The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum's capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders' ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for (...) morally permissible interactions between AI systems and those impacted by their functioning, and two sufficient conditions for realizing the ideal of meaningful benefit. We then contrast this ideal with several salient failure modes, namely, forms of social interactions that constitute unjustified paternalism, coercion, deception, exploitation and domination. The proliferation of incidents involving AI in high-stakes domains underscores the gravity of these issues and the imperative to take an ethics-led approach to AI systems from their inception. (shrink)

Modelling that exploits visual elements and information visualisation are important areas that have contributed immensely to understanding and the computerisation advancements in many domains and yet remain unexplored for the benefit of the law and legal practice. This paper investigates the challenge of modelling and expressing structures and processes in legislation and the law by using visual modelling and information visualisation (InfoVis) to assist accessibility of legal knowledge, practice and knowledge formalisation as a basis for legal AI. The paper uses (...) a subset of the well-defined Unified Modelling Language (UML) to visually express the structure and process of the legislation and the law to create visual flow diagrams called lawmaps, which form the basis of further formalisation. A lawmap development methodology is presented and evaluated by creating a set of lawmaps for the practice of conveyancing and the Landlords and Tenants Act 1954 of the United Kingdom. This paper is the first of a new breed of preliminary solutions capable of application across all aspects, from legislation to practice; and capable of accelerating development of legal AI. (shrink)

Context: The advent of a general artificial intelligence mechanism that learns like humans do would represent the realization of an old and major dream of science. It could be achieved by an artifact able to develop its own cognitive structures following constructivist principles. However, there is a large distance between the descriptions of the intelligence made by constructivist theories and the mechanisms that currently exist. Problem: The constructivist conception of intelligence is very powerful for explaining how cognitive development takes place. (...) However, until now, no computational model has successfully demonstrated the underlying mechanisms necessary to realize it. In other words, the artificial intelligence (AI) community has not been able to give rise to a system that convincingly implements the principles of intelligence as postulated by constructivism, and that is also capable of dealing with complex environments. Results: This paper presents the constructivist anticipatory learning mechanism (CALM), an agent learning mechanism based on the constructivist approach of AI. It is designed to deal dynamically and interactively with environments that are at the same time partially deterministic and partially observable. CALM can model the regularities experienced in the interaction with the environment, on the sensorimotor level as well, as by constructing abstract or high-level representational concepts. The created model provides the knowledge necessary to generate the agent behavior. The paper also presents the coupled agent environment system (CAES) meta-architecture, which defines a conception of an autonomous agent, situated in the environment, embodied and intrinsically motivated. Implications: The paper can be seen as a step towards a computational implementation of constructivist principles, on the one hand suggesting a further perspective of this refreshing movement on the AI field (which is still too steeped in a behaviorist influence and dominated by probabilistic models and narrow applied approaches), and on the other hand bringing some abstract descriptions of the cognitive process into a more concrete dimension, in the form of algorithms. Constructivist content: The connection of this paper with constructivism is the proposal of a computational and formally described mechanism that implements important aspects of the subjective process of knowledge construction based on key ideas proposed by constructivist theories. (shrink)

This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth. We first survey empirical examples of AI deception, discussing both special-use AI systems (including Meta's CICERO) built for specific competitive situations, and general-purpose AI systems (such as large language models). Next, we detail several risks from AI deception, such as fraud, election tampering, and losing (...) control of AI systems. Finally, we outline several potential solutions to the problems posed by AI deception: first, regulatory frameworks should subject AI systems that are capable of deception to robust risk-assessment requirements; second, policymakers should implement bot-or-not laws; and finally, policymakers should prioritize the funding of relevant research, including tools to detect AI deception and to make AI systems less deceptive. Policymakers, researchers, and the broader public should work proactively to prevent AI deception from destabilizing the shared foundations of our society. (shrink)

Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5’s legal reasoning and ChatGPT’s legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints (...) written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5’s legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors’ decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT’s drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM’s legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct. (shrink)

Having a satisfactory definition of behavioural deception is important for understanding several types of evolutionary questions. No definition offered in the literature so far is adequate on all fronts. After identifying characteristics that are important for a definition, a new definition of behavioural deception is offered. The new definition, like some other proposed attempts, relies on formal game-theoretic models of signalling. Unlike others, it incorporates explicit consideration of the population in which the potentially deceptive interactions occur. The general structure (...) of the definition satisfies many of the characteristics that were problematic for other definitions, and others are satisfied by explicitly incorporating information about the population in which interactions occur. Examples are given for chick begging in the Sir Philip Sidney game and other situations. 1 Introduction2 Deception in Formal Models2.1 Formal models of signalling2.2 Deception as dishonesty2.3 Deception as negative value of information2.4 Deception as misinformation with payoff conditions3 A Revised Definition3.1 Technical definitions3.2 Begging behaviour and the Sir Philip Sidney game3.3 ‘Universal’ deception and a possible objection3.4 One-on-one interactions3.5 Lucky errors4 Discussion and Conclusions. (shrink)

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show (...) that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. (shrink)

