how bias can be mitigated, transforming flawed algorithms into tools that fight injustice. Who should read this? We wrote this playbook with three kinds of people in mind. ● C-suite leaders (CTOs, CMOs, CMIOs, etc.): Algorithms may be operating at scale in your organization—but what are they doing? And who is responsible? This playbook will help you think strategically about how algorithms can go wrong, and what your technical teams can do about it. It also lays out oversight structures you can put in place to prevent bias. ● Technical teams working in health care: We’ve found that the difference between biased and unbiased algorithms is often a matter of subtle technical choices. If you build algorithms, this playbook will help you make those choices better. -
Algorithmic Bias on the Implicit Biases of Social Technology
Algorithmic Bias On the Implicit Biases of Social Technology Gabbrielle M Johnson New York University Abstract Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scien- tists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call `the Proxy Prob- lem'. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algo- rithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. 1 Introduction On March 23rd, 2016, Microsoft Corporation released Tay, an artificial intelligence (AI) Twitter chatbot intended to mimic the language patterns of a 19-year-old American girl. Tay operated by learning from human Twitter users with whom it interacted. Only 16 hours after its launch, Tay was shut down for authoring a number of tweets endorsing Nazi ideology and harassing other Twitter users. -
Deep Automation Bias: How to Tackle a Wicked Problem of AI?
big data and cognitive computing Article Deep Automation Bias: How to Tackle a Wicked Problem of AI? Stefan Strauß Institute of Technology Assessment (ITA), Austrian Academy of Sciences, 1030 Vienna, Austria Abstract: The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learning. There is, though, a tendency to fix societal and ethical issues with technical solutions that may cause additional, wicked problems. Alternative analytical approaches are thus needed to avoid this and to comprehend how societal and ethical issues occur in AI systems. Despite various forms of bias, ultimately, risks result from eventual rule conflicts between the AI system behavior due to feature complexity and user practices with limited options for scrutiny. Hence, although different forms of bias can occur, automation is their common ground. The paper highlights the role of automation and explains why deep automation bias (DAB) is a metarisk of AI. Based on former work it elaborates the main influencing factors and develops a heuristic model for assessing DAB-related risks in AI systems. This model aims at raising problem awareness and training on the sociotechnical risks resulting from AI-based automation and contributes to improving the general explicability of AI systems beyond technical issues. Keywords: artificial intelligence; machine learning; automation bias; fairness; transparency; account- ability; explicability; uncertainty; human-in-the-loop; awareness raising Citation: Strauß, S. Deep Automation Bias: How to Tackle a 1. Introduction Wicked Problem of AI? Big Data Cogn. -
An Architecture for Ethical Robots
1 An architecture for ethical robots Dieter Vanderelst & Alan Winfield Bristol Robotics Laboratory, University of the West of England T Block, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, United Kingdom Abstract—Robots are becoming ever more autonomous. This Anderson [2] and our previous work [27] are the only instances expanding ability to take unsupervised decisions renders it im- of robots having been equipped with a set of moral principles. perative that mechanisms are in place to guarantee the safety of So far, most work has been either theoretical [e.g., 25] or behaviours executed by the robot. Moreover, smart autonomous robots should be more than safe; they should also be explicitly simulation based [e.g., 3]. ethical – able to both choose and justify actions that prevent The approach taken by Anderson and Anderson [1, 2] and harm. Indeed, as the cognitive, perceptual and motor capabilities others [25, 3] is complementary with our research goals. These of robots expand, they will be expected to have an improved authors focus on developing methods to extract ethical rules capacity for making moral judgements. We present a control for robots. Conversely, our work concerns the development architecture that supplements existing robot controllers. This so-called Ethical Layer ensures robots behave according to a of a control architecture that supplements the existing robot predetermined set of ethical rules by predicting the outcomes of controller, ensuring robots behave according to a predeter- possible actions and evaluating the predicted outcomes against mined set of ethical rules [27, 26]. In other words, we are those rules. To validate the proposed architecture, we implement concerned with methods to enforce the rules once these have it on a humanoid robot so that it behaves according to Asimov’s been established [10]. -
