Tag: Artificial Intelligence

Posted on

(Featured) Beyond the hype: ‘acceptable futures’ for AI and robotic technologies in healthcare

Beyond the hype: ‘acceptable futures’ for AI and robotic technologies in healthcare

Giulia De Togni et al. delve into the complex dynamics of technoscientific expectations surrounding the future of artificial intelligence (AI) and robotic technologies in healthcare. By focusing on surgery, pathology, and social care, they examine the strategies employed by scientists, clinicians, and other stakeholders to navigate and construct visions of an AI-driven future in healthcare. The authors illustrate the challenges faced by these stakeholders, who must balance promissory visions with more realistic expectations, while acknowledging the performative power of high expectations in attracting investment and resources.

The participants in the study engage in a balancing act between high and low expectations, drawing boundaries to maintain credibility for their research and practice while distancing themselves from the hype. They recognize that over-optimistic visions may create false hope and unrealistic expectations of performance, potentially harming AI and robotics research through deflated investment if the outcomes fail to match expectations. The authors demonstrate how the stakeholders negotiate the tension between sustaining and nurturing the hype while calling for the recalibration of expectations within an ethically and socially responsible framework.

Central to the participants’ visions of acceptable futures is the changing nature of human-machine relationships. Through balancing different social, ethical, and technoscientific demands, the participants articulate futures that are perceived as ethically and socially acceptable, as well as realistically achievable. They frame their articulations of both the present and future potential and limitations of AI and robotics technologies within an ethics of expectations that position normative considerations as central to how these expectations are expressed.

This research article contributes to broader philosophical debates concerning the role of expectations and imaginaries in shaping our understanding of technoscientific innovation, human-machine relationships, and the ethics of care. By exploring the dynamic interplay between these factors, the authors shed light on how the future of AI and robotics in healthcare is being constructed and negotiated. This study resonates with key themes in the philosophy of futures studies, including the co-constitution of technological visions and sociotechnical imaginaries, the performativity of expectations, and the ethical dimensions of forecasting and envisioning the future.

To further enrich our understanding of these complex dynamics, future research could explore the perspectives of additional stakeholders, such as patients and policymakers, to gain a more comprehensive picture of the expectations surrounding AI and robotics in healthcare. Additionally, cross-cultural and comparative studies could reveal how different cultural contexts and healthcare systems influence expectations and acceptance of these technologies. Ultimately, by continuing to examine the societal implications of AI and robotic technologies, including their impact on patient autonomy, privacy, and the human aspects of care, scholars can contribute to a more nuanced and ethically responsible vision of the future of healthcare.

Abstract

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically ‘acceptable futures’ framed by an ‘ethics of expectations.’ This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies.

Beyond the hype: ‘acceptable futures’ for AI and robotic technologies in healthcare
Posted on

(Featured) Machine learning in bail decisions and judges’ trustworthiness

Machine learning in bail decisions and judges’ trustworthiness

Alexis Morin-Martel navigates the intricate landscape of judicial decision-making and advances the concept of Judge Assistance Systems (JAS), proposing it as a tool for enhancing the trustworthiness of judges in bail decisions. The argument is grounded in the relational theory of procedural justice, which emphasizes the role of trust, voice, neutrality, and respect in the administration of justice. The research underpins its analysis through an exploration of the nuanced terrain of trustworthiness, distinguishing between actual and rich trustworthiness, and articulating the potential role of JAS in amplifying both.

The author leverages the empirical study by Kleinberg et al. (2017a) to illustrate how JAS, equipped with complex algorithms, can assist judges in making more precise bail decisions, thereby enhancing their actual trustworthiness. A key idea espoused is the potential for JAS to act as a check on judicial decision-making, allowing judges to reconsider decisions that deviate significantly from statistical norms. However, the author acknowledges that the implementation of JAS should not undermine the principle of voice, one of the pillars of relational justice, ensuring that defendants have the opportunity to influence the decision-making process.

Further, the study takes into account the perceived trustworthiness of judges when using a JAS. It acknowledges the inherent public skepticism towards algorithmic decisions, often due to their perceived opacity. The argument is made that focusing on accuracy, rather than transparency, of these algorithms is more likely to enhance perceived trustworthiness. Importantly, the author suggests that regular audits within legal institutions could effectively monitor the accuracy of JAS, thus reinforcing public trust over time. However, the author admits that while the ‘voice’ and ‘neutrality’ criteria could likely be met by JAS, its ability to meet the ‘respect’ requirement remains uncertain and needs further examination.