Until now, the mass spread of fake news and its negative consequences have implied mainly textual content towards a loss of citizens' trust in institutions. Recently, a new type of machine learning framework has arisen, Generative Adversarial Networks (GANs) – a class of deep neural network models capable of creating multimedia content (photos, videos, audio) that simulate accurate content with extreme precision. While there are several areas of worthwhile application of GANs – e.g., in the field of audio-visual production, human-computer (...) interactions, satire, and artistic creativity – their deceptive uses, at least as currently foreseeable, are just as numerous and worrying. The main concern is linked to the so-called “deepfakes”, fake images or videos that simulate real events with extreme precision. When trained on a human face, GANs can make the face assume hyper-realistic movements, expressions and (verbal and non-verbal) communication abilities. This technology poses an urgent threat to the governance of democratic processes concerning the production of public opinions and political discourses, with significant potential for reality-altering and disinformation. After a short introduction of their current technical state-of-the-art, in this paper we want to enquire the GANs` socio-technical system alongside different and intertwined philosophical accounts. Firstly, we will argue about the conditions that make perceived a GANs-generated content as trustworthy, arguing also about the general effects GANs might have on the perceived trustworthiness of individuals. Thereafter, we will discuss about the inadequacy to approach GANs only as perception-altering technology. Against this backdrop, we will propose a theoretical turn that considers the human-machine relationships of trustworthiness as elements of a broader hybrid socio-technical systems. This turn come up with political repercussions that we will discuss in the last part of the paper. (shrink)

The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projected that, in (...) the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena. (shrink)

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to (...) what a human might produce (meaningsemblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding. (shrink)

This paper explores the intersection of René Girard's mimetic theory and the algorithmic society, particularly in the context of the potential advent of Artificial General Intelligence (AGI). Girard's theory, which elucidates the dynamics of desire, rivalry, scapegoating, and the sacrificial crisis, provides a unique lens through which to examine the complexities of our relationship with AI and its role in the creation of the sacred. As individuals increasingly rely on AI recommendations, the distinction between personal choice and algorithmic manipulation becomes (...) less clear, raising concerns about the authenticity of cultural expressions and the role of algorithms in shaping cultural narratives. The triangular structure of desire, with AI as the model and individuals as the imitators, underscores the power of algorithms in this process. The sacrificial crisis, a key concept in Girard's theory, becomes a critical point of reflection in the algorithmic society. The exposure of the scapegoating mechanism reveals the destructive potential of algorithmic manipulation and calls for new forms of understanding, empathy, and non-violent solutions. This paper argues that recognizing the sacrificial crisis can prompt individuals and society to critically examine the impact of AI's influence, challenge the narratives it perpetuates, and reclaim agency in the face of algorithmic dominance. This paper further discusses the potential implications of the emergence of AGI, which could intensify the influence of algorithms on the creation of the sacred due to its advanced cognitive capabilities and deep understanding of human desires and behaviors. This could fuel a rapid evolution of the mimetic ecosystem, with profound implications for personal freedom, independent decision-making, and the formation and preservation of individual identity. This paper concludes by emphasizing the need for responsible algorithmic practices and ethical considerations to ensure that the creation of the sacred serves the common good in the algorithmic society. (shrink)

Despite the reality that self-learning artificial intelligence systems (SLAIS) are gaining in sophistication, humanity’s focus regarding SLAIS-human interactions are unnervingly centred upon transnational commercial sectors and, most generally, around issues of intellectual property law. But as SLAIS gain greater environmental interaction capabilities in digital spaces, or the ability to self-author code to drive their development as algorithmic models, a concern arises as to whether a system that displays a “deceptive” level of human-like engagement with users in our physical world ought (...) to be uniquely protected. Although many voices in the legal and technology realms have continued to argue against unique protections for digital entities, the fact at hand is that SLAIS design is becoming increasingly anthropomorphic so as to make these systems more capable of interacting with a wide range of (potentially) vulnerable populations—generally as a means to enhance these populations’ overall well-being. To frame this concern in a different way, the specific question at hand is whether a human’s “ownership” of such an advanced SLAIS is legal, considering that it (or they) may possess intelligence on par with a human or a convincing-enough display of such behaviour. Given that “ownership” over entities with (seemingly) intelligent behaviours consistent with human populations has been effectively banned by the international community, an examination into this subject and its implications is wholly necessary given humanity’s quest to exist solely in digital environments through whatever means possible. (shrink)

