Tag: Philosophy

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(Featured) A Loosely Wittgensteinian Conception of the Linguistic Understanding of Large Language Models like BERT, GPT-3, and ChatGPT

A Loosely Wittgensteinian Conception of the Linguistic Understanding of Large Language Models like BERT, GPT-3, and ChatGPT

Reto Gubelmann articulates a “loosely Wittgensteinian” conception of linguistic understanding, particularly in the context of advanced artificial intelligence (AI) models such as BERT, GPT-3, and ChatGPT. The author posits that these transformer-based natural language processing (NNLP) models are closing in on the capacity to genuinely understand language, a claim that is buttressed by both empirical and conceptual arguments. The empirical basis is grounded on the remarkable performance of these AI models on benchmarks like GLUE and SuperGLUE, which evaluate them on tasks that, in a human context, would necessitate a deep understanding of language, such as answering questions about a text, summarizing text, and discerning logical relationships between statements. The conceptual underpinnings of this claim draw upon the works of Glock, Taylor, and Wittgenstein to argue that linguistic understanding, a form of intelligence, is marked by flexibility in handling new tasks and novel inputs, as well as the capability to autonomously adapt to new tasks​​.

The article further navigates through the terrain of philosophical objections to the idea that AI can understand language. The author counters objections raised by Searle, Bender, Koller, Davidson, and Nagel, among others, arguing that understanding language does not necessitate any esoteric or mysterious component such as qualia. Rather, it is dependent on the competencies of the AI model, specifically its autonomous adaptability and performance in a wide array of linguistic tasks in diverse settings. By this definition, the author contends that current transformer-based NNLP models are inching closer to meeting the criteria for linguistic understanding​​.

The author also provides a succinct yet comprehensive overview of the evolution of AI models, from the era of “Good Old-Fashioned AI” (GOFAI), which relied on explicit rules and logical processing, to the emergence of neural network models or connectionist AI, which represent a fundamentally different approach to designing intelligent systems. The distinguishing feature of these neural network models, such as the transformer-based models under discussion, is their learning-based approach, which enables them to adapt to new tasks and exhibit flexibility in the face of novel inputs​​.

Embedding these discussions within broader philosophical issues, the article provides a fruitful platform for exploring the nature of intelligence, understanding, and language. The examination of whether or not AI models can understand language opens up questions about the definition of understanding and the conditions that must be met to ascribe understanding to a being. This interrogation is undergirded by a Wittgensteinian perspective, which has profound implications for our understanding of language, mind, and the possibilities of AI. It also prompts us to reconsider the boundaries we draw between human and machine intelligence.

Future research should continue to explore these Wittgensteinian conceptions of linguistic understanding, particularly as AI models continue to evolve and improve. More empirical work could be conducted to test the adaptability and flexibility of AI models in novel linguistic situations, providing more robust evidence for or against their capacity to understand language. Furthermore, the philosophical debate concerning language understanding in AI should continue to be pushed forward, with deeper explorations of the arguments against AI understanding and the development of new philosophical frameworks that can accommodate the rapidly advancing capabilities of AI. As this field advances, interdisciplinary collaboration between AI researchers, linguists, and philosophers will be vital in order to fully grasp the implications of these transformative technologies.

Abstract

In this article, I develop a loosely Wittgensteinian conception of what it takes for a being, including an AI system, to understand language, and I suggest that current state of the art systems are closer to fulfilling these requirements than one might think. Developing and defending this claim has both empirical and conceptual aspects. The conceptual aspects concern the criteria that are reasonably applied when judging whether some being understands language; the empirical aspects concern the question whether a given being fulfills these criteria. On the conceptual side, the article builds on Glock’s concept of intelligence, Taylor’s conception of intrinsic rightness as well as Wittgenstein’s rule-following considerations. On the empirical side, it is argued that current transformer-based NNLP models, such as BERT and GPT-3 come close to fulfilling these criteria.

