Powerful transformer models based on neural networks such as GPT-4 have enabled huge progress in natural language processing. This paper identifies three challenges for computer programs dealing with metaphors. First, the phenomenon of Twice-Apt-Metaphors shows that metaphorical interpretations do not have to be triggered by syntactical, semantic or pragmatic tensions. The detection of these metaphors seems to involve a sense of aesthetic pleasure or a higher-order theory of mind, both of which are difficult to implement into computer programs. Second, (...) the contexts relative to which metaphors are interpreted are not simply given but must be reconstructed based on pragmatic considerations that can involve presuppositional pretence. If computer programs cannot produce or understand such a form of pretence, they will have problems dealing with certain metaphors. Finally, adequately interpreting and reacting to some metaphors seems to require the ability to have internal, first-personal experiential and affective states. Since it is questionable whether computer programs have such mental states, it can be assumed that they will have problems with these kinds of metaphors. (shrink)

Computer vision is a subfeld of artifcial intelligence, aimed at making computers see. Computer vision tools enable a system or device to automatically analyze, interpret, and respond to images and videos. Computer vision tasks range from object detection and tracking, to the recognition of people’s faces and emotional states. While the ethics of AI in general has received signifcant attention, and the ethics of facial recognition (a computer vision application) too, little of the AI ethics literature focuses specifcally on (...) the ethics of computer vision. In this chapter, I create an overview of ethical, social, and political issues related to computer vision, using a critical approach. This means that I identify issues in terms of power and evaluate them in function of their impact on the value of autonomy and the normative goal of emancipatory progress. The aim of this chapter is frst and foremost to ofer an overview of potential normative implications of computer vision. Additionally, the chapter functions as an example for the use of a critical approach to AI ethics. (shrink)

Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions.,The paper synthesises information from academic articles, industry reports, (...) case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions.,The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed.,Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject.,This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data.,This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing.,To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

How can one build AI systems such that they pursue the goals their designers want them to pursue? This is the alignment problem. Numerous authors have raised concerns that, as research advances and systems become more powerful over time, misalignment might lead to catastrophic outcomes, perhaps even to the extinction or permanent disempowerment of humanity. In this paper, I analyze the severity of this risk based on current instances of misalignment. More specifically, I argue that contemporary large language models and (...) game-playing agents are sometimes misaligned. These cases suggest that misalignment tends to have a variety of features: misalignment can be hard to detect, predict and remedy, it does not depend on a specific architecture or training paradigm, it tends to diminish a system’s usefulness and it is the default outcome of creating AI via machine learning. Subsequently, based on these features, I show that the risk of AI alignment magnifies with respect to more capable systems. Not only might more capable systems cause more harm when misaligned, aligning them should be expected to be more difficult than aligning current AI. (shrink)

Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of three NECs. It finds that the UK-Census-trained EthnicityEstimator displays large accuracy biases with regards (...) to ethnicity, but relatively less among gender and age groups. In contrast, the Twitter-trained NamePrism and the Wikipedia-trained Ethnicolr are more balanced among ethnicity, but less among gender and age. We relate these biases to global power structures manifested in naming conventions and NECs’ input distribution of names. To improve on the uncovered biases, we program a novel NEC, N2E, using fairness-aware AI techniques. We make N2E freely available at www.name-to-ethnicity.com. (shrink)

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did (...) not come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an example. (shrink)

Mark Coeckelbergh starts his book with a very powerful picture based on a real incident: On the 9th of January 2020, Robert Williams was wrongfully arrested by Detroit police officers in front of his two young daughters, wife and neighbors. For 18 hours the police would not disclose the grounds for his arrest (American Civil Liberties Union 2020; Hill 2020). The decision to arrest him was primarily based on a facial detection algorithm which matched Mr. Williams’ driving license photo (...) with the picture of a man who was suspected of watch theft two years earlier. Not only did the computer ‘get it wrong’ as one of the detectives said, when Mr. Williams made them aware that the picture of the suspect obviously wasn’t resembling him, the probably unreliable algorithm very likely contributed to racial discrimination (Hill 2020). It is well documented that many available facial detection algorithms at this time had significant problems (e.g. a comparably high false positive rate) with respect to black persons, like Mr. Williams (NIST 2019). Multiple causes may exist, such as unbalanced training datasets and insufficient optimization. Coeckelbergh compares the disturbing case of Mr. Williams with a political interpretation of Franz Kafka’s The Trial, where the protagonist, Josef K., is accused of an unspecified crime by an opaque, oppressive and absurd bureaucracy: “In the 21st-century United States, Josef K. is black and is falsely accused by an algorithm, without explanation” (p. 2). (shrink)

