The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks

Survey of Trustworthy AI: A Meta Decision of AI

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective.Cloud-based Computational Decision, Artificial Intelligence, Machine Learning9. Industry, innovation and infrastructur

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of AI. Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI

Ethical research in public policy.

Public policy research is research for a purpose, guided by a distinctive range of normative considerations. The values are the values of public service; the work is generally done in the public domain; and the research is an intrinsic part of the democratic process, which depends on deliberation and accountability. Conventional representations of ethical research typically focus on ‘human subjects’ research, which raises different kinds of ethical issues to public policy research. Existing research ethics advice does not address the issues surrounding public policy research. Such research is typically concerned with collective action and the work of institutions, and the central guiding principles are not about responsibility to research participants, but duties to the public, as seen in principles of beneficence, citizenship, empowerment and the democratic process

Ethical Evidence and Policymaking

EPDF and EPUB available Open Access under CC-BY-NC-ND licence. This important book offers practical advice for using evidence and research in policymaking. The book has two aims. First, it builds a case for ethics and global values in research and knowledge exchange, and second, it examines specific policy areas and how evidence can guide practice. The book covers important policy areas including the GM debate, the environment, Black Lives Matter and COVID-19. Each chapter assesses the ethical challenges, the status of evidence in explaining or describing the issue and possible solutions to the problem. The book will enable policymakers and their advisors to seek evidence for their decisions from research that has been conducted ethically and with integrity

Values for a Post-Pandemic Future

This open access book shows how value sensitive design (VSD), responsible innovation, and comprehensive engineering can guide the rapid development of technological responses to the COVID-19 crisis. Responding to the ethical challenges of data-driven technologies and other tools requires thinking about values in the context of a pandemic as well as in a post-COVID world. Instilling values must be prioritized from the beginning, not only in the emergency response to the pandemic, but in how to proceed with new societal precedents materializing, new norms of health surveillance, and new public health requirements. The contributors with expertise in VSD bridge the gap between ethical acceptability and social acceptance. By addressing ethical acceptability and societal acceptance together, VSD guides COVID-technologies in a way that strengthens their ability to fight the virus, and outlines pathways for the resolution of moral dilemmas. This volume provides diachronic reflections on the crisis response to address long-term moral consequences in light of the post-pandemic future. Both contact-tracing apps and immunity passports must work in a multi-system environment, and will be required to succeed alongside institutions, incentive structures, regulatory bodies, and current legislation. This text appeals to students, researchers and importantly, professionals in the field