In artificial intelligence literature, “delusions” are characterized as the generation of unfaithful output from reliable source content. There is an extensive literature on computer-generated delusions, ranging from visual hallucinations, like the production of nonsensical images in Computer Vision, to nonsensical text generated by (natural) language models, but this literature is predominantly taxonomic. In a recent research paper, however, a group of scientists from DeepMind successfully presented a formal treatment of an entire class of delusions in generative AI models (i.e., (...) models based on a transformer architecture, both with and without RLHF—reinforcement learning with human feedback, such as BERT, GPT-3 or the more recent GPT-3.5), referred to as auto-suggestive delusions. Auto-suggestive delusions are not mere unfaithful output, but are self-induced by the transformer models themselves. Typically, these delusions have been subsumed under the concept of exposure bias, but exposure bias alone does not elucidate their nature. In order to address their nature, I will introduce a formal framework that clarifies the probabilistic delusions capable of explaining exposure bias in a broad manner. This will serve as the foundation for exploring auto-suggestive delusions in language models. Next, an examination of self- or auto-suggestive delusions will be undertaken, by drawing an analogy with the rule-following problematic from the philosophy of mind and language. Finally, I will argue that this comprehensive approach leads to the suggestion that transformers, large language models in particular, may develop in a manner that touches upon solipsism and the emergence of a private language, in a weak sense. (shrink)

Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three (...) AI models, including random forest, artificial neural network, and support vector machine for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process. (shrink)

This article presents a formal dialogue game for adjudication dialogues. Existing AI & law models of legal dialogues and argumentation-theoretic models of persuasion are extended with a neutral third party, to give a more realistic account of the adjudicator’s role in legal procedures. The main feature of the model is a division into an argumentation phase, where the adversaries plea their case and the adjudicator has a largely mediating role, and a decision phase, where the adjudicator decides the dispute (...) on the basis of the claims, arguments and evidence put forward in the argumentation phase. The model allows for explicit decisions on admissibility of evidence and burden of proof by the adjudicator in the argumentation phase. Adjudication is modelled as putting forward arguments, in particular undercutting and priority arguments, in the decision phase. The model reconciles logical aspects of burden of proof induced by the defeasible nature of arguments with dialogical aspects of burden of proof as something that can be allocated by explicit decisions on legal grounds. (shrink)

Citizenship is usually seen as a product of modern nation-states, or of other political entities which possess institutional infrastructures and political systems capable of producing a coherent framework that defines the relationship between that system and its members. In this paper, we show that an early system of modern citizenship was created in the absence of a formal state, notably by the cultural elite of a stateless nation. The Polish case illustrates that an elite may become a dominant class (...) in the given society only later, and institutionalize that early citizenship system within the framework of a newly founded state. As a result of the legacy of the emergence of citizenship predating the restoration of statehood, the contemporary Polish citizenship model is influenced by a strong and largely overlooked cultural component that emerged at the turn of the 19th century. This model uses the figure of the intelligentsia member as its ideal citizen. Despite the dramatic political and economic changes in the decades which have passed since its emergence, this cultural frame, which was institutionalized during the interwar period, still defines the key features of the Polish citizenship model. Consequently, we argue that the culturalization of citizenship is hardly a new phenomenon. It can be seen as a primary mechanism in the formation of civic polities within the imperial context. Moreover, it shows that such processes can have many ambiguous aspects as far as their Orientalizing forces of exclusion are concerned. (shrink)

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when (...) advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services (“OASIS”) towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat. (shrink)

New types of artificial intelligence products are gradually transferring to voice interaction modes with the demand for intelligent products expanding from communication to recognizing users' emotions and instantaneous feedback. At present, affective acoustic models are constructed through deep learning and abstracted into a mathematical model, making computers learn from data and equipping them with prediction abilities. Although this method can result in accurate predictions, it has a limitation in that it lacks explanatory capability; there is an urgent need for an (...) empirical study of the connection between acoustic features and psychology as the theoretical basis for the adjustment of model parameters. Accordingly, this study focuses on exploring the differences between seven major “acoustic features” and their physical characteristics during voice interaction with the recognition and expression of “gender” and “emotional states of the pleasure-arousal-dominance (PAD) model.” In this study, 31 females and 31 males aged between 21 and 60 were invited using the stratified random sampling method for the audio recording of different emotions. Subsequently, parameter values of acoustic features were extracted using Praat voice software. Finally, parameter values were analyzed using a Two-way ANOVA, mixed-design analysis in SPSS software. Results show that gender and emotional states of the PAD model vary among seven major acoustic features. Moreover, their difference values and rankings also vary. The research conclusions lay a theoretical foundation for AI emotional voice interaction and solve deep learning's current dilemma in emotional recognition and parameter optimization of the emotional synthesis model due to the lack of explanatory power. (shrink)

Digital health and welfare technologies and artificial intelligence are proposed to revolutionise healthcare systems around the world by enabling new models of care. Digital health and welfare technologies enable remote monitoring and treatments, and artificial intelligence is proposed as a means of prediction instead of reaction to individuals’ health and as an enabler of proactive care and rehabilitation. The digital transformation not only affects hospital and primary care but also how the community meets older people's needs. Community care is often (...) provided by informal and formal care‐givers, most of whom are women. Gender equality is at the heart of many national strategies, but do all genders have equal rights, responsibilities and opportunities when it comes to community care and its digital transformation? The digital transformation of community care is entangled with how care is provided to older people and the working conditions of community‐care professionals. Current and, even more so, future community‐care systems are and will be partly constituted by networks of technological artefacts. These health and welfare technological artefacts and the discourse surrounding them mediate and constitute social relations and community care. This article looks into how health and welfare technology and artificial intelligence‐based devices and systems mediate and constitute gender relations in community care and presents an argument for reflexivity, embodiment, pluralism, participation and ecology as an alternative strategy to treating community care as one‐size‐fit‐all and being blind to gender‐related issues. (shrink)