We consider the potential impact of Computer Supported Cooperative Work, with special reference to large technically advanced projects involving several organizations. It is vital that such projects are managed efficiently, without delays, since a product that reaches the market a few months earlier than its competitors enjoys a great advantage. Traditional methods of coordinating large projects, based on hierarchical communication, tend to produce delays, since technicians at remote sites are obliged to solve coordination problems by passing them up the hierachy. (...) It would be better if such problems were solved by improvising conferences among the technicians; Computer Supported Cooperative Work will provide the technical means of implementing this heterarchical style of management without losing control of the project. The use of computers as a social medium raises methodological and ethical issues which are discussed in the final section. (shrink)

Purpose This paper aims to appraise current challenges in adopting generative AI by reviewers to evaluate the readability and quality of submissions. The paper discusses how to make the AI-powered peer-review process immune to unethical practices, such as the proliferation of AI-generated poor-quality or fake reviews that could harm the value of peer review. Design/methodology/approach This paper examines the potential roles of AI in peer review, the challenges it raises and their mitigation. It critically appraises current opinions and practices (...) while acknowledging the lack of consensus about best practices in AI for peer review. Findings The adoption of generative AI by the peer review process seems inevitable, but this has to happen (1) gradually, (2) under human supervision, (3) by raising awareness among stakeholders about all its ramifications, (4) through improving transparency and accountability, (5) while ensuring confidentiality through the use of locally hosted AI systems, (6) by acknowledging its limitations such as its inherent bias and unawareness of up-to-date knowledge, (7) by putting in place robust safeguards to maximize its benefits and lessen its potential harm and (8) by implementing a robust quality assurance to assess its impact on overall research quality. Originality/value In the current race for more AI in scholarly communication, this paper advocates for a human-centered oversight of AI-powered peer review. Eroding the role of humans will create an undesirable situation where peer review will gradually metamorphose into an awkward conversation between an AI writing a paper and an AI evaluating that paper. (shrink)

A survey conducted in 2023 surveyed 3,017 high school and college students. It found that almost one-third of them confessed to using ChatGPT for assistance with their homework. The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has led to a surge in academic misconduct. Students can now complete their assignments and exams just by asking an LLM for solutions to the given problem, without putting in the effort required for learning. And, what is more worrying, educators (...) do not have the proper tools to detect it. The more advanced AI tools become, the more human-like text they generate, and the more difficult they are to detect. Additionally, some educators find it difficult to adapt their teaching and assessment methods to avoid plagiarism. This paper is focused on how LLMs and AI-Generated Content (AIGC) have affected education. It first shows the relationship between LLMs and academic dishonesty. Then, it reviews state-of-the-art solutions for preventing academic plagiarism in detail, including a survey of the main datasets, algorithms, tools, and evasion strategies for plagiarism detection. Lastly, it identifies gaps in existing solutions and presents potential long-term solutions based on AI tools and educational approaches to address plagiarism in an ever-changing world. (shrink)

Artificial Intelligence (AI) applied to the legal domain is gaining ground. AI is argued to be particularly helpful with labour-intensive activities and repetitive tasks. Amongst the various AI solutions, ChatGPT has gathered momentum and its acclaimed advantages are, amongst others, document generation and contract review. This paper wishes to assess the effectiveness of two chatbots in contract drafting. To this aim, ChatGPT (by OpenAI) and Gemini (by Google) are prompted to write two supply contracts each, the first one written in (...) English according to the laws of England and Wales, and the other one still written in English, but according to the laws of the Republic of Italy. The paper finds that each contract pair generated by the AI solutions is identical, thus disregarding legal-system specificities and language conventions. Therefore, the drafts present a number of fallacies, either because they are incomplete, or because they include clauses that are redundant or inapplicable in a given legal system. The paper claims that human intervention and supervision are necessary to tackle the shortcomings generated by AI, at least as far as contract generation is concerned and with regard to the two chatbots addressed. (shrink)