A Loosely Wittgensteinian Conception of the Linguistic Understanding of Large Language Models like BERT, GPT-3, and ChatGPT
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(Featured) Ethics of AI and Health Care: Towards a Substantive Human Rights Framework

Ethics of AI and Health Care: Towards a Substantive Human Rights Framework

S. Matthew Liao provides an incisive exploration into the ethical considerations intrinsic to the application of artificial intelligence (AI) in healthcare contexts. The paper underscores the burgeoning interest in employing AI for health-related purposes, with AI applications demonstrating competencies in diagnosing certain types of cancer, identifying heart rhythm abnormalities, diagnosing various eye diseases, and even identifying viable embryos. However, the author cautions that the deployment of AI in healthcare settings necessitates adherence to robust ethical frameworks and guidelines.

The author identifies a burgeoning multitude of ethical frameworks for AI that have been proposed over recent years. The count of such frameworks exceeds 80 and stems from a diverse array of sources including private corporations, governmental agencies, academic institutions, and intergovernmental bodies. These frameworks commonly reference the four principles of biomedical ethics: autonomy, beneficence, non-maleficence, and justice, and often include recommendations for transparency, explainability, and trust. However, the author warns that the proliferation of these frameworks has led to confusion, thereby raising pressing questions about the basis, justification, and practical implementation of these recommendations.

In response to this conundrum, the author proposes an AI ethics framework rooted in substantive human rights theory. This proposed framework seeks to address the questions raised by the proliferation of ethical guidelines and to provide clear and practical guidance for the use of AI in healthcare. The author argues for an ethical framework that is not only abstract but also expounds the grounds and justifications of the recommendations it puts forward, as well as how these recommendations should be applied in practice.

The broader philosophical discourse that this research engages with is the ethics of technology and, more specifically, the ethical and moral implications of AI use in healthcare. The central philosophical question the author grapples with is the tension between the rapid development and application of AI in healthcare and the need for substantive ethical guidelines to govern its use. This brings into sharp focus the perennial philosophical tension between progress and ethical constraint, raising the specter of issues such as the nature of autonomy, the definition of harm, and the equitable distribution of benefits and burdens.

For future research, the author’s proposition of a human rights-based ethical framework opens up multiple avenues. First, the application of this framework could be examined in real-world healthcare scenarios to assess its efficacy in guiding ethical AI use. Second, the interplay between this framework and existing legal systems could be studied to ascertain any gaps or overlaps. Lastly, a comparative analysis could be conducted of how this proposed framework fares against other ethical frameworks in use, and how it might be refined or integrated with other approaches for a more robust ethical guidance in healthcare AI applications.

Abstract

There is enormous interest in using artificial intelligence (AI) in health care contexts. But before AI can be used in such settings, we need to make sure that AI researchers and organizations follow appropriate ethical frameworks and guidelines when developing these technologies. In recent years, a great number of ethical frameworks for AI have been proposed. However, these frameworks have tended to be abstract and not explain what grounds and justifies their recommendations and how one should use these recommendations in practice. In this paper, I propose an AI ethics framework that is grounded in substantive, human rights theory and one that can help us address these questions.

Ethics of AI and Health Care: Towards a Substantive Human Rights Framework
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(Featured) The black box problem revisited. Real and imaginary challenges for automated legal decision making

The black box problem revisited. Real and imaginary challenges for automated legal decision making

Bartosz Brożek et al. explore the ethical and practical dilemmas arising from the integration of Artificial Intelligence (AI) in the realm of law. The authors suggest that despite the perceived opacity and unpredictability of AI, these machines can provide rational and justifiable decisions in legal reasoning. By challenging conventional notions of decision-making and justifiability, the paper reframes the discussion around AI’s role in law and provides a compelling argument for AI’s potential to aid in legal reasoning.

The authors delve into the intricacies of legal decision-making, highlighting the contrast between our traditional expectations and the realities of legal reasoning. They argue that while we expect legal decisions to be based on clearly identifiable structures, algorithmic operations on beliefs, and classical logic, the cognitive science research paints a contrasting picture. The authors further suggest that most legal decisions emerge unconsciously, lack a recognizable structure, and are often influenced by emotional reactions and social training. This observation paves the way for a paradigm shift, suggesting that rather than the process, it is the justifiability of the decision ex post that is paramount.