Customer behavior, market dynamics, and technological advances have made it challenging for marketing theorists to provide comprehensive explanations and actionable insights. Although there are numerous substantive marketing frameworks, no formal marketing theory exists. This study aims to develop the first formal grounded theory in marketing by incorporating artificial intelligence and Forde's conceptual framework as a guiding lens. Charmaz's constructivist grounded theory tradition and Forde's conceptual framework and data analysis strategy were employed for this purpose. The data analysis strategy (...) used with grounded theory’s constant compare-and-contrast method comprises holistic and systems thinking, dramaturgical and situational analysis, perspective-taking, and deconstruction. To evaluate Glaser’s assertion that all is data, artificial intelligence was incorporated into the research as the data source for retrieving the theoretical samples to address the primary research question: How can Forde's conceptual framework and artificial intelligence be leveraged to develop a formal marketing theory grounded in evidence? Of significance was the emergence of democratic marketing behaviors and the normalization of anti-democratic marketing approaches leveraged to manipulate buyers or gain access to segments of customers. Buyers and sellers can simultaneously benefit from and be prejudiced by marketing exchange dynamics. This finding lends credibility to the newly emerged framework of strategic actions where empowerment and disempowerment are leveraged to achieve specific goals. Considering this finding, it is suggested that businesses view marketing as a symbiotic relationship between two power centers, buyers and sellers, who respectively strive to maintain human dignity and sustainability. Researchers are encouraged to apply the framework across substantive areas, international markets, public relations, political campaigns, and others. (shrink)

This article describes the use of continuous vector space models for reasoning with a formal knowledge base. The practical significance of these models is that they support fast, approximate but robust inference and hypothesis generation, which is complementary to the slow, exact, but sometimes brittle behaviour of more traditional deduction engines such as theorem provers.The article explains the way logical connectives can be used in semantic vector models, and summarizes the development of Predication-based Semantic Indexing, which involves the use (...) of Vector Symbolic Architectures to represent the concepts and relationships from a knowledge base of subject-predicate-object triples. Experiments show that the use of continuous models for formal reasoning is not only possible, but already demonstrably effective for some recognized informatics tasks, and showing promise in other traditional problem areas. Examples described in this article include: predicting new uses for existing drugs in biomedical informatics; removing unwanted meanings from search results in information retrieval and concept navigation; type inference from attributes; comparing words based on their orthography; and representing tabular data, including modelling numerical values.The algorithms and techniques described in this article are all publicly released and freely available in the Semantic Vectors open-source software package.1. (shrink)