Given the pervasiveness of AI systems and their potential negative effects on people’s lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more “explainable” by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, (...) or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of “health”, which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing “justificatory explanations” about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of “health”, we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that “justificatory explanations” are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project’s main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. (shrink)

Ultrasonic excitation has been widely used in the detection of microcracks on metal surfaces, but there are problems such as poor excitation effect of ultrasonic pulse, long time to reach the best excitation, and difficult to find microcracks. In this paper, an adaptive ultrasonic pulse excitation device and infrared thermal imaging technology have been combined, as well as their control method, to solve the problem. The adaptive ultrasonic pulse excitation device adds intelligent modules to realize automatic adjustment of (...) class='Hi'>detection parameters, which can quickly obtain reliable excitation; the multidegree-of-freedom base realizes the three-dimensional direction change of the ultrasonic gun to adapt to different excitation occasions. When the appropriate ultrasonic excitation makes microcracks in the resonance state, the microcracks can be frictionated, which produce heat rise with the temperature. Then, the microcrack defect can be detected by the infrared thermal instrument through the different surface temperatures with imaging recognition method. Our detection experiments of the titanium alloy plates and the aluminum alloy profiles of marine engineering show that the method can get reliable detection parameters in a short time and measure the crack length effectively. It can be used in many aspects such as crack detection in mechanical structures or complex equipment operating conditions and industrial production processes. (shrink)

This book examines the philosophical conception of abductive reasoning as developed by Charles S. Peirce, the founder of American pragmatism. It explores the historical and systematic connections of Peirce's original ideas and debates about their interpretations. Abduction is understood in a broad sense which covers the discovery and pursuit of hypotheses and inference to the best explanation. The analysis presents fresh insights into this notion of reasoning, which derives from effects to causes or from surprising observations to explanatory theories. The (...) author outlines some logical and AI approaches to abduction as well as studies various kinds of inverse problems in astronomy, physics, medicine, biology, and human sciences to provide examples of retroductions and abductions. The discussion covers also everyday examples with the implication of this notion in detective stories, one of Peirce’s own favorite themes. The author uses Bayesian probabilities to argue that explanatory abduction is a method of confirmation. He uses his own account of truth approximation to reformulate abduction as inference which leads to the truthlikeness of its conclusion. This allows a powerful abductive defense of scientific realism. This up-to-date survey and defense of the Peircean view of abduction may very well help researchers, students, and philosophers better understand the logic of truth-seeking. (shrink)

Can managers detect honest people’s lies in a handwritten message? In this article, I will briefly discuss graphology and a basic model of interpersonal communication. I will then develop a fundamental theoretical framework of eight principles for detecting lies based on the basic communication model, handwriting analyses, and the following assumptions: For most people, it is easier to tell the truth than to tell lies. This applies to handwritings also. When most honest people lie, they try to hide their stressful (...) emotions in the encoding process. As a consequence, they deviate from their own normal writing and violate their own personal moral standards. Interestingly enough, the art or science of detecting a lie in a handwritten sample is to focus not on what they write, but on how they write it. These 24 exhibits (cases) written in 11 languages—used in different parts of the world—help managers apply this important theoretical framework of interpersonal communication, understand the encoding process, pinpoint these sudden emotional changes, decode handwritten messages, unlock the secrets, reveal the message’s true meanings, and detect people’s lies. (shrink)

