This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Have you heard about fake news, AI, and deep fake? This blog post is going to introduce you to all three of these terms, giving a broad overview of just what is going on. Are we living in a science fiction film? Are computers going to bring about the end of civilization as we know it? This isn't fiction, so I'm not talking about Brave New World (although you should read that book).
In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
Recent controversies related to topics such as fake news, privacy, and algorithmic bias have prompted increased public scrutiny of digital technologies and soul-searching among many of the people associated with their development. In response, the tech industry, academia, civil society, and governments have rapidly increased their attention to "ethics" in the design and use of digital technologies ("tech ethics"). Yet almost as quickly as ethics discourse has proliferated across the world of digital technologies, the limitations of these approaches have also become apparent: tech ethics is vague and toothless, is subsumed into corporate logics and incentives, and has a myopic focus on individual engineers and technology design rather than on the structures and cultures of technology production. As a result of these limitations, many have grown skeptical of tech ethics and its proponents, charging them with "ethics-washing": promoting ethics research and discourse to defuse criticism and government regulation without committing to ethical behavior. By looking at how ethics has been taken up in both science and business in superficial and depoliticizing ways, I recast tech ethics as a terrain of contestation where the central fault line is not whether it is desirable to be ethical, but what "ethics" entails and who gets to define it. This framing highlights the significant limits of current approaches to tech ethics and the importance of studying the formulation and real-world effects of tech ethics. In order to identify and develop more rigorous strategies for reforming digital technologies and the social relations that they mediate, I describe a sociotechnical approach to tech ethics, one that reflexively applies many of tech ethics' own lessons regarding digital technologies to tech ethics itself.
In the current era, people and society have grown increasingly reliant on Artificial Intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, health care, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great efforts of designing more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.
In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.
Back in 2014, the Los Angeles Times published a report about an earthquake three minutes after it happened. This feat was possible because a staffer had developed a bot (a software robot) called Quakebot to write automated articles based on data generated by the US Geological Survey. Today, AIs write hundreds of thousands of the articles that are published by mainstream media outlets every week. At first, most of the Natural Language Generation (NLG) tools producing these articles were provided by software companies like Narrative Science. Today, many media organisations have developed in-house versions.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.