The authors propose a two-module AI system, one intuitive and the other rational. The intuitive module, powered by machine learning, recognizes patterns from large datasets and makes decisions. The rational module, grounded in logic, does not make decisions but justifies those made by the intuitive module. In this framework, AI can be seen as rational if an acceptable justification can be provided for its decisions, despite their unpredictability. This interesting intertwining of machine learning and logic reshapes our understanding of AI’s role in legal decision-making.

This paper touches upon broader philosophical issues surrounding consciousness, rationality, and decision-making. By arguing for a shift from a process-oriented to a result-oriented evaluation of decision-making, the authors challenge the traditional Kantian perspective. The proposed model, in which an AI’s decisions are assessed based on their post-hoc justifiability, aligns more closely with consequentialist philosophy. This emphasis on the end result rather than the means to reach it further stimulates the ongoing debate on the ethical implications of AI use and the re-evaluation of long-held philosophical tenets in the face of technological advancements.

Future research could explore various facets of this proposed two-module AI system, particularly the interplay and potential conflicts between the intuitive and rational modules. Questions around what constitutes an “acceptable justification” in various legal contexts also demand further exploration. Additionally, research could investigate how this approach to AI in law would intersect with other legal principles, such as fairness, transparency, and due process. Ultimately, the paper presents a compelling case for rethinking the role and evaluation of AI in legal decision-making, opening up intriguing possibilities for future philosophical and legal discourse.

Abstract

This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.

The black box problem revisited. Real and imaginary challenges for automated legal decision making
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(Featured) Bare statistical evidence and the legitimacy of software-based judicial decisions

Bare statistical evidence and the legitimacy of software-based judicial decisions

Eva Schmidt et al. explore the question of whether evidence provided by software systems can serve as a legitimate basis for judicial decisions, focusing on two primary cases: recidivism predictions and DNA cold hit cases. The authors approach this question by analyzing the nature of bare statistical evidence and its relation to individualized evidence. They argue that while bare statistical evidence is generally considered insufficient to meet the standard of proof in criminal and civil cases, software-generated evidence can be individualized and, thus, meet this standard of proof under certain conditions.

In the case of recidivism predictions, the authors discuss the use of software systems such as COMPAS, which rely on bare statistical evidence to estimate the risk of an individual reoffending. They argue that for a sentence to be just and have the potential to serve as an incentive, it must be based on the specific features of the individual concerned, rather than solely on general features of a group that they belong to, which may correlate with high recidivism risk. The authors maintain that bare statistical evidence alone is insufficient for sentencing decisions.

Regarding DNA cold hit cases, the authors propose that statistical evidence generated by software systems like TrueAllele can be individualized through abductive reasoning or inference to the best explanation when it comes to cases of extreme probability. They argue that when the best explanation for the evidence is that the defendant is the source of the crime scene DNA, the evidence can be considered individualized and, thus, meet the standard of proof for criminal cases. This aligns with the normic account of individualized evidence, which posits that the best explanation of a piece of evidence is also the most normal explanation.

The authors’ analysis raises broader philosophical questions concerning the nature of evidence, the role of statistical reasoning in judicial decision-making, and the ethical implications of using software systems in the courtroom. It highlights the importance of distinguishing between different types of support, such as abductive (normic) support and probabilistic support, and of understanding the connections and disconnections between these concepts. Moreover, the paper touches on issues related to transparency, explainability, and fairness in the use of software systems as decision-making aids in the legal context.

Future research could further explore the implications of using software systems for other types of legal evidence and decision-making processes, as well as the ethical and epistemological challenges that these systems pose. Additionally, investigating the relationship between individualized evidence and statistical reasoning could shed light on the nature of evidence itself and the standards of proof required in various legal contexts. Finally, future work could focus on the development of guidelines and best practices for the implementation and evaluation of software systems in the courtroom, addressing issues such as transparency, explainability, and the appropriate weighting of statistical and individualized evidence.

Abstract

Can the evidence provided by software systems meet the standard of proof for civil or criminal cases, and is it individualized evidence? Or, to the contrary, do software systems exclusively provide bare statistical evidence? In this paper, we argue that there are cases in which evidence in the form of probabilities computed by software systems is not bare statistical evidence, and is thus able to meet the standard of proof. First, based on the case of State v. Loomis, we investigate recidivism predictions provided by software systems used in the courtroom. Here, we raise problems for software systems that provide predictions that are based on bare statistical evidence. Second, by examining the case of People v. Chubbs, we argue that the statistical evidence provided by software systems in cold hit DNA cases may in some cases suffice for individualized evidence, on a view on which individualized evidence is evidence that normically supports the relevant proposition (Smith, in Mind 127:1193–1218, 2018).

Bare statistical evidence and the legitimacy of software-based judicial decisions
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(Featured) A phenomenological perspective on AI ethical failures: The case of facial recognition technology

A phenomenological perspective on AI ethical failures: The case of facial recognition technology

Yuni Wen and Matthias Holweg conduct a philosophical analysis of the responses of four prominent technology firms to the ethical concerns surrounding the use and development of facial recognition technology. The article meticulously delves into the controversies surrounding Amazon, IBM, Microsoft, and Google, as they grapple with public backlash and stakeholder disapproval. By analyzing these cases, the authors elucidate four distinct strategies that these organizations employ to mitigate potential reputation loss: deflection, improvement, validation, and pre-emption. They astutely highlight the spectrum of these responses, ranging from the most accommodative to the most defensive approach.

The authors propose three possible antecedents that may determine an organization’s response strategy to controversial AI technology: the financial importance of the technology to the company, the strategic importance of the technology to the company’s product and service offerings, and the degree to which the controversial technology violates the company’s stated public values. Through their examination of the facial recognition controversies and the strategies employed by the tech giants, they provide invaluable insights into how these factors contribute to shaping the responses of companies facing ethical dilemmas in AI technology.

Although the article’s primary focus is on large technology firms, it acknowledges the limitations of its analysis and encourages further research on small and medium-sized firms, non-profit organizations, public sector organizations, and other entities that may intentionally misuse AI for nefarious purposes. It also highlights the need for future research to consider the interplay between organizational strategies and the varying global regulatory landscape concerning AI technology, given the diverse policy initiatives and regional differences.

The article not only contributes to the ongoing discourse about AI ethics but also resonates with broader philosophical debates on corporate social responsibility and the role of organizations in shaping a just and equitable society. In an era of unprecedented technological advances and heightened awareness of ethical concerns, this research raises pertinent questions about the duties and responsibilities that companies bear in addressing the potential social and moral implications of their products and services. It underscores the challenge that organizations face in balancing financial interests and strategic goals with ethical imperatives and societal expectations.

To enrich our understanding of the complex interplay between organizations and AI ethics, future research could explore the processes through which companies develop and implement their response strategies, with an emphasis on the role of leadership, organizational culture, and internal and external stakeholder dynamics. Moreover, investigating how these strategies evolve over time and assessing their effectiveness in addressing public concerns could provide valuable insights into best practices for organizations navigating the ethical minefield of AI technology. Ultimately, this line of inquiry would contribute significantly to our understanding of how corporations can foster the responsible development and use of AI, ensuring that its potential benefits are realized while mitigating its ethical risks.

Abstract

As more and more companies adopt artificial intelligence to increase the efficiency and effectiveness of their products and services, they expose themselves to ethical crises and potentially damaging public controversy associated with its use. Despite the prevalence of AI ethical problems, most companies are strategically unprepared to respond effectively to the public. This paper aims to advance our empirical understanding of company responses to AI ethical crises by focusing on the rise and fall of facial recognition technology. Specifically, through a comparative case study of how four big technology companies responded to public outcry over their facial recognition programs, we not only demonstrated the unfolding and consequences of public controversies over this new technology, but also identified and described four major types of company responses—Deflection, Improvement, Validation, and Pre-emption. These findings pave the way for future research on the management of controversial technology and the ethics of AI.

A phenomenological perspective on AI ethical failures: The case of facial recognition technology
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(Featured) Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds

Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds

David M. Lyreskog et al. outline and analyze the ethical implications and conceptual challenges surrounding technologically enabled collective minds (TCMs). The paper proposes four main categories to help understand the varying levels of unity and directionality in TCMs: DigiMinds, UniMinds, NetMinds, and MacroMinds. Each category has its own set of unique ethical challenges, which the authors argue should be considered in a multidimensional manner to effectively address the complexities of agency and responsibility in TCMs.