We use content analysis to show that the diagnosis of the financial crisis of 2007–2009 shifted significantly from a focus on the need for structural change in the banking industry to an emphasis on culture and reform at the organizational level. We consider four overlapping subsystems in which this shift in problem–solution clusters played out—political, regulatory, legal, and consulting—and show that the “structural reform agenda,” which was initially strong and publicly prominent in the political arena, lost attention. Over time it (...) was displaced by a neoliberal managerialist turn, which watered down or abandoned structural solutions and instead played up a new “culture and conduct reform agenda.” We explain this shift in terms of the marketization of regulation, which—following Mautner (_Language and the market society_, 1st ed. Routledge, 2010)’s model of interdiscursive alignment—we detect in the shifting language of financial-services reform across the four subsystems in scope. We argue that a neoliberal turn took place with a _discursive closure_ that made the structural reform alternative gradually unsayable and, in the end, unthinkable. At the same time, the discourse turned to embrace the neoliberal agenda, built on the myth of self-regulating actors and markets, manifest in the culture problematic. This managerialist turn was able to mobilise, and be operationalised by, an industry of consultants, whereas structural change came to be seen by regulators as too risky to implement. We claim that these dynamics reveal how a form of “collective strategic ignorance,” based on powerful institutional myths, was systematically oriented to ignore and reject structural sources of crisis. Finally, we suggest that the observed pattern of displacement—whereby initial calls for structural change become later displaced by managerial and procedural solutions—is common to other social issues, such as audit reform and corporate social responsibility. (shrink)

With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be (...) transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...) some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential (...) epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry. (shrink)

This essay introduces this special issue on virtue ethics in relation to military AI. It describes the current situation of military AI ethics as following that of AI ethics in general, caught between consequentialism and deontology. Virtue ethics serves as an alternative that can address some of the weaknesses of these dominant forms of ethics. The essay describes how the articles in the issue exemplify the value of virtue-related approaches for these questions, before ending with thoughts for further research.

The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network and a fault detection algorithm based on discrete wavelet transform are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals (...) are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance. (shrink)

Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they work.

GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- to (...) middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

Using biased data to train Artificial Intelligence (AI) algorithms will lead to biased decisions, discriminating against certain groups or individuals. Bias can be explicit (one or several protected features directly influence the decisions) or implicit (one or several protected features indirectly influence the decisions). Unsurprisingly, biased patterns are difficult to detect and mitigate. This paper investigates the extent to which explicit and implicit against one or more protected features in structured classification data sets can be mitigated simultaneously while retaining the (...) data’s discriminatory power. The main contribution of this paper concerns an optimization-based bias mitigation method that reweights the training instances. The algorithm operates with numerical and nominal data and can mitigate implicit and explicit bias against several protected features simultaneously. The trade-off between bias mitigation and accuracy loss can be controlled using parameters in the objective function. The numerical simulations using real-world data sets show a reduction of up to 77% of implicit bias and a complete removal of explicit bias against protected features at no cost of accuracy of a wrapper classifier trained on the data. Overall, the proposed method outperforms the state-of-the-art bias mitigation methods for the selected data sets. (shrink)

Nowadays, batteries play an important role in a lot of different applications like energy storage, electro-mobility, consumer electronic and so on. All the battery types have a common factor that is their complexity, independently of its nature. Usually, the batteries have an electrochemical nature. Several different test are accomplished to check the batteries performance, and commonly, it is predictable how they work depending of their technology. The present research describes the hybrid intelligent system created to accomplish fault detection over (...) a Lithium Iron Phosphate—LiFePO4 power cell type, commonly used in electro-mobility applications. The approach is based on the cell temperatures behaviour for voltage and current specific values. Taken into account the operating range of a real system based on a LiFePO4 cell, a large set of points of operation have been used to achieve the dataset. The different behaviour zones have been obtained by clustering as a first step. Then, different regression techniques have been used over each cluster. Polynomial regression, artificial neural networks and support vector regression were the combined techniques to develop the hybrid intelligent model proposed. The intelligent system gives very good results over the operating range, detecting all the faults tested during the validation. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for (...) the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (shrink)

In this article, our aim is to show why increasing the effectiveness of detecting doping fraud in sport by the use of artificial intelligence (AI) may be morally wrong. The first argument in favour of this conclusion is that using AI to make a non-ideal antidoping policy even more effective can be morally wrong. Whether the increased effectiveness is morally wrong depends on whether you believe that the current antidoping system administrated by the World Anti-Doping Agency is already morally wrong. (...) The second argument is based on the possibility of scenarios in which a more effective AI system may be morally worse than a less effective but non-AI system. We cannot, of course, conclude that the increased effectiveness of doping detection is always morally wrong. But our point is that whether the introduction of AI to increase detection of doping fraud is a moral improvement depends on the moral plausibility of the current system and the distribution of harm that will follow from false positive and false negative errors. (shrink)

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated (...) content, only 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (shrink)

It is estimated that one in three women experience intimate partner violence (IPV) across the course of their life. The popular uptake of “smart speakers” powered by sophisticated AI means that surveillance of the domestic environment is increasingly possible. Correspondingly, there are various proposals to use smart speakers to detect or report IPV. In this paper, we clarify what might be possible when it comes to combatting IPV using existing or near-term technology and also begin the project of evaluating (...) this project both ethically and politically. We argue that the ethical landscape looks different depending on whether one is considering the decision to develop the technology or the decision to use it once it has been developed. If activists and governments wish to avoid the privatisation of responses to IPV, ubiquitous surveillance of domestic spaces, increasing the risk posed to members of minority communities by police responses to IPV, and the danger that more powerful smart speakers will be co-opted by men to control and abuse women, then they should resist the development of this technology rather than wait until these systems are developed. If it is judged that the moral urgency of IPV justifies exploring what might be possible by developing this technology, even in the face of these risks, then it will be imperative that victim-survivors from a range of demographics, as well as government and non-government stakeholders, are engaged in shaping this technology and the legislation and policies needed to regulate it. (shrink)

In this study, we propose the Ankle-Worn Sensor Sleeve to help people maintain their health. Furthermore, our approach uses this sensor sleeve to improve walking motivation by making the walking motion more sensible. In this research, we pursue the design of a wearable device that users can wear as a “shoe accessory,” with the aim of promoting people’s walking motivation. This device is ankle-worn and generates electricity using the physical flexion and extension of the ankle, detects and acquires the amount (...) of electricity generated, and uses the resulting power to produce sound and light feedback while walking. The Sleeve is designed to support health problems, such as locomotive syndrome and dementia, which are caused by physical inactivity. This article presents the results of a questionnaire survey conducted on 172 people who experienced or observed the device during exhibitions. The results reveal that this device increased people’s interest and motivation to walk, including those who did not like to walk. (shrink)

The paper aims to show how the Tv series True Detective (season 1, 2014 – created and written by Nic Pizzolatto) aids us understanding the status of power whether in its political, judicial or religious form. The conjunction of sex and death is depicted through acts of paedophilia, rape, and murder of women and children. The study aims to elucidate the logic which is inherent to those acts of violence and which founds the reproduction of social domination mechanisms.

The world is about to be swamped by an unprecedented wave of AI-generated content. We need reliable ways of identifying such content, to supplement the many existing social institutions that enable trust between people and organisations and ensure social resilience. In this paper, we begin by highlighting an important new development: providers of AI content generators have new obligations to support the creation of reliable detectors for the content they generate. These new obligations arise mainly from the EU’s newly finalised (...) AI Act, but they are enhanced by the US President’s recent Executive Order on AI, and by several considerations of self-interest. These new steps towards reliable detection mechanisms are by no means a panacea—but we argue they will usher in a new adversarial landscape, in which reliable methods for identifying AI-generated content are commonly available. In this landscape, many new questions arise for policymakers. Firstly, if reliable AI-content detection mechanisms are available, who should be required to use them? And how should they be used? We argue that new duties arise for media and Web search companies arise for media companies, and for Web search companies, in the deployment of AI-content detectors. Secondly, what broader regulation of the tech ecosystem will maximise the likelihood of reliable AI-content detectors? We argue for a range of new duties, relating to provenance-authentication protocols, open-source AI generators, and support for research and enforcement. Along the way, we consider how the production of AI-generated content relates to ‘free expression’, and discuss the important case of content that is generated jointly by humans and AIs. (shrink)

