Tag: Governance

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(Featured) Moral distance, AI, and the ethics of care

Moral distance, AI, and the ethics of care

Carolina Villegas-Galaviz and Kirsten Martin analyze the ethical implications of AI decision-making and suggest the ethics of care as a framework for mitigating its negative impacts. They argue that AI exacerbates moral distance by creating proximity and bureaucratic distance, which lead to a lack of consideration for the needs of all stakeholders. The ethics of care, which emphasizes interdependent relationships, context and circumstances, vulnerability, and voice, can help contextualize the issue and bring us closer to those at a distance. The authors note that this framework can aid in the development of algorithmic decision-making tools that consider the ethics of care.

The authors argue that moral distance arises from proximity and bureaucratic distance. Proximity distance refers to the physical, cultural, and temporal separation between people, while bureaucratic distance refers to hierarchy, complexity, and principle-based decision-making. These types of moral distance are inherent in how AI works, and the authors contend that AI exacerbates them. The authors also suggest that the ethics of care can help mitigate the negative impacts of AI by emphasizing the need for interdependent relationships, contextual understanding, vulnerability, and voice.

The authors argue that the ethics of care is useful in analyzing algorithmic decision-making in AI. They suggest that the ethics of care offers a mechanism for designing and developing algorithmic decision-making tools that consider the needs of all stakeholders. However, they acknowledge that the ethics of care may not be a comprehensive solution to all moral problems or harms.

The paper raises broader philosophical issues about the role of ethics in technology. It highlights the need to consider the ethical implications of technology and the importance of developing ethical frameworks for AI decision-making. The authors suggest that the ethics of care offers a new conversation for the critical examination of AI and underscores the importance of hearing diverse voices and considering the needs of all stakeholders in technology development.

Future research should explore the legal, moral, epistemic, and practical aspects of moral distance and their specific implications. It should also examine the full range of feminist theory and its potential to mitigate the problem of representativeness in the technology workforce. The authors note that interdisciplinary and intercultural teams are essential in developing and deploying AI ethically. Finally, they suggest that a deeper understanding of the ethics of care could have implications for other areas of philosophical inquiry, such as environmental ethics and bioethics.

Abstract

This paper investigates how the introduction of AI to decision making increases moral distance and recommends the ethics of care to augment the ethical examination of AI decision making. With AI decision making, face-to-face interactions are minimized, and decisions are part of a more opaque process that humans do not always understand. Within decision-making research, the concept of moral distance is used to explain why individuals behave unethically towards those who are not seen. Moral distance abstracts those who are impacted by the decision and leads to less ethical decisions. The goal of this paper is to identify and analyze the moral distance created by AI through both proximity distance (in space, time, and culture) and bureaucratic distance (derived from hierarchy, complex processes, and principlism). We then propose the ethics of care as a moral framework to analyze the moral implications of AI. The ethics of care brings to the forefront circumstances and context, interdependence, and vulnerability in analyzing algorithmic decision making.

Moral distance, AI, and the ethics of care
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(Featured) The epistemic impossibility of an artificial intelligence take-over of democracy

The epistemic impossibility of an artificial intelligence take-over of democracy

Daniel Innerarity explores the limits of algorithmic governance in relation to democratic decision-making. They argue that algorithms function with a 0/1 logic that is the opposite of ambiguity, and they are unable to handle complex problems that are not well-structured or quantifiable. The authors argue that politics consists of making decisions in the absence of indisputable evidence and that algorithms are of limited utility in such circumstances. Algorithmic rationality reduces the complexity of social phenomena to numbers, whereas political decisions are rarely based on binary categories. The authors suggest that the epistemological principle of uncertainty is central to democratic institutions and that our democratic institutions are a recognition of our ignorance.

The author highlights the limitations of algorithms in decision-making and suggest that they are appropriate only for well-structured and quantifiable problems. In contrast, political decisions are rarely based on binary categories, and politics consists of making decisions in the absence of indisputable evidence. The authors argue that algorithmic rationality reduces the complexity of social phenomena to numbers, which is inappropriate for democratic decision-making. Instead, they suggest that democratic institutions are a recognition of our ignorance and the importance of uncertainty in decision-making.

The author suggests that the epistemological principle of uncertainty is central to democratic institutions. They argue that democracy exists precisely because our knowledge is so limited, and we are so prone to error. Precisely where our knowledge is incomplete, we have greater need for institutions and procedures that favour reflection, debate, criticism, independent advice, reasoned argumentation, and the competition of ideas and visions. Our democratic institutions are not an exhibition of how much we know but a recognition of our ignorance.

The research presented in this paper is significant for broader philosophical issues related to the relationship between knowledge, power, and democratic decision-making. It raises questions about the role of algorithms in decision-making and the limits of rationality in politics. It also highlights the importance of uncertainty, ambiguity, and contingency in democratic decision-making, which has important implications for the legitimacy of democratic institutions.

Future research could explore the implications of these findings for the development of democratic institutions and the role of algorithms in decision-making. It could also explore the role of uncertainty, ambiguity, and contingency in decision-making more broadly and its relationship to different philosophical traditions. Furthermore, it could explore the implications of these findings for the development of more participatory and deliberative forms of democracy that allow for greater reflection, debate, and criticism.