The research article finds a nexus with broader philosophical themes, particularly those concerning human-machine interaction and the ethical implications of algorithmic decision-making. The proposal of JAS as a tool to enhance judicial trustworthiness is reflective of the broader trend towards technocratic governance. This trend raises critical questions about the balance between human judgment and algorithmic precision, and the philosophical implications of delegating traditionally human tasks to artificial intelligence. Moreover, the emphasis on accuracy over transparency in JAS echoes the larger debate on the ethical trade-offs in AI applications, especially in high-stake public decisions.

Future research could explore several intriguing avenues. The extension of JAS to other areas of judicial decision-making, beyond bail decisions, could be considered. Studies could also focus on the development of more transparent and interpretable models without compromising accuracy, addressing public distrust of ‘black box’ algorithms. Furthermore, future research might investigate the potential impact of JAS on other aspects of the relational theory of procedural justice, particularly the ‘respect’ requirement. Lastly, empirical studies evaluating the effectiveness and reliability of JAS in real-world court settings could provide valuable insights into the practicality of implementing such systems.

Abstract

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness.

Machine learning in bail decisions and judges’ trustworthiness
Posted on

(Featured) In Conversation with Artificial Intelligence: Aligning language Models with Human Values

In Conversation with Artificial Intelligence: Aligning language Models with Human Values

Atoosa Kasirzadeh and Iason Gabriel embark on an ambitious analysis of how large-scale conversational agents, such as AI language models, can be better designed to align with human values. The premise of the article is grounded in the philosophy of language and pragmatics, employing Gricean maxims and Speech Act Theory to establish the importance of context and cooperation in achieving effective and ethical linguistic communication. The authors underscore the necessity of considering pragmatic norms and concerns in the design of conversational agents and illustrate their proposition through three discursive domains: science, civic life, and creative exchange.

The authors present a novel approach, suggesting the operationalization of Gricean maxims of quantity, quality, relation, and manner, to aid in cooperative communication between humans and AI. They also emphasize the diversity of utterances, asserting that there is no single universal condition of validity that applies to all. Instead, the validity of utterances often depends on different sorts of truth conditions which require different methodologies for substantiation, based on context-specific criteria of validity. They further stress the centrality of contextual information in the design of ideal conversational agents and highlight the need for research to theorise and measure the difference between the literal and contextual meaning of utterances.

The authors also delve into the implications of their analysis for future research into the design of conversational agents. They discuss the potential for anthropomorphisation of conversational agents and the constraints that might be imposed on them. They note that while anthropomorphism can sometimes be consistent with the creation of value-aligned agents, there are situations where it might be undesirable or inappropriate. They also advocate for the exploration of the potential for conversational agents to facilitate more robust and respectful conversations through context construction and elucidation. Lastly, they suggest that their analysis could be used to evaluate the quality of interactions between conversational agents and users, providing a framework for refining both human and automatic evaluation of conversational agent performance.

The research article resonates with broader philosophical themes, particularly those concerning the interplay between technology and society. It touches upon the ethical dimensions of AI, hinting at the moral responsibility of designing AI systems that align with human values and norms. The exploration of Gricean maxims and Speech Act Theory in the context of AI conversational agents provides a unique blend of AI ethics, philosophy of language, and pragmatics, reflecting the interdisciplinary nature of contemporary AI research. In doing so, the article stimulates dialogue about the role of AI in shaping our social and communicative practices, challenging conventional boundaries between humans and machines, and highlighting the potential of AI as a tool for fostering effective and ethically sound communication.

In terms of future avenues of research, the authors’ analysis opens up a myriad of possibilities. First, while the paper focuses primarily on the English language, a fruitful direction of research could involve the exploration of norms and pragmatics in other languages, thereby ensuring the cultural inclusivity and sensitivity of AI systems. Second, the proposed alignment of AI conversational agents with Gricean maxims and discursive ideals could be further operationalized and tested empirically to assess its effectiveness in real-world scenarios. Third, the article alludes to the potential of AI in fostering more robust and respectful conversations, which suggests an opportunity to investigate how AI can play an active role in shaping discourse norms and facilitating constructive dialogues. Lastly, the authors’ work can be further enriched by drawing from other sociological and philosophical traditions, such as Luhmann’s system theory or Latour’s actor-network theory, to offer a more comprehensive and nuanced understanding of the complex interplay between AI, language, and society.

