Tag: Artificial Intelligence

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(Featured) The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work

The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work

In the realm of artificial intelligence (AI) deployment, a neglected ethical concern is the impact of AI on meaningful work. Sarah Bankins and Paul Formosa focus on this critical aspect, emphasizing that understanding the consequences of AI on meaningful work for the remaining workforce is as significant as examining the impact of AI-induced unemployment. Meaningful work plays a crucial role in human well-being, autonomy, and flourishing, rendering it an essential ethical dimension.

The authors investigate three paths of AI deployment: replacing tasks, ‘tending the machine’, and amplifying, across five dimensions of meaningful work: task integrity, skill cultivation and use, task significance, autonomy, and belongingness. By employing this approach, they identify the ways AI may both enhance and undermine meaningful work experiences across the dimensions. Additionally, the authors draw upon ethical implications by utilizing five key ethical AI principles, providing practical guidance for organizations and suggesting opportunities for future research.

The paper concludes that AI has the potential to make work more meaningful for some workers by performing less meaningful tasks and amplifying their capabilities. However, it also highlights the risk of making work less meaningful for others by generating monotonous tasks, restricting worker autonomy, and disproportionately distributing AI benefits away from less-skilled workers. This dualistic impact suggests that AI’s future effects on meaningful work will be both significant and varied.

The authors’ analysis of AI and meaningful work raises broader philosophical issues. One such issue pertains to the value of work in the context of human dignity, self-realization, and social connection. As AI technologies advance, society will need to reflect on the meaning of work and redefine it in response to the changes brought about by these innovations. Furthermore, the ethical principles guiding AI development and deployment must not only ensure fair and equitable distribution of benefits but also preserve the essence of human engagement in work.

Future research in this area could explore the potential impact of AI on work’s existential value and its influence on the human experience. Researchers may also delve into the development of ethical frameworks that ensure AI technologies foster more meaningful work and equitable distribution of benefits. Finally, the potential outcomes and implications of artificial general intelligence (AGI) on meaningful work should be considered, as AGI could dramatically alter the landscape of human labor and the very nature of work itself.

Abstract

The increasing workplace use of artificially intelligent (AI) technologies has implications for the experience of meaningful human work. Meaningful work refers to the perception that one’s work has worth, significance, or a higher purpose. The development and organisational deployment of AI is accelerating, but the ways in which this will support or diminish opportunities for meaningful work and the ethical implications of these changes remain under-explored. This conceptual paper is positioned at the intersection of the meaningful work and ethical AI literatures and offers a detailed assessment of the ways in which the deployment of AI can enhance or diminish employees’ experiences of meaningful work. We first outline the nature of meaningful work and draw on philosophical and business ethics accounts to establish its ethical importance. We then explore the impacts of three paths of AI deployment (replacing some tasks, ‘tending the machine’, and amplifying human skills) across five dimensions constituting a holistic account of meaningful work, and finally assess the ethical implications. In doing so we help to contextualise the meaningful work literature for the era of AI, extend the ethical AI literature into the workplace, and conclude with a range of practical implications and future research directions.

The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work
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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) Emotional AI and the future of wellbeing in the post-pandemic workplace

Emotional AI and the future of wellbeing in the post-pandemic workplace

Peter Mantello and Manh-Tung Ho examine the impact of emotional artificial intelligence (AI) technologies on employee-employer relationships, focusing on the case of Amazon Japan. The authors argue that the adoption of AI technologies for managing employee emotions can exacerbate pre-existing issues of precarity and worsen the already dire global economic situation. Although emotional AI is being touted as a way to combat stress-related work absences, it is based on the same neoliberal logic that creates these problems. The paper concludes that in order for emotional AI to play a positive role in the workplace, three essential steps must be taken: the technology must be designed to better understand human emotions, workers must have access and control over their data, and a more pluralistic approach to devising regulatory frameworks must be adopted.

The authors begin by discussing the growth of precarity and the worsening global economic situation, noting that these factors have led to an increased demand for emotion-sensing technologies. They examine the case of Amazon Japan, which has been embroiled in legal disputes due to its culturally insensitive performance improvement plan and general hostility towards collective bargaining. The authors argue that emotional AI is being uncritically adopted as a tool for combating stress-related work absences, without considering the underlying neoliberal logic and efficiency practices that contribute to these problems.

The authors then turn to the traditional Japanese work culture, which values loyalty over productivity and focuses on solidarity, consensus, long-term trust, and human growth. They argue that the adoption of AI-driven management systems signifies a lack of trust in workers, which challenges this traditional work culture. The authors suggest that emotional AI companies and policy makers would benefit from embracing a more pluralistic approach to devising regulatory frameworks that draw from both Eastern and Western value traditions.

This paper raises important questions about the role of emotional AI technologies in the workplace and their impact on employee-employer relationships. It also highlights the need to better understand the complexity of human emotions and to incorporate a greater range of modulators to account for diversity and particularity. Philosophers and researchers interested in the ethics of AI and its impact on society will find this paper to be a valuable contribution to the ongoing debate.

Future research could explore the impact of emotional AI on other aspects of the workplace, such as employee creativity and innovation. It could also examine the potential for emotional AI to exacerbate issues of bias and discrimination. Finally, future research could explore the implications of emotional AI technologies for the broader philosophical debate about the relationship between humans and machines.

Abstract

This paper interrogates the growing pervasiveness of affect recognition tools as an emerging layer human-centric automated management in the global workplace. While vendors tout the neoliberal incentives of emotion-recognition technology as a pre-eminent tool of workplace wellness, we argue that emotional AI recalibrates the horizons of capital not by expanding outward into the consumer realm (like surveillance capitalism). Rather, as a new genus of digital Taylorism, it turns inward, passing through the corporeal exterior to extract greater surplus value and managerial control from the affective states of workers. Thus, empathic surveillance signals a profound shift in the ontology of human labor relations. In the emotionally quantified workplace, employees are no longer simply seen as physical capital, but conduits of actuarial and statistical intelligence gleaned from their most intimate subjective states. As a result, affect-driven automated management means that priority is often given to actuarial rather than human-centered managerial decisions.

Emotional AI and the future of wellbeing in the post-pandemic workplace
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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