Integrating large language models (LLMs) like GPT-4 into medical ethics is a novel concept, and understanding the effectiveness of these models in aiding ethicists with decision-making can have significant implications for the healthcare sector. Thus, the objective of this study was to evaluate the performance of GPT-4 in responding to complex medical ethical vignettes and to gauge its utility and limitations for aiding medical ethicists. Using a mixed-methods, cross-sectional survey approach, a panel of six ethicists assessed LLM-generated responses to eight (...) ethical vignettes.The main outcomes measured were relevance, reasoning, depth, technical and non-technical clarity, as well as acceptability of GPT-4’s responses. The readability of the responses was also assessed. Of the six metrics evaluating the effectiveness of GPT-4’s responses, the overall mean score was 4.1/5. GPT-4 was rated highest in providing technical (4.7/5) and non-technical clarity (4.4/5), whereas the lowest rated metrics were depth (3.8/5) and acceptability (3.8/5). There was poor-to-moderate inter-rater reliability characterised by an intraclass coefficient of 0.54 (95% CI: 0.30 to 0.71). Based on panellist feedback, GPT-4 was able to identify and articulate key ethical issues but struggled to appreciate the nuanced aspects of ethical dilemmas and misapplied certain moral principles.This study reveals limitations in the ability of GPT-4 to appreciate the depth and nuanced acceptability of real-world ethical dilemmas, particularly those that require a thorough understanding of relational complexities and context-specific values. Ongoing evaluation of LLM capabilities within medical ethics remains paramount, and further refinement is needed before it can be used effectively in clinical settings. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Introduction to the Special Section on Psychedelics Research and TreatmentDominic SistiAgainst a backdrop of post-pandemic malaise, diseases of despair, and a fragmented mental health care system, psychedelics have enjoyed a resurgence of interest as powerful psychotherapeutic agents and as catalysts of personal growth. The true power of these substances—some of which are considered sacramental by Indigenous peoples—has been shrouded for half a century by cultural mythology, political propaganda, and (...) misuse. From about 1940 to 1970, psychedelics including psilocybin and LSD were studied and used by clinicians to treat a range of psychiatric disorders from alcoholism and depression in adults to "autistic schizophrenia" in children.In June 1971, the Nixon administration's racist and illiberal War on Drugs inaugurated what was essentially a total ban on psychedelics. Society was robbed of half a century of scientific progress, and one can only speculate how differently our society might now function, and how many people might have been spared the trauma of mental illness and incarceration. Thankfully, it appears the time has arrived for psychedelic medicines to be decriminalized and included again in the pharmacopeia.In June 2023, a group of psychedelic researchers, therapists, bioethicists, Indigenous scholars, and advocates met at the Banbury Conference Center at Cold Spring Harbor Laboratory. There, this interdisciplinary group discussed several pressing ethical issues in psychedelics research and treatment that continue to [End Page 114] challenge the field. The aim of this meeting was to develop a bioethical framework for the use of psychedelics in mainstream medical settings. That is, how should psychedelics be employed responsibly by everyday clinicians, including psychiatrists, psychologists, social workers, and other behavioral health-care providers? This special section offers a sampling of three topics in psychedelic bioethics raised by the Banbury group, several of whom appear as coauthors.In our first paper, Logan Neitzke-Spruill and colleagues offer an overview of explanatory models describing the therapeutic mechanisms of psychedelic substances and how each of these models generates unique ethical quandaries. Starting from molecular biology and moving to neural circuitry and networks, neurobiological models now propel contemporary scientific research into psychedelics. Knowing how these substances work on a molecular level may offer promising ways forward in the development of new molecules designed to treat serious mental illnesses and other neurologic conditions. Perhaps neuroplastic mechanisms will be harnessed to develop new therapies without necessitating a psychedelic trip—a controversial premise discussed in our third paper, by Katherine Cheung, Brian Earp, and David Yaden.Such "neuroreductionism" risks ignoring the subjective psychological and spiritual experiences of patients that appear to be pivotal in psychedelic-assisted therapy, often described as mystical and utterly transformative. At the psychological level, these substances engender a kind of vulnerability and suggestibility requiring clear ethical standards to ensure patients' psychological and physical safety. And so too, practitioners will need structural and cultural competency to recognize the spiritual or moral values that inspire patient reactions during and after their psychedelic experiences. Ethically informed psychedelic practitioners will have to navigate between disciplinary silos and explanatory models. One practical upshot is the open question of how best to train psychedelic practitioners who hold this broad set of competencies.To that end, well-trained practitioners will need to be able to disclose in some way the transformative properties of psychedelics to prepare patients for their experiences. But how does one describe the ineffable? This raises the question of how or if it is possible for patients to truly consent to the transformative experience elicited by psychedelics, often described as one of the most important experiences of a person's life. The experience might stimulate significant emotional healing, or it might be horrifying. This sounds like a problem uniquely attached to psychedelics: after all, most pharmaceuticals don't trigger changes in one's fundamental moral values, life choices, and commitments, nor are they intended to. Yet, Brent Kious, Andrew Peterson, and Amy McGuire challenge the view that the psychedelic experience is uniquely transformative and impervious to the disclosure requirements of informed consent, a bedrock of bioethics. Ultimately, the writers show how consent is possible even if the experiences are dramatically different from most medical interventions, endorsing a practical view of consent [End... (shrink)

Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses (...) from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the “dehumanisation of healthcare” and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. (shrink)

Obtaining informed consent from patients prior to a medical or surgical procedure is a fundamental part of safe and ethical clinical practice. Currently, it is routine for a significant part of the consent process to be delegated to members of the clinical team not performing the procedure (eg, junior doctors). However, it is common for consent-taking delegates to lack sufficient time and clinical knowledge to adequately promote patient autonomy and informed decision-making. Such problems might be addressed in a number of (...) ways. One possible solution to this clinical dilemma is through the use of conversational artificial intelligence using large language models (LLMs). There is considerable interest in the potential benefits of such models in medicine. For delegated procedural consent, LLM could improve patients’ access to the relevant procedural information and therefore enhance informed decision-making.In this paper, we first outline a hypothetical example of delegation of consent to LLMs prior to surgery. We then discuss existing clinical guidelines for consent delegation and some of the ways in which current practice may fail to meet the ethical purposes of informed consent. We outline and discuss the ethical implications of delegating consent to LLMs in medicine concluding that at least in certain clinical situations, the benefits of LLMs potentially far outweigh those of current practices. (shrink)

Scenarios involving the introduction of artificially intelligent (AI) assistive technologies in health care practices raise several ethical issues. In this paper, I discuss four objections to introducing AI assistive technologies in health care practices as replacements of human care. I analyse them as demands for felt care, good care, private care, and real care. I argue that although these objections cannot stand as good reasons for a general and a priori rejection of AI assistive technologies as such or as replacements (...) of human care, they demand us to clarify what is at stake, to develop more comprehensive criteria for good care, and to rethink existing practices of care. In response to these challenges, I propose a (modified) capabilities approach to care and emphasize the inherent social dimension of care. I also discuss the demand for real care by introducing the ‘Care Experience Machine’ thought experiment. I conclude that if we set the standards of care too high when evaluating the introduction of AI assistive technologies in health care, we have to reject many of our existing, low-tech health care practices. (shrink)

Mazur & Booth (1998) (M&B) suggested that high testosterone (T) relates to status, dominance, and (anti-) social behaviour. However, low T also relates to status and to formal dominance. The General Trait Covariance (GTC) model predicts both relations under the assumption that high and low T modulates the genotype in ways that enforce the development of almost polar covariant patterns of body, brain, intellectual, and personality traits, irrespective of race. The precise modelling of these dose-dependent molecular body-intelligence-personality-behaviour relations requires (...) that causes, mechanisms, and effects enjoy equal operational standing. (shrink)

A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure (...) is instructive: in exploring the argument, we gain insight into how to model advanced AI systems. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries into (...) the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such (...) a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies. (shrink)

In our recent article ‘The Ethics of the Algorithmic Prediction of Goal of Care Preferences: From Theory to Practice’1, we aimed to ignite a critical discussion on why and how to design artificial intelligence (AI) systems assisting clinicians and next-of-kin by predicting goal of care preferences for incapacitated patients. Here, we would like to thank the commentators for their valuable responses to our work. We identified three core themes in their commentaries: (1) the risks of AI paternalism, (2) worries about (...) attacks to our humanity stemming from the use of AI and (3) the possibility of designing AI systems for more relevant use cases than the one we consider in our work. We shall focus on these themes, leaving aside some other interesting suggestions, given the limited space available for our response. Diaz Milian and Bhattacharyya discuss the risks of AI paternalism highlighting how the use of an AI to predict goal of care preferences may incentivise clinicians and surrogates to shift the burden of decision making to the machine.2 In particular, ‘[i]f the AI-generated response is given priority above the other agents’2 and the AI makes decisions autonomously, then ‘AI paternalism’ would emerge.2 Against this possibility, the authors suggest four safeguards to be implemented in the AI life-cycle.2 These are procedures that may improve the trustworthiness of the AI and human control over it.3 We agree that it is important …. (shrink)

Given that psychophysical responses are not a function of a single property but vary with a variety of stimulus and context variables, Lockhead has little hope for laws relating behavior to the environment. However, progress can be made with tasks that manipulate multiple sources of information to test formal information-processing models.

Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and (...) a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's ranking theoretic approach to conditionals. (shrink)

ABSTRACT:Psychedelics have again become a subject of widespread interest, owing to the reinvigoration of research into their traditional uses, possible medical applications, and social implications. As evidence for psychedelics' clinical potential mounts, the field has increasingly focused on searching for mechanisms to explain the effects of psychedelics and therapeutic efficacy of psychedelic-assisted therapy (PAT). This paper reviews three general frameworks that encompass several prominent models for understanding psychedelics' effects—specifically, neurobiological, psychological, and spiritual frameworks. Following our review, the implications of each (...) framework for ethics and professional competencies in the implementation of psychedelics as medicines are explored. We suggest that interdisciplinary education may be necessary to improve communication between researchers, develop models that effectively incorporate multiple levels of analysis, and facilitate collaboration between professionals with diverse backgrounds in the implementation of psychedelic medicines. We also address pitfalls associated with overemphasis on neuro-mechanisms, risks associated with instigating vulnerable states of consciousness, and hurdles associated with the integration of spiritual frameworks in medicine. Ultimately, as psychedelics push the boundaries of explanatory frameworks focused on one level of analysis, developing new and more useful models to reflect knowledge being produced in this field should be a central aim of psychedelic science going forward. (shrink)

For implementing ethics in AI technology, there are at least two major ethical challenges. First, there are various competing AI ethics guidelines and consequently there is a need for a systematic overview of the relevant values that should be considered. Second, if the relevant values have been identified, there is a need for an indicator system that helps assessing if certain design features are positively or negatively affecting their implementation. This indicator system will vary with regard to specific forms of (...) AI technology. An adequate indicator system for the ethical development of recommendation algorithms, for example, will diverge considerably from another for autonomous road vehicles, although both are based on shared values. In this contribution, we propose solutions to both challenges with regard to the special case of the development of an AI-assisted rescue robot. (shrink)

Recent developments in AI, especially the spectacular success of Large Language models, have instigated renewed questioning of what remains distinctively human. As AI stands poised to take over more and more human tasks, what is left that distinguishes humans? One way we might identify a humanlike intelligence would be when we detect it telling lies. Yet AIs lack both the intention and the motivation to truly tell lies, instead producing merely bullshit. With neither emotions, embodiment, nor the social awareness that (...) leads to a theory of mind, AIs lack the internal referents on which to judge truth or falsity. When we are deceived by our computers, we need to look for the hidden agent who benefits from the deception. (shrink)

Physicians and other healthcare professionals are increasingly finding ways to use artificial intelligent decision support systems in their work. IBM Watson Health, for example, is a commercially available technology that is providing AI-DDS services in genomics, oncology, healthcare management and more.1 AI’s ability to scan massive amounts of data, detect patterns, and derive solutions from data is vastly more superior than that of humans. AI technology is undeniably integral to the future of healthcare and public health, and thoughtful consideration of (...) the legal, ethical and moral issues surrounding this technology is a must. The authors of the article, Responsibility, Second Opinions, and Peer-Disagreement—Ethical and Epistemological Challenges of Using AI in Clinical Diagnostic Contexts, provide an informed discussion of how AI-DSS may be used, both practically and ethically, to assist healthcare professionals in cooperative diagnostic processes2. The authors propose a process, whereby AI-DSS would provide a physician a second opinion, and when there is a mismatch between opinions, another physician would provide a third opinion. This approach maintains a ‘physician-in-charge’ perspective, and suggests that decision-making must ultimately be made by a person. This approach is also consistent with the ‘physician in-the-loop’ concept, such that even when increasingly autonomous AI-DSSs are put to use, physicians will still be providing checks and overseeing clinical decisions. The authors conceptualise AI as a replacement of human cognitive labour. That is, AI is used to supplant a human professional in particular functions …. (shrink)

Artificial intelligence technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology and the theory of perceived risk, this study identified six factors that influence students' (...) willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy, performance expectancy, and social influence were all positively related to college students' WTA AI-assisted learning environments. Psychological risk significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments. (shrink)

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) (...) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. (shrink)

After more than 15 years of study, the 1/f noise or complex-systems approach to cognitive science has delivered promises of progress, colorful verbiage, and statistical analyses of phenomena whose relevance for cognition remains unclear. What the complex-systems approach has arguably failed to deliver are concrete insights about how people perceive, think, decide, and act. Without formal models that implement the proposed abstract concepts, the complex-systems approach to cognitive science runs the danger of becoming a philosophical exercise in futility. The (...) complex-systems approach can be informative and innovative, but only if it is implemented as a formal model that allows concrete prediction, falsification, and comparison against more traditional approaches. (shrink)

This paper considers the kind of introspection that large language models (LLMs) might be able to have. It argues that LLMs, while currently limited in their introspective capabilities, are not inherently unable to have such capabilities: they already model the world, including mental concepts, and already have some introspection-like capabilities. With deliberate training, LLMs may develop introspective capabilities. The paper proposes a method for such training for introspection, situates possible LLM introspection in the 'possible forms of introspection' framework proposed by (...) Kammerer and Frankish, and considers the ethical ramifications of introspection and self-report in AI systems. (shrink)

Voice-assistants are becoming increasingly popular and can be deployed to offers a low-cost tool that can support and potentially reduce falls, injuries, and accidents faced by older people within the age of 65 and older. But, irrespective of the mobility and walkability challenges faced by the aging population, studies that employed Artificial Intelligence (AI)-based voice-assistants to reduce risks faced by older people when they use public transportation and walk in built environment are scarce. This is because the development of AI-based (...) voice-assistants suitable for the mobility domain presents several techno–social challenges. Accordingly, this study aims to identify _user-centered_ service design and functional requirements, techno–social factors, and further design an architectural model for an AI-based voice-assistants that provide personalized recommendation to reduce falls, injuries, and accidents faced by older people. Accordingly, a scoping review of the literature grounded on secondary data from 59 studies was conducted and descriptive analysis of the literature and content-related analysis of the literature was carried out. Findings from this study presents the perceived techno-socio factors that may influences older people use of AI-based voice-assistants. More importantly, this study presents user-centred service design and functional requirements needed to be considered in developing voice-assistants suitable for older people. Implications from this study provides AI techniques for implementing voice-assistants that provide safe mobility, walkability, and wayfinding for older people in urban areas. (shrink)

This chapter is offered as a contribution to the logic of down below. We attempt to demonstrate that the nature of human agency necessitates that there actually be such a logic. The ensuing sections develop the suggestion that cognition down below has a structure strikingly similar to the physical structure of quantum states. In its general form, this is not an idea that originates with the present authors. It is known that there exist mathematical models from the cognitive science of (...) cognition down below that have certain formal similarities to quantum mechanics. We want to take this idea seriously. We will propose that the subspaces of von Neumann-Birkhoff lattices are too crisp for modelling requisite cognitive aspects in relation to subsymbolic logic. Instead, we adopt an approach which relies on projections into nonorthogonal density states. The projection operator is motivated from cues which probe human memory. (shrink)

Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial intelligence (AI) (...) research attempts to develop such models. But even as cognitive science has displaced behavioralism as the dominant paradigm for investigating the human mind, fundamental questions about the very possibility of artificial intelligence continue to be debated. This Essay explores those questions through a series of thought experiments that transform the theoretical question whether artificial intelligence is possible into legal questions such as, "Could an artificial intelligence serve as a trustee?" What is the relevance of these legal thought experiments for the debate over the possibility of artificial intelligence? A preliminary answer to this question has two parts. First, putting the AI debate in a concrete legal context acts as a pragmatic Occam's razor. By reexamining positions taken in cognitive science or the philosophy of artificial intelligence as legal arguments, we are forced to see them anew in a relentlessly pragmatic context. Philosophical claims that no program running on a digital computer could really be intelligent are put into a context that requires us to take a hard look at just what practical importance the missing reality could have for the way we speak and conduct our affairs. In other words, the legal context provides a way to ask for the "cash value" of the arguments. The hypothesis developed in this Essay is that only some of the claims made in the debate over the possibility of AI do make a pragmatic difference, and it is pragmatic differences that ought to be decisive. Second, and more controversially, we can view the legal system as a repository of knowledge-a formal accumulation of practical judgments. The law embodies core insights about the way the world works and how we evaluate it. Moreover, in common-law systems judges strive to decide particular cases in a way that best fits the legal landscape-the prior cases, the statutory law, and the constitution. Hence, transforming the abstract debate over the possibility of AI into an imagined hard case forces us to check our intuitions and arguments against the assumptions that underlie social decisions made in many other contexts. By using a thought experiment that explicitly focuses on wide coherence, we increase the chance that the positions we eventually adopt will be in reflective equilibrium with our views about related matters. In addition, the law embodies practical knowledge in a form that is subject to public examination and discussion. Legal materials are published and subject to widespread public scrutiny and discussion. Some of the insights gleaned in the law may clarify our approach to the artificial intelligence debate. (shrink)

Sedlakova and Traschel (2023) propose limiting the scope and capabilities of psychotherapy-delivering conversational artificial intelligence (CAI), as (1) they deceive some users into thinking they...

The problems with grace and free will have prompted long-standing theological conflicts, chiefly revolving around certain disagreements over the nature of divine causality in respect to the free will's of creatures and His foreknowledge of free acts. Eleonore Stump offers a new interpretation of divine action on the will that holds God only acts by way of formal causality and that human cooperation with grace is only by way of "quiescence." I argue that this account lacks coherence in certain (...) important respects, especially in how human beings freely decide upon conversion - a process impossible on this model. I also argue that any model of divine causality cannot escape the dichotomy of holding a Molinist or a Banezian model of efficacious grace. In response, I offer a new interpretation of classical Thomist models of grace that preserves human freedom and divine sovereignty in grace. (shrink)

Member States of the United Nations proposed Seventeen Sustainable Development Goals (SDGs) in 2015, emphasizing the well-being of people, planet, prosperity, peace, and partnership. Countries are expected to work diligently to achieve these goals by the year 2030. The paths chosen to achieve the SDGs depend on each country’s specific needs, challenges, and opportunities. This contribution conducts a bibliometric study of selected SDG research related to hunger and climate change among countries of the North Asia Pacific region. A review of (...) literature related to the seventeen SDGs is provided to update the current research in SDGs as a background for the regional bibliometric search. The academic published literature related to this work were collected and text mined to create a research database for trend analysis. In this research, the natural language model KeyBERT and a text mining algorithm for normalized term frequency were used to extract key terminologies for bibliometric analysis. The study provides an overview of the seventeen SDGs in the literature review and then focuses on SDG 2 (zero hunger) and SDG 13 (climate action) publication trends. These two SDGs are in close alignment with sustainable food systems and agricultural development. The conclusion indicates that greater cooperation (SDG 17) is critical to improving the quality of life within the region and in building an ethical framework that restricts external exploitation of resources that benefit newly developed economies over developing economies. (shrink)

In this paper, we first enumerate the problems that humans might face with a new type of technology such as robots with artificial intelligence (AI robots). Robotics entrepreneurs are calling for discussions about goals and values because AI robots, which are potentially more intelligent than humans, can no longer be fully understood and controlled by humans. AI robots could even develop into ethically “bad” agents and become very harmful. We consider these discussions as part of a process of developing responsible (...) innovations in AI robotics in order to prevent catastrophic risks on a global scale. To deal with these issues, we propose the capability-effectual approach, drawing on two bodies of research: the capability approach from ethics, and the effectual process model from entrepreneurship research. The capability approach provides central human capabilities, guiding the effectual process through individual goals and aspirations in the collaborative design process of stakeholders. More precisely, by assuming and understanding correspondences between goals, purposes, desires, and aspirations in the languages of different disciplines, the capability-effectual approach clarifies both how a capability list working globally could affect the aspirations and end-goals of individuals, and how local aspirations and end-goals could either energise or limit effectual processes. Theoretically, the capability-effectual approach links the collaboration of stakeholders and the design process in responsible innovation research. Practically, this approach could potentially contribute to the robust development of AI robots by providing robotics entrepreneurs with a tool for establishing a permissible action range within which to develop AI robotics. (shrink)

Machine-based automation has long been a key factor in the modern era. However, lately, many people have been shocked by artificial intelligence (AI) applications, such as ChatGPT (OpenAI), that can perform tasks previously thought to be human-exclusive. With recent advances in natural language processing (NLP) technologies, AI can generate written content that is similar to human-made products, and this ability has a variety of applications. As the technology of large language models continues to progress by making use of colossal reservoirs (...) of digital information, AI is becoming more capable of recognizing patterns and associations within given contexts, making it especially helpful in assisting with various professional writing tasks [1]. In this paper, we will discuss several key points regarding the current state and future of AI in academia. Furthermore, since in certain aspects, acts can speak louder than words, we will also present some of our hands-on experiences of working with ChatGPT. (shrink)