Staff, Author at Institute of Futures Research

April 27, 2023

(Featured) 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

April 27, 2023April 27, 2023

(Featured) 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

April 27, 2023

(Featured) 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

April 27, 2023

(Featured) 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

April 27, 2023

(Featured) The machine-like repair of aging. Disentangling the key assumptions of the SENS agenda

Pablo García-Barranquero and Marta Bertolaso critically examine the key assumptions of the Strategies for Engineered Negligible Senescence (SENS) agenda, which seeks to defeat aging by repairing the various cellular and molecular damages that accumulate over time. The authors argue that while SENS has made significant strides in understanding the mechanisms of aging, it fails to account for the complexity of the aging process and the potential risks of radical interventions. They explore the limitations of telomere lengthening, one of the most promising interventions against aging, to illustrate the challenges of intervening in biological mechanisms. The authors conclude that a better understanding of the “why” of aging is necessary to develop successful interventions that promote healthier aging.

The authors first outline the key assumptions of SENS, which include the view that aging is the result of accumulated damage to the body’s cells and molecules, and that repairing this damage can prevent or even reverse aging. They then explore the limitations of this approach, including the potential risks and unknown consequences of radical interventions, and the fact that aging is a complex, multifaceted process that cannot be reduced to a simple mechanistic model. The authors argue that while SENS has made significant progress in understanding the mechanisms of aging, it fails to account for the complexity of the aging process and the potential risks of radical interventions.

The authors then focus on telomere shortening, one of the fundamental mechanisms of aging, to illustrate the limitations of current knowledge of biological mechanisms. While artificial lengthening of telomeres by the action of the enzyme telomerase can stop and even reverse the process of telomere shortening, this approach is not without risks, including the potential for increased risk of cancer. The authors conclude that a better understanding of the “why” of aging is necessary to develop successful interventions that promote healthier aging.

This paper raises important philosophical questions about the relationship between biology and aging, and the limits of scientific intervention in biological processes. The authors argue that while a mechanistic approach to understanding and intervening in aging is necessary, it must be complemented by a broader understanding of the complex biological, social, and environmental factors that contribute to aging. This raises important questions about the role of philosophy in guiding scientific research and the need for interdisciplinary approaches to complex problems.

Future research in this area should focus on developing a more nuanced understanding of the complex interplay between biological, social, and environmental factors in aging. This will require interdisciplinary collaborations between philosophers, biologists, social scientists, and public health professionals, as well as a greater focus on the ethical and social implications of aging research. Additionally, there is a need for more research on the potential risks and unknown consequences of radical interventions, as well as the potential benefits of more targeted, personalized interventions. Finally, future research should explore alternative approaches to understanding and intervening in aging, including approaches that focus on promoting resilience and healthy aging rather than simply reversing the effects of aging.

Abstract

The possibility of curing aging is currently generating hopes and concerns among entrepreneurs, experts, and the general public. This article aims to clarify some of the key assumptions of the Strategies for Engineered Negligible Senescence agenda, one of the most prominent paradigms for rejuvenation. To do this, we present the three fundamental claims of this research program: (1) aging can be repaired; (2) rejuvenation is possible through the reversal of all molecular damage; (3) and the human organism is a sophisticated machine. Secondly, we argue that this agenda fits with a machine conception of the organism (described by Daniel Nicholson); we show that, if aging is understood from this philosophical approach, there is an internal confusion in the research program between what is repair and what is rejuvenation. Finally, we state that this theoretical viewpoint connects with scientific criticism and reinforces the idea that there are limits to the aspirations to live indefinitely young.
The machine-like repair of aging. Disentangling the key assumptions of the SENS agenda

April 26, 2023April 27, 2023

(Featured) Machine Understanding and Deep Learning Representation

Michael Tamir and Elay Shech embark on an ambitious journey to explore the notion of understanding in the context of deep learning algorithms. They attempt to ascertain if the impressive achievements of deep learning, particularly in areas where these algorithms can compete with human performance, can be construed as an indication of genuine understanding. To this end, the authors delve into the philosophy of understanding and seek to establish criteria for evaluating machine understanding. This is done in the hope of determining whether the trends and patterns exhibited by deep learning algorithms in representation and information compression can be equated with partial or full satisfaction of these criteria.

The authors identify three key factors in understanding: reliability and robustness, information relevance, and well-structured representation. They argue that these factors can be observed in deep learning algorithms, providing a basis for evaluating their presence in direct task performance or in analyzing the representations learned within the neural networks’ hidden layers. In order to assess understanding in machines, the authors draw upon various concepts and techniques from the realm of deep learning, such as generalization error, information bottleneck analysis, and the notion of disentanglement.

The authors consider and address three possible objections to their arguments. First, they acknowledge the narrow scope of deep learning models’ success in achieving human competitive performance, as well as their limitations in other tasks. However, they contend that their goal is to provide a framework for evaluating potential machine understanding, rather than claiming that any specific algorithm exhibits a certain degree of understanding. Second, they respond to concerns about the constitutive nature of the three key factors by emphasizing that their work serves as a critical tool for quantifying and comparing evidence of understanding, rather than making conclusive judgments. Finally, the authors address the objection that understanding requires a “mentality,” highlighting the value of an aspect-sensitive account of machine understanding that is independent of presupposed mentality.

When it comes to broader philosophical issues, the paper contributes to ongoing debates surrounding the nature of understanding in both human and artificial agents. The authors draw connections between deep learning algorithms and philosophical accounts of understanding, showing how concepts from the latter can be utilized to develop evaluation criteria for the former. By doing so, they provide a valuable philosophical framework for approaching the topic of machine understanding, allowing for a more nuanced analysis of the similarities and differences between human and machine cognition.

The paper’s findings open up several avenues for future research and investigation. One possible direction is to delve deeper into the interplay between the key factors of understanding, exploring how they might be combined or weighted to better assess the relative strengths and weaknesses of various deep learning algorithms. Another promising area for exploration is the application of the authors’ framework to other types of artificial intelligence, such as reinforcement learning or unsupervised learning. Additionally, examining the potential impact of advancements in AI hardware, neural network architectures, or training methodologies on the key factors of understanding could further enrich our understanding of the relationship between deep learning and the philosophy of understanding.

Abstract

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms.
Machine understanding and deep learning representation

April 26, 2023April 26, 2023

(Featured) 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