Abstract

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values.

In Conversation with Artificial Intelligence: Aligning language Models with Human Values
Posted on

(Featured) Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use

Christoph Durt, Thomas Fuchs, and Tom Froese investigate the astonishing capacities of Large Language Models (LLMs) to mimic human-like responses. They begin by acknowledging the unprecedented feats of these models, particularly GPT-3, which have led some to assert that they possess common-sense reasoning and even sentience. They caution, however, that these claims often overlook the instances where LLMs fail to produce sensical responses. Even as the models evolve and mitigate some of these limitations, the authors urge circumspection regarding the attribution of understanding and sentience to these systems.

The authors argue that the progress of LLMs invites a reassessment of long-standing philosophical debates about the limits of AI. The authors challenge the view, expressed by philosophers such as Hubertus Dreyfus, that AI is inherently incapable of understanding meaning. Given the emergent linguistic capabilities of these models, they query whether these advancements warrant attributing understanding to the computational system. Contrary to Dreyfus’s assertion that any formal system cannot be directly sensitive to the relevance of its situation, the authors propose that LLMs seem to exhibit this sensitivity in a pragmatic sense.

While the article explores the philosophical debates surrounding AI understanding and sentience, it does not definitively conclude whether LLMs truly understand or are sentient. The authors suggest that the human-like behaviour exhibited by these models may lead to the inference of a human-like mind. However, they argue that more nuanced and empirically informed positions are required. The authors further advocate for a more comprehensive assessment of LLM output, rather than relying on selective instances of impressive performance.

This research brings into focus the broader philosophical implications of our interaction with AI, particularly the ontological and epistemological assumptions we make when interacting with LLMs. The debate surrounding AI sentience and understanding illuminates the complexities inherent in defining consciousness and understanding, a philosophical quandary that dates back to Descartes and beyond. It forces us to interrogate the nature of understanding – is it a purely human phenomenon, or can it be replicated, even surpassed, by silicon-based entities? Moreover, it challenges our anthropocentric views of cognition and compels us to consider alternate forms of intelligence and understanding.

Looking forward, the study of AI and philosophy would benefit from an even deeper exploration of these questions. More empirical research is needed to understand the extent and limitations of LLMs’ capacities. Concurrently, philosophical inquiry can help define and refine the metrics by which we measure AI understanding and sentience. As we delve further into the AI era, it is crucial that we continue to scrutinize and challenge our assumptions about AI capabilities, not only to enhance our technological advancements but also to enrich our philosophical understanding of the world.

Abstract

Large language models such as ChatGPT are deep learning architectures trained on immense quantities of text. Their capabilities of producing human-like text are often attributed either to mental capacities or the modeling of such capacities. This paper argues, to the contrary, that because much of meaning is embedded in common patterns of language use, LLMs can model the statistical contours of these usage patterns. We agree with distributional semantics that the statistical relations of a text corpus reflect meaning, but only part of it. Written words are only one part of language use, although an important one as it scaffolds our interactions and mental life. In human language production, preconscious anticipatory processes interact with conscious experience. Human language use constitutes and makes use of given patterns and at the same time constantly rearranges them in a way we compare to the creation of a collage. LLMs do not model sentience or other mental capacities of humans but the common patterns in public language use, clichés and biases included. They thereby highlight the surprising extent to which human language use gives rise to and is guided by patterns.

Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use
Posted on

(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
Posted on

(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
Posted on

(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
Posted on

(Featured) Algorithmic Nudging: The Need for an Interdisciplinary Oversight

Algorithmic Nudging: The Need for an Interdisciplinary Oversight

Christian Schmauder et al. critically assess the implications and risks of employing “black box” AI systems for the development and implementation of personalized nudges in various domains of life. They begin by outlining the power and promise of algorithmic nudging, drawing attention to how AI-driven nudges could bring about widespread benefits in areas such as health, finance, and sustainability. However, they contend that outsourcing nudging to opaque AI systems poses challenges in terms of understanding the underlying reasons for their effectiveness and addressing potential unintended consequences.

The authors delve deeper into the nuances of algorithmic nudging by examining the role of personalized advice in influencing human decision-making. They highlight a key concern that arises when AI systems attempt to maximize user satisfaction: the tendency of the algorithms to exploit cognitive biases in order to achieve desired outcomes. Consequently, the effectiveness of the AI-developed nudges might come at the cost of truthfulness, ultimately undermining the very goals they were designed to achieve.

To address this issue, the authors advocate for the need to look “under the hood” of AI systems, arguing that understanding the underlying cognitive processes harnessed by these systems is crucial for mitigating unintended side effects. They emphasize the importance of interdisciplinary collaboration between computer scientists, cognitive scientists, and psychologists in the development, monitoring, and refinement of AI systems designed to influence human decision-making.

The authors’ exploration of the limitations and risks of “black box” AI nudges raises broader philosophical concerns, particularly in relation to the ethics of autonomy, transparency, and accountability. These concerns call into question the balance between leveraging AI-driven nudges to benefit society and preserving individual autonomy and freedom of choice. Furthermore, the analysis highlights the tension between relying on AI’s predictive power and fostering a deeper understanding of the mechanisms driving human behavior.

This paper provides a valuable foundation for future research on the ethical and philosophical implications of AI-driven nudging. Further investigation could delve into the possible approaches to designing more transparent and explainable AI systems, exploring how such systems might enhance, rather than hinder, human decision-making processes. Additionally, researchers could examine the moral responsibilities of AI developers and regulators, studying the ethical frameworks necessary to guide the development and deployment of AI nudges that respect human autonomy, values, and dignity. Ultimately, a deeper understanding of these complex philosophical questions will be instrumental in realizing the full potential of AI-driven nudges while safeguarding against their potential pitfalls.

Abstract

Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to “black box” AI systems means that the ultimate reasons for why such nudges work, that is, the underlying human cognitive processes that they harness, will often be unknown. In this paper, we unpack this concern by considering a series of examples and case studies that demonstrate how AI systems can learn to harness biases in human judgment to reach a specified goal. Drawing on an analogy in a philosophical debate concerning the methodology of economics, we call for the need of an interdisciplinary oversight of AI systems that are tasked and deployed to nudge human behaviours.

Algorithmic Nudging: The Need for an Interdisciplinary Oversight
Posted on

(Featured) Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Frank Ursin et al. investigate the ethical considerations associated with medical artificial intelligence (AI), particularly in the context of radiology. They emphasize the importance of implementing explainable AI (XAI) techniques to address epistemic and explanatory concerns that arise when AI is employed in medical decision-making. The authors outline a four-level approach to explicability, comprising disclosure, intelligibility, interpretability, and explainability, with each successive level representing an escalation in the level of detail and clarity provided to the patient or physician.

The authors argue that XAI has great potential in the medical field, and they present two examples from radiology to illustrate its practical applications. The first example involves the use of image inpainting techniques to generate sharper and more detailed saliency maps, which can help localize relevant regions within radiological images. The second example highlights the importance of natural language communication in XAI, where an image-to-text model is used to generate medical reports based on radiological images. These two examples demonstrate that incorporating XAI techniques in radiology can provide valuable insights and improved communication for medical practitioners and patients.

In the paper’s conclusion, the authors emphasize the need for a tailored approach to explicability that considers the needs of patients and the scope of medical decisions. They also advocate for the use of insights gained from medical AI ethics to re-evaluate established medical practices and confront biases in medical classification systems. By applying the four levels of explicability in a thoughtful manner, the authors posit that ethically defensible information processes can be established when utilizing medical AI.

This paper touches on broader philosophical issues related to the ethics of technology, medical autonomy, and the nature of trust in AI-driven decision-making. As AI becomes increasingly integrated into various domains of human activity, questions about transparency, fairness, and the moral implications of AI systems become paramount. This paper demonstrates the necessity of establishing an ethical framework for AI applications in healthcare, providing valuable insights that can be extended to other disciplines as well. By considering the complex interplay between AI-driven systems and human agents, the authors also underscore the importance of understanding how technological advancements impact the broader social fabric and the values we uphold as a society.

Future research in this area could explore the generalizability of the four-level approach to explicability in other medical domains or even non-medical contexts. Additionally, researchers may investigate how the incorporation of diverse perspectives in the development of AI systems and explainability techniques can mitigate the potential for biases and discriminatory outcomes. It would also be valuable to study how XAI can be adapted to the specific needs and preferences of individual patients or physicians, creating personalized approaches to explicability. Lastly, researchers may wish to assess the long-term impact of integrating XAI in medical practice, particularly in terms of patient satisfaction, physician trust, and overall quality of care.

Abstract

Definition of the problem

The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI?

Arguments

We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example.

Conclusion

We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?
Posted on

(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