This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning 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 —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. 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 Machine Learning development 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. (shrink)

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout (...) the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. (shrink)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (shrink)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (shrink)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To (...) achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)

This paper proposes a systematic framework for the analysis of tensions in corporate sustainability. The framework is based on the emerging integrative view on corporate sustainability, which stresses the need for a simultaneous integration of economic, environmental and social dimensions without, a priori, emphasising one over any other. The integrative view presupposes that firms need to accept tensions in corporate sustainability and pursue different sustainability aspects simultaneously even if they seem to contradict each other. The framework proposed in this paper (...) goes beyond the traditional triad of economic, environmental and social dimensions and argues that tensions in corporate sustainability occur between different levels, in change processes and within a temporal and spatial context. The framework provides vital groundwork for managing tensions in corporate sustainability based on paradox strategies. The paper then applies the framework to identify and characterise four selected tensions and illustrates how key approaches from the literature on strategic contradictions, tensions and paradoxes—i.e., acceptance and resolution strategies—can be used to manage these tensions. Thereby, it refines the emerging literature on the integrative view for the management of tensions in corporate sustainability. The framework also provides managers with a better understanding of tensions in corporate sustainability and enables them to embrace these tensions in their decision making. (shrink)

In previous works (Floridi 2018) I introduced the distinction between hard ethics (which may broadly be described as what is morally right and wrong independently of whether something is legal or illegal), and soft or post-compliance ethics (which focuses on what ought to be done over and above existing legislation). This paper analyses the applicability of soft ethics to the General Data Protection Regulation and advances the theory that soft ethics has a dual advantage—as both an opportunity strategy and a (...) risk management solution. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of (...) automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

Many writers have commented on the heterogeneity of the socially responsible investment (SRI) movement. However, few have actually tried to understand and explain it, and even fewer have discussed whether the opposite – standardisation – is possible and desirable. In this article, we take a broader perspective on the issue of the heterogeneity of SRI. We distinguish between four levels on which heterogeneity can be found: the terminological, definitional, strategic and practical. Whilst there is much talk about the definitional ambiguities (...) of SRI, we suggest that there is actually some agreement on the definitional level. There are at least three explanations which we suggest can account for the heterogeneity on the other levels: cultural and ideological differences between different regions, differences in values, norms and ideology between various SRI stakeholders, and the market setting of SRI. Discussing the implications of the three explanations for the SRI market, we suggest that there is reason to be sceptical about the possibilities of standardisation if not standardisation is imposed top-down. Whether this kind of standardisation is desirable or not, we argue, depends on what the motives for it would be. To the extent that standardisation may facilitate the mainstreaming of SRI, it could be a good thing – but we entertain doubts about whether mainstreaming really requires standardisation. (shrink)

We investigate how conventional asset managers account for environmental, social, and governance factors in their investment process. We do so on the basis of an international survey among fund managers. We find that many conventional managers integrate responsible investing in their investment process. Furthermore, we find that ESG information in particular is being used for red flagging and to manage risk. We find that many conventional fund managers have already adopted features of responsible investing in the investment process. Furthermore, we (...) argue and show that ESG investing is highly similar to fundamental investing. We also reveal that there is a substantial difference in the ways in which U.S. and European asset managers view ESG. (shrink)

We investigate how conventional asset managers account for environmental, social, and governance factors in their investment process. We do so on the basis of an international survey among fund managers. We find that many conventional managers integrate responsible investing in their investment process. Furthermore, we find that ESG information in particular is being used for red flagging and to manage risk. We find that many conventional fund managers have already adopted features of responsible investing in the investment process. Furthermore, we (...) argue and show that ESG investing is highly similar to fundamental investing. We also reveal that there is a substantial difference in the ways in which U.S. and European asset managers view ESG. (shrink)

Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks (...) are inadequate for AI-intensive companies. To fully account for contemporary technology, the following categories of evaluation will be developed and featured as vital investing criteria: autonomy, dignity, privacy, performance. With these priorities established, the larger goal is a model for humanitarian investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors. (shrink)

This article introduces an integrative framework of corporate social responsibility (CSR) design and implementation. A review of CSR literature -in particular with regard to design and implementation models -provides the background to develop a multiple case study. The resulting integrative framework, based on this multiple case study and Lewin's change model, highlights four stages that span nine steps of the CSR design and implementation process. Finally, the study identifies critical success factors for the CSR process.