Goto

Collaborating Authors

A Framework for Ethical AI at the United Nations

arXiv.org Artificial Intelligence

This paper aims to provide an overview of the ethical concerns in artificial intelligence (AI) and the framework that is needed to mitigate those risks, and to suggest a practical path to ensure the development and use of AI at the United Nations (UN) aligns with our ethical values. The overview discusses how AI is an increasingly powerful tool with potential for good, albeit one with a high risk of negative side-effects that go against fundamental human rights and UN values. It explains the need for ethical principles for AI aligned with principles for data governance, as data and AI are tightly interwoven. It explores different ethical frameworks that exist and tools such as assessment lists. It recommends that the UN develop a framework consisting of ethical principles, architectural standards, assessment methods, tools and methodologies, and a policy to govern the implementation and adherence to this framework, accompanied by an education program for staff.


Future Intelligent Autonomous Robots, Ethical by Design. Learning from Autonomous Cars Ethics

arXiv.org Artificial Intelligence

Development of the intelligent autonomous robot technology presupposes its anticipated beneficial effect on the individuals and societies. In the case of such disruptive emergent technology, not only questions of how to build, but also why to build and with what consequences are important. The field of ethics of intelligent autonomous robotic cars is a good example of research with actionable practical value, where a variety of stakeholders, including the legal system and other societal and governmental actors, as well as companies and businesses, collaborate bringing about shared view of ethics and societal aspects of technology. It could be used as a starting platform for the approaches to the development of intelligent autonomous robots in general, considering human-machine interfaces in different phases of the life cycle of technology - the development, implementation, testing, use and disposal. Drawing from our work on ethics of autonomous intelligent robocars, and the existing literature on ethics of robotics, our contribution consists of a set of values and ethical principles with identified challenges and proposed approaches for meeting them. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Our recommendations of ethical requirements for autonomous cars can be used for other types of intelligent autonomous robots, with the caveat for social robots that require more research regarding interactions with the users. We emphasize that existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need of a continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders.


Teaching AI, Ethics, Law and Policy

arXiv.org Artificial Intelligence

The cyberspace and the development of new technologies, especially intelligent systems using artificial intelligence, present enormous challenges to computer professionals, data scientists, managers and policy makers. There is a need to address professional responsibility, ethical, legal, societal, and policy issues. This paper presents problems and issues relevant to computer professionals and decision makers and suggests a curriculum for a course on ethics, law and policy. Such a course will create awareness of the ethics issues involved in building and using software and artificial intelligence.


From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices

arXiv.org Artificial Intelligence

However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles--the'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)--rather than on practices, the'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.


AI Ethics Principles in Practice: Perspectives of Designers and Developers

arXiv.org Artificial Intelligence

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia's national scientific research agency (CSIRO), who are involved in designing and developing AI systems for a range of purposes. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles that are proposed by the Australian Government. The principles comprise: Privacy Protection & Security, Reliability & Safety, Transparency & Explainability, Fairness, Contestability, Accountability, Human-centred Values, and Human, Social & Environmental Wellbeing. The insights of the researchers and engineers as well as the challenges that arose for them in the practical application of the principles are examined. Finally, a set of organisational responses are provided to support the implementation of high-level AI ethics principles into practice.