Abstract Artificial Intelligence Systems Research Articles

Exploring the bioethical implications of using artificial intelligence in writing research proposals

Abstract Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.

Artificial intelligence and clinical decision support: clinicians' perspectives on trust, trustworthiness, and liability.

Artificial intelligence (AI) could revolutionise health care, potentially improving clinician decision making and patient safety, and reducing the impact of workforce shortages. However, policymakers and regulators have concerns over whether AI and clinical decision support systems (CDSSs) are trusted by stakeholders, and indeed whether they are worthy of trust. Yet, what is meant by trust and trustworthiness is often implicit, and it may not be clear who or what is being trusted. We address these lacunae, focusing largely on the perspective(s) of clinicians on trust and trustworthiness in AI and CDSSs. Empirical studies suggest that clinicians' concerns about their use include the accuracy of advice given and potential legal liability if harm to a patient occurs. Onora O'Neill's conceptualisation of trust and trustworthiness provides the framework for our analysis, generating a productive understanding of clinicians' reported trust issues. Through unpacking these concepts, we gain greater clarity over the meaning ascribed to them by stakeholders; delimit the extent to which stakeholders are talking at cross purposes; and promote the continued utility of trust and trustworthiness as useful concepts in current debates around the use of AI and CDSSs.

The Virtues of Interpretable Medical Artificial Intelligence.

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are "black boxes." The initial response in the literature was a demand for "explainable AI." However, recently, several authors have suggested that making AI more explainable or "interpretable" is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a "lethal prejudice." In this article, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to-and perhaps even harm-patients.

Digital IDs for advanced robotics systems as a regulatory infrastructure

Abstract Artificial intelligence (AI) has been increasingly performing legitimate tasks on behalf of humans. However, the legal aspects of this task performance are critical, given the unprecedented ability of AI to self-learn and act reasonably and independently. This article therefore identifies the critical regulatory aspects of AI that authorities and policymakers should be aware of, with regard to whether AI should be regulated now or at a later stage in its development. Nevertheless, the absence of a specific law, literally designated as an Artificial Intelligence Law, does not necessarily mean that AI is unregulated. This research therefore argues that AI is already indirectly regulated in several aspects. It is subsequently found that broad regulation could be premature, given that some types of AI systems are not yet specific in their form, or have yet to mature. On a global level, it is argued in this paper that it would be sensible to establish an international organization sooner rather than later, as AI grows in complexity and becomes increasingly transnational. Therefore, this research presents some of the possible obstacles this proposed organization may face. On a national level, it is suggested that the time has come to establish a regulatory infrastructure as a pre-regulatory phase, which would render future AI regulation more feasible. In particular, AI needs to be identified under a national digital identification scheme, as this would make it more regulatable and manageable. This is a necessary step to enable the authorities to confront the myriad legal issues and challenges in the longer term. While establishing a digital ID scheme for AI systems, discussions over the liability associated with AI’s wrongful acts would be especially productive.

Artificial Intelligence and Data Protection: How to Reconcile Both Areas from the European Law Perspective

Abstract Artificial intelligence (AI) is the latest in a long wave of disruptive technology, offering significant benefits but also creating risks if deployed in an uncontrolled manner. Recognizing this, many countries around the world, including Vietnam, are making efforts to research and build a legal framework to regulate and manage the development of AI to ensure that this technology supports their socio-economic development. In this context, the legislative experience of the European Union, one of world’s leaders in digital technology, is of high significance. The European GDPR, adopted in 2016, has become the international standard in terms of data protection. The EU AI Act, which is currently in a draft stage and will come into force not before end of 2024, sets out horizontal rules for the development, commodification and use of AI-driven products, services and systems, including the AI system and data protection. This article particularly scrutinizes the interplay between both the AI Act/AI systems and the GDPR and considers the regulatory state of play as at October 2022.

Artificial Intelligence and Effective Governance: Legal Framework

Artificial intelligence (AI) use in the state governance structures is obviously on the rise. Cognitive technologies have potential to transform the government sector — by reducing expenses, mundane chores, coping with resource limitations, making more accurate projections, and implementing AI into an array of organizational processes and systems. Methods. General research methods: analysis, synthesis, logical method were employed to study certain concepts and legal categories and their interrelations (artificial intelligence, artificial intelligence technologies, governance system, machine-readable law, digital state, automated decision-making, etc.) and develop insights into public relations amid proactive use of artificial intelligence systems and technologies in the governance system. Comparative legal research method was used to discern dynamics and further trends in legal relations, as well as to compare approaches of foreign countries to regulating AI systems and technologies. Prognostic method was applied to project the future of the Russian legislation as concerns building effective legal framework to regulate AI systems and technologies in the governance system. Technical legal (dogmatic) method helped develop legal foundation for the use of technologies and AI systems in the governance sphere. The analysis showed promising theoretical and practical avenues of modern law development in the aspect of artificial intelligence: the concept of artificial intelligence within the conceptual legal framework was described; legal regulation of administrative processes and its specifics were defined; ethics and principles of artificial intelligence application in governance were stressed, which involves restrictions of AI use in automated decision-making; stipulating the status of informed consent in the legislation in case an automated decision is made; the procedure which allows prohibiting the use of automated decision was established, as well as the procedure of AI risk assessment in the governance system, ensuring proper data protection and independent security monitoring.