Abstract

Those who claim, whether with fear or with hope, that algorithmic governance can control politics or the whole political process or that artificial intelligence is capable of taking charge of or wrecking democracy, recognize that this is not yet possible with our current technological capabilities but that it could come about in the future if we had better quality data or more powerful computational tools. Those who fear or desire this algorithmic suppression of democracy assume that something similar will be possible someday and that it is only a question of technological progress. If that were the case, no limits would be insurmountable on principle. I want to challenge that conception with a limit that is less normative than epistemological; there are things that artificial intelligence cannot do, because it is unable to do them, not because it should not do them, and this is particularly apparent in politics, which is a peculiar decision-making realm. Machines and people take decisions in a very different fashion. Human beings are particularly gifted at one type of situation and very clumsy in others. The part of politics that is, strictly speaking, political is where this contrast and our greatest aptitude are most apparent. If that is the case, as I believe, then the possibility that democracy will one day be taken over by artificial intelligence is, as a fear or as a desire, manifestly exaggerated. The corresponding counterpart to this is: if the fear that democracy could disappear at the hands of artificial intelligence is not realistic, then we should not expect exorbitant benefits from it either. For epistemic reasons that I will explain, it does not seem likely that artificial intelligence is capable of taking over political logic.

The epistemic impossibility of an artificial intelligence take-over of democracy
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(Featured) Accountability in artificial intelligence: what it is and how it works

Accountability in artificial intelligence: what it is and how it works

Claudio Novelli, Mariarosaria Taddeo, and Luciano Floridi provide a comprehensive analysis of accountability in the context of artificial intelligence (AI). The paper begins by defining accountability as a relation of answerability that requires recognition of authority, interrogation of power, and limitations on that power. The authors then specify the content of this relation through seven features, including context, range, agent, forum, standard, process, and implications. They also identify four goals of accountability in AI, including compliance, report, oversight, and enforcement. The authors apply their analysis to AI governance, highlighting the importance of proactive and reactive accountability and the governance missions that underlie different accountability policies. The paper concludes with reflections on the challenges and opportunities for accountability in the context of AI.

The authors’ analysis of accountability in AI is both detailed and nuanced. They provide a clear and comprehensive framework for understanding the different dimensions of accountability and the goals that it can serve. The paper’s focus on the importance of both proactive and reactive accountability is particularly important, as it highlights the need for accountability to be built into the design, development, and deployment of AI systems, rather than being an afterthought. The authors’ emphasis on the importance of governance objectives is also useful, as it highlights the need for accountability policies to be tailored to specific contexts and goals.

One of the most interesting aspects of the paper is the authors’ analysis of the relationship between accountability and power. The authors argue that accountability is a necessary mechanism for limiting the power of those who develop and deploy AI systems. This raises broader philosophical questions about the nature of power and its relationship to ethics and morality. For example, how can we ensure that those who hold power are held accountable for their actions? What ethical principles should guide the use of power in the context of AI? These are important questions that require further philosophical exploration.

The authors’ analysis of accountability in AI also raises important questions about the role of technology in society. As AI systems become more prevalent and powerful, the need for accountability becomes ever more pressing. However, ensuring accountability is not a simple matter, as it requires balancing competing values and interests. The authors suggest that future research should focus on developing more concrete and practical guidelines for implementing accountability in the context of AI. This is an important avenue for further exploration, as it could help to ensure that AI systems are developed and deployed in ways that are consistent with ethical and moral principles. Overall, this paper provides a useful framework for understanding accountability in the context of AI, and it offers important insights for both philosophers and policymakers.

Abstract

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, and implications). We analyze this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasize or prioritize some over others depending on the proactive or reactive use of accountability and the missions of AI governance.

Accountability in artificial intelligence: what it is and how it works
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(Featured) How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation

How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation

Sophia Knopf, Nina Frahm, and Sebastian Pfotenhauer provide a thought-provoking exploration of the ethical considerations and implications that emerge within the context of direct-to-consumer (DTC) neurotechnology start-ups. The authors investigate how these companies approach and enact ethical considerations, particularly focusing on boundary-work and the strategic use of ethics to establish credibility, legitimacy, and autonomy in an unsettled and contested field. Through a series of interviews and qualitative analysis, the paper uncovers the various ways in which neurotechnology start-ups mobilize ethics to navigate the complex terrain between visionary promises and potential ethical hazards and risks.

The study highlights four dimensions of boundary-work that DTC neurotechnology start-ups engage in: actual vs. hypothetical issues, good vs. bad purposes and consequences, consumer safety vs. medical risk or harm, and sound science vs. overpromising. The authors suggest that ethics functions as a mediator, facilitating the articulation of visions of successful technologies and desirable futures. By framing ethics through boundary-work, the start-ups strategically defer certain ethical challenges to the future while delegating ethical reasoning to established knowledge regimes.

The authors propose that such framing of ethics allows the start-ups to construct desirable technology trajectories from the present into the future, establishing credibility and legitimacy in the field. In essence, the paper argues that ethics becomes a key ingredient in nascent knowledge-control regimes where the power to shape a specific understanding of ethics allocates rights and responsibilities, legitimizing certain visions of desirable socio-technical futures and neuro-innovation practices.

Relating this research to broader philosophical issues, we can observe that the questions raised in the paper touch upon the nature of ethics and responsibility in technological innovation. This resonates with wider discussions in philosophy regarding the ethics of emerging technologies, the role of expertise, and the co-construction of socio-technical futures. The paper illuminates the complex relationship between ethical considerations, stakeholder interests, and the shaping of technology and society, which has been a longstanding concern in philosophy of technology and science and technology studies.

The paper offers numerous potential avenues for further research and investigation. Future studies could explore how these ethical strategies and boundary-work practices compare to those employed in other emerging technology sectors. Another promising area of inquiry would be the examination of the potential effects of evolving regulatory frameworks and public discourse on the ethical practices of start-ups in DTC neurotechnology and beyond. Such research would further our understanding of the dynamic interplay between ethics, technology, and society in shaping our collective future.

Abstract

Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society—from brain-computer-interfaces to neural enhancement and cognitive phenotyping—are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses—and especially start-ups—address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology’s actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology’s benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of ‘ethical realities’ in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.

How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation