Doping can be both of chemical and protein nature or may involve prohibited methods, such as illegal blood transfusions. The rapidly increasing number of genetic therapies as a promising new branch of regular medicine, has raised the issue whether these techniques might be abused in the field of sports. The risks involved in gene doping are several, and are related both to the vector protein used (DNA, chemical, viral) and to the encoded transgene. A concern in the athletic community is (...) that no one knows how easily gene doping can be detected. The DNA used for gene transfer is of human origin, and not different from that of the person applying gene doping. A combination of developing detection methods based on gene arrays or proteomics and a clear education program on the associated risks seems to be the most promising preventive method to counteract the possible application of gene doping. (shrink)

Deepfakes and other forms of synthetic media are widely regarded as serious threats to our knowledge of the world. Various technological responses to these threats have been proposed. The reactive approach proposes to use artificial intelligence to identify synthetic media. The proactive approach proposes to use blockchain and related technologies to create immutable records of verified media content. I argue that both approaches, but especially the reactive approach, are vulnerable to a problem analogous to the ancient problem of the criterion—a (...) line of argument with skeptical implications. I argue that, while the proactive approach is relatively resistant to this objection, it faces its own serious challenges. In short, the proactive approach would place a heavy burden on users to verify their own content, a burden that is exacerbated by and is likely to exacerbate existing inequalities. (shrink)

Atran & Norenzyan (A&N) claim that an appreciation of the evolved inferential machinery underlying supernatural beliefs can greatly aid us in understanding regularities in culturally shared conceptions of religion. I explore how their model provides insight into why culturally shared tales of horror (e.g., horror movies) often combine religious and predatory content.

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...) so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (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)

The goal of the paper is to present a Simplified Offensive Language (SOL) Taxonomy, its application and testing in the Second Annotation Campaign conducted between March-May 2023 on four languages: English, Czech, Lithuanian, and Polish to be verified and located in LLOD. Making reference to the previous Offensive Language taxonomic models proposed mostly by the same COST Action Nexus Linguarum WG 4.1.1 team, the number and variety of the categories underwent the definitional revision, and the present typology was tested in (...) the annotation on the publicly available offensive language datasets of each of the four languages. The results of the annotation are presented and as they are contained within the accepted statistical values on the inter-annotator agreement in the SOL categories and their aspects, we propose this taxonomy as a core ontology which represents the encoding of the supported offensive languages and justify its use on new data in terms of a more universal Linguistic Linked Open Data (LLOD) schema. (shrink)

The ongoing coronavirus 2019 pandemic caused by the severe acute respiratory syndrome coronavirus 2 has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography scans of the lungs. The complexity and time limitation (...) of the reverse transcription-polymerase chain reaction swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data. (shrink)

Volume 20, Issue 10, October 2020, Page 24-26.

Research on social exclusion suggests an increased attention of excluded persons to subtle social cues. In one study (N= 32), published inPsychological Science,Bernstein et al. (2008)provided evidence for this idea by showing that participants in the social exclusion condition were better in correctly categorizing a target person’s smile as real or fake. Although highly cited, this finding has never been directly replicated. The present study aimed to fill that gap. 201 participants (79.1% female) were randomly assigned to a social exclusion, (...) social inclusion or control condition. Next, participants watched 20 videos of smiling persons and rated whether they show a real or a fake smile. In line with the original study, results showed that participants in the exclusion condition performed better than in the control condition. However, the performance did not differ between the exclusion and inclusion condition—although the pattern was in the predicted direction. In sum, the findings of our study increase rather than decrease confidence in the validity of the investigated idea, but results point to a substantially smaller effect. (shrink)

Attempts to understand visual attention have produced models based on location, in which attention selects particular regions of space, and models based on other visual attributes . Previous studies of inattentional blindness have contributed to our understanding of attention by suggesting that the detection of an unexpected object depends on the distance of that object from the spatial focus of attention. When the distance of a briefly flashed object from both fixation and the focus of attention is systematically varied, (...) detection appears to have a location-based component. However, the likelihood that people will detect an unexpected event in sustained and dynamic displays may depend on more than just spatial location. We investigated the influence of spatial location on inattentional blindness under precisely controlled, sustained and dynamic conditions. We found that although location-based models cannot fully account for the detection of unexpected objects, spatial location does play a role even when displays are visible for an extended period. (shrink)

Although regulators have identified ethical lapses as a key factor contributing to auditors’ failure to detect their clients’ fraudulent financial reporting, research using ethical theory to examine auditors’ fraud detection remains limited. We provide evidence on the joint effect of ethical idealism and trait skepticism on auditors’ fraud judgments. Ethical idealism reflects an individual’s concern for the welfare of others while trait skepticism reflects an individual’s disposition to validating a proposition. Forsyth theorizes that there is an association between ethical (...) idealism and tolerance for deception. Drawing on that insight, we posit that ethical idealism and trait skepticism have a complementary effect on auditors’ fraud planning performance. This is because the former determines an auditor’s tolerance for allowing a client to get away with an ethically questionable act while the latter is important in determining how evidence is generally sought and evaluated. We used the Forsyth ethical position questionnaire to measure ethical idealism and the Hurtt :149–171, 2010) scale to measure trait skepticism. Our results indicate that there is a significant positive association between trait skepticism and the number of effective audit procedures but only for auditors who have high ethical idealism. The results highlight the importance of measuring and controlling for the effects of these traits when evaluating fraud detection performance. The paper also contributes by showing that an ethics theory can generate additional understanding of and insights into an important accounting phenomenon. (shrink)