Neuroimaging technologies have improved our understanding of deception and also exhibit their potential in revealing the origins of its neural mechanism. In this study, a quantitative power analysis method that uses the Welch power spectrum estimation of functional near-infrared spectroscopy signals was proposed to examine the brain activation difference between the spontaneous deceptive behavior and controlled behavior. The power value produced by the model was applied to quantify the activity energy of brain regions, which can serve as a neuromarker for (...) deception detection. Interestingly, the power analysis results generated from the Welch spectrum estimation method demonstrated that the spontaneous deceptive behavior elicited significantly higher power than that from the controlled behavior in the prefrontal cortex. Meanwhile, the power findings also showed significant difference between the spontaneous deceptive behavior and controlled behavior, indicating that the reward system was only involved in the deception. The proposed power analysis method for processing fNIRS data provides us an additional insight to understand the cognitive mechanism of deception. (shrink)

The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the (...) Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things. (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)

This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems (...) into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation. (shrink)

“Strong AI” is the view that it is possible for an artificial agent to be mentally indistinguishable from human agents. Because the behavioral dispositions of artificial agents are determined by underlying dispositional systems, Strong AI seems to entail human behavioral dispositions are also determined by dispositional systems. It is, however, highly intuitive that non-dispositional, phenomenal properties, such as being in pain, at least partially determine certain human behavioral dispositions, like the disposition to take a pain killer. Consequently, Strong AI seems (...) to conflict with an intuitive view of phenomenal properties’ role in determining human behavioral dispositions. My goal here is not directly to evaluate this tension, but rather to clarify how dispositionalism in the metaphysics of properties bears on it. While a tempting thought is that dispositionalism fits well with Strong AI’s thoroughly dispositional account of human behavior, I argue that this thought does not hold for dispositionalism in general. In particular, I argue that combining a version of the “powerful qualities view” with certain dispositionalist conceptions of the will leads to a version of the intuitive view of phenomenal properties that is radically incompatible with Strong AI. I argue further that this view also raises a challenge for the weaker claim that an artificial agent could be behaviorally indistinguishable from a human agent. (shrink)

This article discusses the political economy of AI capitalism. It considers AI as a General Purpose Technology and argues we need to investigate the power concentration of Big Tech. AI capitalism is characterised by the commodification of data, data extraction and a concentration in hiring of AI talent and compute capacity. This is behind Big Tech’s unstoppable drive for growth, which leads to monopolisation and enclosure under the winner takes all principle. If we consider AI as a GPT—technologies that alter (...) society’s economic and social structures—we need to come up with alternatives in terms of ownership and governance. The commons is proposed as an alternative for thinking about how to organise AI development and how to distribute the value that can be derived from it. Using the commons framework is also a way of giving society a more prominent role in the debate about what we expect from AI and how we should approach it. (shrink)

Automation is inherently tied to ethical challenges because of its potential involvement in morally loaded decisions. In the present research, participants (n = 34) took part in a moral multi-trial dilemma-based task where the agent (human vs. machine) and the behavior (action vs. inaction) factors were randomized. Self-report measures, in terms of morality, consciousness, responsibility, intentionality, and emotional impact evaluation were gathered, together with electroencephalography (delta, theta, beta, upper and lower alpha, and gamma powers) and peripheral autonomic (electrodermal activity, heart (...) rate variability) data. Data showed that moral schemata vary as a function of the involved decider, and when the agent and behavior factors are crossed. Subjects did not consider machines full moral deciders to the same degree as humans and tend to morally better accept human active behavior and machine inaction. Moreover, the autonomic physiological activity might support the a-posteriori moral evaluation. In the evaluation of the agent’s consciousness, a beta ventrolateral prefrontal synchronization was detected for human action and machine inaction, while a generalized gamma synchronization occurred in artificial agent trials while rating the emotional impact of the decider’s behavior. The detected differences might point to a potential explicit and implicit asymmetry in moral reasoning toward artificial and human agents. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)