DigiMinds are minimally direct, minimally directional interfaces, such as virtual avatars in digital spaces, where individuals are separate but can communicate through digital means. UniMinds are low-directional, highly direct interfaces, in which senders can communicate and manipulate neuronal behavior in receivers. This category is further divided into Weak UniMinds, which are collaborative interfaces, and Strong UniMinds, which create an entirely new joint entity. NetMinds, on the other hand, are minimally direct, highly directional tools that facilitate vast networks of collective thinking, such as swarm intelligence applications. Lastly, MacroMinds are maximally direct and maximally directed tools, with multiple participants connected through interfaces that allow direct neuronal transmissions in all directions. This category is also subdivided into Weak MacroMinds, which are collaborative interfaces, and Strong MacroMinds, which create new joint entities.

The authors argue that each of these four categories challenges our current understanding of collective and joint actions, urging a reevaluation of the conceptual and ethical frameworks that guide our thinking. For instance, UniMinds and MacroMinds raise questions about identity, agency, and responsibility when a new entity emerges from the connected individuals. In NetMinds, the role of the computer as an organizer poses challenges concerning responsibility and transparency. The paper suggests that instead of a binary approach, future ethical analyses should consider the technological specifications, the domain in which the TCM is deployed, and the reversibility of joining a Collective Mind.

This research taps into broader philosophical issues surrounding the nature of identity, consciousness, and agency in an increasingly interconnected world. As we move towards a future where technology not only extends our cognitive capabilities but also has the potential to fundamentally reshape our understanding of what it means to be an individual, we are forced to reevaluate our traditional conceptions of personhood, ethics, and responsibility. TCMs challenge the philosophical foundations of agency and responsibility, as well as the ways in which we understand and define collective versus individual actions and decisions.

To further explore the ethical and conceptual challenges of TCMs, future research could delve deeper into the practical implications of integrating these technologies into various aspects of our society, such as healthcare, education, governance, and commerce. Avenues for research might include examining the legal and policy ramifications of TCMs, the potential for power imbalances in such systems, and the implications for privacy and autonomy. Additionally, scholars could investigate how the experience of participating in a TCM might impact our sense of self and our relationships with others. By addressing these areas, we can move towards a more comprehensive understanding of the complex ethical landscape of technologically enabled collective minds and prepare ourselves for the challenges that lie ahead.

Abstract

A growing number of technologies are currently being developed to improve and distribute thinking and decision-making. Rapid progress in brain-to-brain interfacing and swarming technologies promises to transform how we think about collective and collaborative cognitive tasks across domains, ranging from research to entertainment, and from therapeutics to military applications. As these tools continue to improve, we are prompted to monitor how they may affect our society on a broader level, but also how they may reshape our fundamental understanding of agency, responsibility, and other key concepts of our moral landscape.

In this paper we take a closer look at this class of technologies – Technologies for Collective Minds – to see not only how their implementation may react with commonly held moral values, but also how they challenge our underlying concepts of what constitutes collective or individual agency. We argue that prominent contemporary frameworks for understanding collective agency and responsibility are insufficient in terms of accurately describing the relationships enabled by Technologies for Collective Minds, and that they therefore risk obstructing ethical analysis of the implementation of these technologies in society. We propose a more multidimensional approach to better understand this set of technologies, and to facilitate future research on the ethics of Technologies for Collective Minds.

Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds
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(Featured) Philosophical foundation of the right to mental integrity in the age of neurotechnologies

Philosophical foundation of the right to mental integrity in the age of neurotechnologies

Andrea Lavazza and Rodolfo Giorgi argue that the development and use of neurotechnology present new challenges to privacy, mental integrity, and autonomy, necessitating a reevaluation of existing ethical frameworks and the introduction of new rights to protect individuals against potential threats to these fundamental aspects of human dignity.

The authors first examine the concept of intentionality, highlighting its importance for understanding the subjective and first-person perspective of mental experiences. They argue that neurotechnology poses a risk to intentionality by potentially manipulating or monitoring individuals’ mental processes. This risk extends to the first-person perspective, as the development of brain-computer interfaces could blur the boundaries between the self and external entities, undermining the sense of ownership and agency that is integral to personal identity.

The paper further discusses the significance of autonomy in moral decision-making and identity-building. Drawing upon moral constructivism, the authors contend that privacy and mental integrity are crucial for individuals to engage in the process of moral self-determination. They assert that neurotechnology has the potential to interfere with this process, leading to misinterpretations of mental states and behaviors, and ultimately hindering individuals’ ability to make autonomous choices and form their own moral judgments.

This research contributes to broader philosophical issues by shedding light on the complex relationship between emerging neurotechnology and fundamental aspects of human nature, such as intentionality, autonomy, and personal identity. It underscores the importance of establishing a right to mental integrity in order to protect these essential elements of human dignity in a world increasingly influenced by advancements in neuroscience and technology.

For future research, it is vital to investigate the ethical and legal implications of the right to mental integrity, delineating its scope and limitations in relation to neurotechnology. This may include examining the potential consequences of different types of interventions, ranging from non-invasive monitoring to direct manipulation of brain states. Additionally, interdisciplinary collaboration between philosophers, neuroscientists, and policymakers will be crucial to developing comprehensive ethical guidelines that address the profound challenges posed by the ongoing development and implementation of neurotechnology in various domains of human life. By bridging these disciplines, we can ensure that the protection of mental integrity remains a central consideration as we navigate the uncharted territory of human-machine interaction.

Abstract

Neurotechnologies broadly understood are tools that have the capability to read, record and modify our mental activity by acting on its brain correlates. The emergence of increasingly powerful and sophisticated techniques has given rise to the proposal to introduce new rights specifically directed to protect mental privacy, freedom of thought, and mental integrity. These rights, also proposed as basic human rights, are conceived in direct relation to tools that threaten mental privacy, freedom of thought, mental integrity, and personal identity. In this paper, our goal is to give a philosophical foundation to a specific right that we will call right to mental integrity. It encapsulates both the classical concepts of privacy and non-interference in our mind/brain. Such a philosophical foundation refers to certain features of the mind that hitherto could not be reached directly from the outside: intentionality, first-person perspective, personal autonomy in moral choices and in the construction of one’s narrative, and relational identity. A variety of neurotechnologies or other tools, including artificial intelligence, alone or in combination can, by their very availability, threaten our mental integrity. Therefore, it is necessary to posit a specific right and provide it with a theoretical foundation and justification. It will be up to a subsequent treatment to define the moral and legal boundaries of such a right and its application.

Philosophical foundation of the right to mental integrity in the age of neurotechnologies
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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) AI Moral Enhancement: Upgrading the Socio-Technical System of Moral Engagement

AI Moral Enhancement: Upgrading the Socio-Technical System of Moral Engagement

Richard Volkman and Katleen Gabriels critically examine current approaches to AI moral enhancement and propose a new model that more closely aligns with the reality of moral progress as a socio-technical system. The paper begins by discussing two main approaches to AI moral enhancement: the exhaustive approach, which aims to program AI systems with complete moral knowledge, and the auxiliary approach, which seeks to use AI as a tool to assist humans in moral decision-making. The authors argue that the exhaustive approach is overly ambitious and unattainable, while the auxiliary approach, as exemplified by Lara and Deckers’ Socratic Interlocutor, lacks the depth and nuance necessary for genuine moral engagement.

Instead, the authors propose an alternative model of AI moral enhancement that emphasizes the importance of moral diversity, ongoing dialogue, and the cultivation of practical wisdom. Their model envisions a modular system of AI “mentors”, each embodying a distinct moral perspective, engaging in conversation with one another and with the user. This system would more accurately represent the complex, evolving socio-technical process of moral progress and would be safer and more effective than the existing proposals for AI moral enhancement.

The authors address potential objections to their proposal, arguing that the goal of moral enhancement should not be to transcend human limitations but to engage more deeply with our moral thinking. They emphasize that their approach to moral enhancement is not aimed at simplifying the process of moral improvement but at making us more skilled in the ways of practical wisdom. They conclude that their proposal represents a path to genuine moral enhancement that is more achievable and less fraught with risk than previous approaches.

This research contributes to broader philosophical discussions about the nature and scope of moral progress, the role of technology in moral enhancement, and the limits of human rationality. By engaging with these issues, the paper not only critiques existing proposals but also highlights the importance of considering the historical, social, and technological dimensions of moral inquiry. In doing so, it raises questions about the extent to which AI can and should be involved in human moral development, and how best to navigate the potential risks and benefits associated with such involvement.

As for future research, several avenues present themselves. First, it would be fruitful to explore the development of these AI “mentors” in more detail, focusing on the technical and ethical challenges associated with creating AI systems that embody diverse moral perspectives. Additionally, empirical studies could be conducted to assess the effectiveness of such AI mentors in promoting moral enhancement among users. Finally, interdisciplinary research could be undertaken to better understand the complex relationship between AI, moral enhancement, and broader social and cultural dynamics, in order to ensure that future AI moral enhancement efforts are both safe and effective.

Abstract

Several proposals for moral enhancement would use AI to augment (auxiliary enhancement) or even supplant (exhaustive enhancement) human moral reasoning or judgment. Exhaustive enhancement proposals conceive AI as some self-contained oracle whose superiority to our own moral abilities is manifest in its ability to reliably deliver the ‘right’ answers to all our moral problems. We think this is a mistaken way to frame the project, as it presumes that we already know many things that we are still in the process of working out, and reflecting on this fact reveals challenges even for auxiliary proposals that eschew the oracular approach. We argue there is nonetheless a substantial role that ‘AI mentors’ could play in our moral education and training. Expanding on the idea of an AI Socratic Interlocutor, we propose a modular system of multiple AI interlocutors with their own distinct points of view reflecting their training in a diversity of concrete wisdom traditions. This approach minimizes any risk of moral disengagement, while the existence of multiple modules from a diversity of traditions ensures pluralism is preserved. We conclude with reflections on how all this relates to the broader notion of moral transcendence implicated in the project of AI moral enhancement, contending it is precisely the whole concrete socio-technical system of moral engagement that we need to model if we are to pursue moral enhancement.

AI Moral Enhancement: Upgrading the Socio-Technical System of Moral Engagement
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(Featured) Machine Ethics: Do Androids Dream of Being Good People?

Machine Ethics: Do Androids Dream of Being Good People?

Gonzalo Génova, Valentín Moreno, and M. Rosario González explore the possibility and limitations of teaching ethical behavior to artificial intelligence. The paper delves into two main approaches to teaching ethics to machines: explicit ethical programming and learning by imitation. It highlights the difficulties faced by each approach and discusses the implications and potential issues surrounding the application of machine learning to ethical issues.

The authors begin by examining explicit ethical programming, such as Asimov’s Three Laws, and discuss the challenges involved in foreseeing the consequences of an act, as well as the necessity of having an explicit goal for ethical behavior. The second approach, learning by imitation, involves machines observing the behavior of experts or a majority in order to emulate them. The paper also discusses the Moral Machine experiment by MIT, which aimed to teach machines to make moral decisions based on the preferences of the majority.

Despite the potential of machine learning techniques, the authors argue that both approaches fail to capture the essence of genuine ethical thinking in human beings. They emphasize that ethics is not about following a code of conduct or imitating the behavior of others, but rather about critical thinking and the formation of one’s own conscience. The paper concludes by questioning whether machines can truly learn ethics like humans do, suggesting that current methods of teaching ethics to machines are inadequate for capturing the complexity of human ethical life.

The research presented in the paper raises important philosophical questions about the nature of ethics and the role of machines in our ethical lives. It challenges the instrumentalist and reductionist approaches to ethics, which view ethical values as computable or reducible to a set of rules. By highlighting the limitations of these approaches, the paper invites us to reconsider the importance of value rationality and the recognition of the uniqueness and unrepeatable nature of human beings in ethical considerations.

In light of these findings, future research could explore alternative approaches to teaching ethics to machines that go beyond mere rule-following or imitation. This could involve the development of novel machine learning techniques that foster critical thinking and the ability to reason with values without reducing them to numbers. Additionally, interdisciplinary collaboration between philosophers, AI researchers, and ethicists could further enrich our understanding of the ethical dimensions of artificial intelligence and help to develop AI systems that not only do the right thing but also respect the complexity and richness of human ethical life.

Abstract

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.

Machine Ethics: Do Androids Dream of Being Good People?