Review Into Bias in Algorithmic Decision-Making
Review into bias in algorithmic decision-making November 2020 Review into bias in algorithmic decision-making: Contents Contents Preface 3 By the Board of the Centre for Data Ethics and Innovation Executive summary 5 Part I: Introduction 14 1. Background and scope 15 2. The issue 20 Part II: Sector reviews 37 3. Recruitment 39 4. Financial services 48 5. Policing 61 6. Local government 72 Part III: Addressing the challenges 80 7. Enabling fair innovation 83 8. The regulatory environment 108 9. Transparency in the public sector 130 10. Next steps and future challenges 144 11. Acknowledgements 147 Centre for Data Ethics and Innovation 2 Review into bias in algorithmic decision-making: Contents Preface Fairness is a highly prized human value. Algorithms, like all technology, should work for people, Societies in which individuals can flourish and not against them. need to be held together by practices and This is true in all sectors, but especially key in the public institutions that are regarded as fair. What sector. When the state is making life-affecting decisions it means to be fair has been much debated about individuals, that individual often can’t go elsewhere. throughout history, rarely more so than in Society may reasonably conclude that justice requires decision-making processes to be designed so that human recent months. Issues such as the global judgment can intervene where needed to achieve fair Black Lives Matter movement, the “levelling and reasonable outcomes for each person, informed by up” of regional inequalities within the UK, individual evidence. and the many complex questions of fairness raised by the COVID-19 pandemic have kept Data gives us a powerful weapon to see where fairness and equality at the centre of public bias is occurring and measure whether our debate. -
Acknowledgements Acknowl
2161 Acknowledgements Acknowl. B.21 Actuators for Soft Robotics F.58 Robotics in Hazardous Applications by Alin Albu-Schäffer, Antonio Bicchi by James Trevelyan, William Hamel, The authors of this chapter have used liberally of Sung-Chul Kang work done by a group of collaborators involved James Trevelyan acknowledges Surya Singh for de- in the EU projects PHRIENDS, VIACTORS, and tailed suggestions on the original draft, and would also SAPHARI. We want to particularly thank Etienne Bur- like to thank the many unnamed mine clearance experts det, Federico Carpi, Manuel Catalano, Manolo Gara- who have provided guidance and comments over many bini, Giorgio Grioli, Sami Haddadin, Dominic Lacatos, years, as well as Prof. S. Hirose, Scanjack, Way In- Can zparpucu, Florian Petit, Joshua Schultz, Nikos dustry, Japan Atomic Energy Agency, and Total Marine Tsagarakis, Bram Vanderborght, and Sebastian Wolf for Systems for providing photographs. their substantial contributions to this chapter and the William R. Hamel would like to acknowledge work behind it. the US Department of Energy’s Robotics Crosscut- ting Program and all of his colleagues at the na- C.29 Inertial Sensing, GPS and Odometry tional laboratories and universities for many years by Gregory Dudek, Michael Jenkin of dealing with remote hazardous operations, and all We would like to thank Sarah Jenkin for her help with of his collaborators at the Field Robotics Center at the figures. Carnegie Mellon University, particularly James Os- born, who were pivotal in developing ideas for future D.36 Motion for Manipulation Tasks telerobots. by James Kuffner, Jing Xiao Sungchul Kang acknowledges Changhyun Cho, We acknowledge the contribution that the authors of the Woosub Lee, Dongsuk Ryu at KIST (Korean Institute first edition made to this chapter revision, particularly for Science and Technology), Korea for their provid- Sect. -
The Grand Challenges of Science Robotics Exclusive Licensee Guang-Zhong Yang,1* Jim Bellingham,2 Pierre E
SCIENCE ROBOTICS | PERSPECTIVE ROBOTICS Copyright © 2018 The Authors, some rights reserved; The grand challenges of Science Robotics exclusive licensee Guang-Zhong Yang,1* Jim Bellingham,2 Pierre E. Dupont,3 Peer Fischer,4,5 Luciano Floridi,6,7,8,9,10 American Association 11 12,13 14 15 1 for the Advancement Robert Full, Neil Jacobstein, Vijay Kumar, Marcia McNutt, Robert Merrifield, of Science. No claim 16 17,18 7,8,9 19 Bradley J. Nelson, Brian Scassellati, Mariarosaria Taddeo, Russell Taylor, to original U.S. 20 21 22,23 Manuela Veloso, Zhong Lin Wang, Robert Wood Government Works One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpin- ning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible in- novation and how ethics and security should be carefully considered as we develop the technology further. Downloaded from INTRODUCTION great strides in realizing its mission of power- ing components into synthetic structures to Just over a year ago, we published the first issue ing the world’s robots, from space robot chal- create robots that perform like natural systems. of Science Robotics. Even within this relatively lenges to autonomous driving, industrial (iii) New power sources, battery technol- short period of time, remarkable progress has assembly, and surgery.