Artificial Intelligence and Data Protection: A Comparative Analysis of AI Regulation through the Lens of Data Protection in the EU and Brazil

Abstract Artificial intelligence (AI) is already a major part of our daily lives. From unlocking our smartphones with our faces to receiving film recommendations on streaming platforms, AI is part of our routines. In recent years, a widespread adoption of AI technologies both by public and private agencies has been observed. Notwithstanding the many conveniences it has created, the use of AI also involves many risks for people individually and for society as a whole. For instance, it may jeopardise fundamental rights such as privacy and data protection or even intensify existing discrimination against minorities. For this reason, various nations are now facing the challenge of regulating AI without limiting its development. In terms of data protection, the European General Data Protection Regulation (GDPR) has been consistently applied and enforced in the European Union (EU) and has inspired many other data protection laws that came after it, such as the one in Brazil. In Brazil, the General Data Protection Law (LGPD) has finally come into force and is slowly being enforced. In 2021, a series of legislative initiatives concerning the development and use of AI systems drew the attention of governments, academics and the tech industry around the world. In the EU, the European Commission released a proposal for regulation in April that presents harmonised rules on AI. Meanwhile, in Brazil, in September, the Chamber of Deputies approved a rather superficial bill aiming to regulate AI in the country. Thus, one can wonder: what is the impact of data protection laws on AI regulations? And how could Brazil benefit once again from following the EU’s lead on regulating AI? In order to answer these questions, this article begins by explaining the concept of AI. It then presents the relation between AI and privacy and data protection as well as the main principles that guide privacy and data protection under both EU and Brazilian data protection laws. Subsequently, it introduces the EU legal framework for AI and focuses on the risk-based approach. Later, it presents the proposed Brazilian bill, focusing on its main principles from a comparative perspective with the EU. Finally, it will conclude how Brazil can benefit from taking inspiration from the EU experience on AI regulation.

Projection: a mechanism for human-like reasoning in Artificial Intelligence

ABSTRACT Artificial Intelligence systems cannot yet match human abilities to apply knowledge to situations that vary from what they have been programmed for, or trained for. In visual object recognition, methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Here a component of this type of inference, called ‘projection’, is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations, across a range of AI domains, such as vision, robotics, or language. Finally, the relevance of projection to tackling the commonsense knowledge problem is discussed.

Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehicles

AbstractArtificial intelligence (AI) systems have become increasingly common and the trend will continue. Examples of AI systems include autonomous vehicles (AV), computer vision, natural language processing and AI medical experts. To allow for safe and effective deployment of AI systems, the reliability of such systems needs to be assessed. Traditionally, reliability assessment is based on reliability test data and the subsequent statistical modelling and analysis. The availability of reliability data for AI systems, however, is limited because such data are typically sensitive and proprietary. The California Department of Motor Vehicles (DMV) oversees and regulates an AV testing program, in which many AV manufacturers are conducting AV road tests. Manufacturers participating in the program are required to report recurrent disengagement events to California DMV. This information is being made available to the public. In this paper, we use recurrent disengagement events as a representation of the reliability of the AI system in AV, and propose a statistical framework for modelling and analysing the recurrent events data from AV driving tests. We use traditional parametric models in software reliability and propose a new non-parametric model based on monotonic splines to describe the event process and to estimate the cumulative baseline intensity function of the event process. We develop inference procedures for selecting the best models, quantifying uncertainty and testing heterogeneity in the event process. We then analyse the recurrent events data from four AV manufacturers, and make inferences on the reliability of the AI systems in AV. We also describe how the proposed analysis can be applied to assess the reliability of other AI systems. This paper has online supplementary materials.

Adopting AI in the Context of Knowledge Work: Empirical Insights from German Organizations

Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI in the context of knowledge work. Drawing on case study research in eight German organizations that have either implemented AI or are in the process of developing AI systems, we identify three pervasive changes that knowledge workers perceive: a shift from manual labor and repetitive tasks to tasks that involve reasoning and empathy, an emergence of new tasks and roles, and an emergence of new skill requirements. In addition, we identify three factors that are conducive to the development of AI systems in the context of knowledge work: leadership support, participative change management, and effective integration of domain knowledge. Theoretical and managerial implications are discussed.

The deployment of artificial intelligence in alternative dispute resolution: the AI augmented arbitrator

ABSTRACT Artificial Intelligence (AI) is reshaping the way legal services are delivered, which includes arbitrators in Alternative Dispute Resolution (ADR) proceedings. This trend is being accelerated by the coronavirus (COVID-19) pandemic, with many disputes being conducted online. Previously, legal tasks have usually been associated with human lawyers; however, increasingly autonomous computer systems may soon replace arbitrators in the delivery of legal services in ADR proceedings. This article evaluates the extent to which the deployment of AI may augment arbitrators in ADR proceedings and provides an analysis of how much legal work may be effectively performed by AI in ADR proceedings. The central focus is on the capabilities of AI systems against human arbitrators and the requirement for human intervention and supervision of AI systems deployed in ADR proceedings. This article extends the debate on AI and arbitrators where the focus is on the augmentation of legal services in ADR with AI systems and the implications therein. The doctrinal approach is employed to conduct the study. The primary objective of this article is to evaluate the extent to which the deployment of AI systems may augment the efficiency and productivity of legal services in ADR and to highlight the proposed recommendations.

The impact of artificial intelligence on learner\u2013instructor interaction in online learning

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors’ routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner–instructor interaction (inter alia, communication, support, and presence) has a profound impact on students’ satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner–instructor interaction, capturing students’ and instructors’ concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

Artificial Intelligence in Healthcare

Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

Worth of Artificial Intelligence in the Epoch of Modern Livestock Farming: A Review

Artificial intelligence (AI) is a human intelligence in machine encountered daily and impacts our lives. It is expected that the use of such technology in the livestock industry will automate the livestock processes and easy to manage. Biometric identification plays a key role in artificial intelligence which shows the individual identity, helps in the process of insurance and claim leakages, continue monitoring of farm animal is essential can be done with new technologies. Infra red temperature measurement camera is the newly added technology with sensor system in (AI). It is a temperature measuring device in the form of electromagnetic waves and the infrared radiation intensity. AI system consists of agent, sensor, actuators and effectors which are connected to cloud. It helps in the detection of estrus, animal diseases, body condition score and various physiological parameters using video surveillance data collection method. Artificial neural network is a branch of artificial intelligence (AI) which is based on a collection of connected units or nodes called artificial neurons and stored in a central database system. Sustainable economic future of dairy farms and to achieve 100% compliance rate. Modern dairy farms uses robotic system to deliver vaccines, machine milking and measurement of feed as per individual performance of the animal. AI analyzes the animal origin food quality traceability method from farm to fork. AI helps in the complete mechanized animal husbandry right from the birth of animal to production and food product. The future of AI in animal sector is not predictable, but advantages and daily increasing demand of AI over other sector will ensure future in animal sector as well.

In AI We Trust? Effects of Agency Locus and Transparency on Uncertainty Reduction in Human–AI Interaction

Abstract Artificial intelligence (AI) is increasingly used to make decisions for humans. Unlike traditional AI that is programmed to follow human-made rules, machine-learning AI generates rules from data. These machine-generated rules are often unintelligible to humans. Will users feel more uncertainty about decisions governed by such rules? To what extent does rule transparency reduce uncertainty and increase users’ trust? In a 2 × 3 × 2 between-subjects online experiment, 491 participants interacted with a website that was purported to be a decision-making AI system. Three factors of the AI system were manipulated: agency locus (human-made rules vs. machine-learned rules), transparency (no vs. placebic vs. real explanations), and task (detecting fake news vs. assessing personality). Results show that machine-learning AI triggered less social presence, which increased uncertainty and lowered trust. Transparency reduced uncertainty and enhanced trust, but the mechanisms for this effect differed between the two types of AI.

Bias in Artificial Intelligence Systems

Abstract Artificial intelligence systems are currently deployed in many areas of human activity. Such systems are increasingly assigned tasks that involve taking decisions about people or predicting future behaviours. These decisions are commonly regarded as fairer and more objective than those taken by humans, as AI systems are thought to be resistant to such influences as emotions or subjective beliefs. In reality, using such a system does not guarantee either objectivity or fairness. This article describes the phenomenon of bias in AI systems and the role of humans in creating it. The analysis shows that AI systems, even if operating correctly from a technical standpoint, are not guaranteed to take decisions that are more objective than those of a human, but those systems can still be used to reduce social inequalities.

A Hybrid Approach for Feature Extraction and Classification Using Machine Learning Techniques

Artificial intelligence (AI) solutions are used to help make choices that include a high precision of choices they recommend and a deep understanding of choices, so that the chiefs can trust them. Verifiable, non-emblematic learning methods have greater perceptive accuracy. Express, fair representation information learning method produces increasingly justifiable Approach’s. Hybrid AI systems analyses the data and exploratory characteristics of approach types. The fundamental purpose of this commitment is to differentiate between a proper AI strategy for choice of assistance that produces reliable and fairoutcomes, depending on the various AI techniques, which provide an analysis of different approaches either with special or similar datasets. Comprehensibility is measured subjectively, however, by the form of learning process and the scale of the resulting representation of knowledge. We need a hybrid approach for classification using feature extraction in preprocessing of imbalanced dataset (raw dataset) by normalization for better classification, accuracy, and minimization of Error Rate for the smaller amount of training of significant dataset and lesser amount of training time for a good result. Moreover, data preprocessing play very important role in the field of machine learning, the result is more accurate if your data is cleanedotherwise some misclassification occurs which tend to be problematic in classification problems. Therefore, hybrid comparative analysis and approach is required for the selection of dataset with respect to machine learning classifier that will having different results with different data sets based on the hybrid approach to achieve maximum best results against any dataset. Input with maximum accurate result will be reproduced from our hybrid approach because this approach shows which type of classifiers should be used under what type of dataset you have, meanwhile exception of the generous fact based on results should be different among different classifiers when applied to different dataset. After that, a comparative analysis of different algorithms with different dataset has made and a comparison shows the enhanced significant generic approach with the hybrid approach, a clear result in the form of accuracy, precision, recall and f1 score shows the results against the specific techniques against the dataset and that tell us about the accuracy and rate of misclassification error. At the end we will see which machine learning algorithm improves the accuracy for which type of dataset.

Judicial Artificial Intelligence Bias: A Survey and Recommendations

Artificial intelligence (AI) has continued to disrupt the way tasks are being carried out, finding its way into almost all facets of human existence, and advancing the development of human society. The AI revolution has made huge and significant inroad into diverse industries like health, energy, transport, retail, advertising, et cetera. AI has been found to assist in carrying out tasks more quickly and efficiently too. Tasks which were hitherto difficult have been simplified significantly through the use of AI. Slow adoption in judiciary has however been reported, compared to other sectors. A lot of factors have been attributed to this, with AI bias being an issue of concern. Decisions emanating from courts have a significant impact on an individual’s private and professional life. It is thus imperative to identify and deal with bias in any judicial AI system in order to avoid delivering a prejudiced and inaccurate decision, thereby possibly intensifying the existing disparities in the society. This paper therefore surveys judicial artificial intelligence bias, paying close attention to types and sources of AI bias in judiciary. The paper also studies the trust-worthy AI, the qualities of a trust-worthy artificial intelligence system and the expectations of users as it is being deployed to the judiciary, and concludes with recommendations in order to mitigate the AI bias in Judiciary.

Artificial intelligence using Nelder-Mead algorithm- based design and performance optimization of microstrip patch antenna

Artificial intelligence systems are one of the important machines in performing operations that are difficult to perform traditionally. Optimization is one of the difficult and delicate processes that AI can be used to accomplish, especially if the optimizations are too small for antennas like microstrip patch antenna. A Microstrip patch antenna is considered one of the most widely used antennas that vary from lightweight wireless devices to airplanes and airspaces applications. One of the most attractive points about those antennas is their lightweight, small size, and ease of fabrication process. Although this antenna has many advantages, it suffers from some drawbacks like low gain and limited bandwidth. In this paper, we are presenting an optimization process by using the Nelder-Mead algorithm to achieve a new design of patch antenna that offers a broader bandwidth and higher gain. This design is achieved by optimizing the dimensions of the width and the frequency of the antenna. The results show that this device is responding perfectly at 1.471GHz and the ranges of substrate dimensions and relative permittivity affect the device performance and behavior.

The role of artificial intelligence in disinformation

Abstract Artificial intelligence (AI) systems are playing an overarching role in the disinformation phenomenon our world is currently facing. Such systems boost the problem not only by increasing opportunities to create realistic AI-generated fake content, but also, and essentially, by facilitating the dissemination of disinformation to a targeted audience and at scale by malicious stakeholders. This situation entails multiple ethical and human rights concerns, in particular regarding human dignity, autonomy, democracy, and peace. In reaction, other AI systems are developed to detect and moderate disinformation online. Such systems do not escape from ethical and human rights concerns either, especially regarding freedom of expression and information. Having originally started with ascending co-regulation, the European Union (EU) is now heading toward descending co-regulation of the phenomenon. In particular, the Digital Services Act proposal provides for transparency obligations and external audit for very large online platforms’ recommender systems and content moderation. While with this proposal, the Commission focusses on the regulation of content considered as problematic, the EU Parliament and the EU Council call for enhancing access to trustworthy content. In light of our study, we stress that the disinformation problem is mainly caused by the business model of the web that is based on advertising revenues, and that adapting this model would reduce the problem considerably. We also observe that while AI systems are inappropriate to moderate disinformation content online, and even to detect such content, they may be more appropriate to counter the manipulation of the digital ecosystem.