Responsible Artificial Intelligence Research Articles

Ethics of artificial intelligence in dermatology

The integration of artificial intelligence (AI) in dermatology holds promise for enhancing clinical accuracy, enabling earlier detection of skin malignancies, suggesting potential management of skin lesions and eruptions, and promoting improved continuity of care. AI implementation in dermatology, however, raises several ethical concerns. This review explores the current benefits and challenges associated with AI integration, underscoring ethical considerations related to autonomy, informed consent, and privacy. We also examine the ways in which beneficence, nonmaleficence, and distributive justice may be impacted. Clarifying the role of AI, striking a balance between security and transparency, fostering open dialogue with our patients, collaborating with developers of AI, implementing educational initiatives for dermatologists and their patients, and participating in the establishment of regulatory guidelines are essential to navigating ethical and responsible AI incorporation into dermatology.

Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa.

This perspective delves into the integration of artificial intelligence (AI) to enhance early diagnosis in hidradenitis suppurativa (HS). Despite significantly impacting Quality of Life, HS presents diagnostic challenges leading to treatment delays. We present a viewpoint on AI-powered clinical decision support system designed for HS, emphasizing the transformative potential of AI in dermatology. HS diagnosis, primarily reliant on clinical evaluation and visual inspection, often results in late-stage identification with substantial tissue damage. The incorporation of AI, utilizing machine learning and deep learning algorithms, addresses this challenge by excelling in image analysis. AI adeptly recognizes subtle patterns in skin lesions, providing objective and standardized analyses to mitigate subjectivity in traditional diagnostic approaches. The AI integration encompasses diverse datasets, including clinical records, images, biochemical and immunological data and OMICs data. AI algorithms enable nuanced comprehension, allowing for precise and customized diagnoses. We underscore AI's potential for continuous learning and adaptation, refining recommendations based on evolving data. Challenges in AI integration, such as data privacy, algorithm bias, and interpretability, are addressed, emphasizing the ethical considerations of responsible AI deployment, including transparency, human oversight, and striking a balance between automation and human intervention. From the dermatologists' standpoint, we illustrate how AI enhances diagnostic accuracy, treatment planning, and long-term follow-up in HS management. Dermatologists leverage AI to analyze clinical records, dermatological images, and various data types, facilitating a proactive and personalized approach. AI's dynamic nature supports continuous learning, refining diagnostic and treatment strategies, ultimately reshaping standards of care in dermatology.

Exploring the Impact of Artificial Intelligence Tools in Engineering Pedagogy: A Qualitative Survey of Academic Experiences

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.

Exploring the Impact of Artificial Intelligence Tools in Engineering Pedagogy: A Qualitative Survey of Academic Experiences

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.

Refusing participation: hesitations about designing responsible patient engagement with artificial intelligence in healthcare

ABSTRACT The rapidly expanding field of artificial intelligence (AI) is often accompanied by calls for parallel research on its societal implications. For research about AI in healthcare, this translates to some form of patient engagement. In this article, we question whether patient engagement and participation really contribute to responsible AI. We first summarise existing critiques of patient participation. We review the critiques of the critiques, themselves motivated by the wish to contribute, and not to leave the field solely to computer- and data scientists. In the final section, we express our doubts about the possibilities for developing positive, generative interventions, and explore ‘refusal’ and ‘hesitation’ as forms of critique and engagement. The conclusion presents a checklist for refusing patient participation, an addition to the growing repertoire of tools for patient participation and responsible innovation. The article draws on and contributes to the STS tradition of creative and speculative writing.

The Influence of Artificial Intelligence on Business Value in Digital Strategy: A Comprehensive Literature Review

As the digital landscape continues to evolve rapidly, businesses are increasingly turning to artificial intelligence (AI) to shape their digital strategies. This paper presents a comprehensive literature review that investigates the multifaceted influence of AI on business value within the digital realm. Drawing on a wide range of academic research, industry reports, and case studies, this review explores the transformative impact of AI on decision making, personalization, cost reduction, revenue generation, and supply chain optimization. It also delves into the role of AI in risk management, customer insights, and competitive advantage, while emphasizing the ethical considerations and regulatory implications of AI integration. Additionally, this review highlights the importance of talent development and strategic partnerships in maximizing AI's potential for enhancing business value. By 12 synthesizing key findings and trends, this paper offers valuable insights for businesses seeking to navigate the complex landscape of AI in digital strategy, emphasizing the critical need for responsible AI practices and continuous innovation to drive sustainable business value.

Intersection of AI and Healthcare

The rapid development of ChatGPT and other generative intelligence models has recently catalyzed the integration of artificial intelligence (AI) into medicine. This evolution raises critical challenges that require attention to technological literacy training in medical education. These generative intelligence models can create inaccurate information, known as hallucinations, and introduce bias into unforeseen workflows. Physician involvement remains pivotal in guiding AI applications, as they possess the most critical perspective on the impact of artificial intelligence on patient outcomes. Physicians must have a voice in AI development and contribute to fact-checking and risk reduction. The development of learning these tools in technology during medical school is vital in addressing these challenges. Institutions like the Western University of Health Sciences advocate for increased technological literacy among future healthcare providers. Improving technical proficiency in students will ensure responsible AI integration, potentially reducing healthcare disparities and empowering prospective providers for more patient-centric care.

Emergence of AI in Marketing and its Implications

The "Emergence of AI in Marketing and Its Implications" explores the profound impact of Artificial Intelligence (AI) on contemporary marketing strategies. Through an exhaustive examination of secondary data, this paper illuminates how businesses leverage AI to redefine customer experiences, enhance decision-making, and optimize operational efficiency. Delving into various applications, the paper uncovers how leading companies, exemplified by case studies on Amazon and Netflix, strategically deploy AI to gain competitive advantages in dynamic markets.
 While the benefits of AI in marketing are evident, the paper acknowledges the challenges inherent in this transformative shift. Ethical considerations, privacy concerns, and potential biases in algorithms demand nuanced responses. The conclusion emphasizes the necessity for businesses to adopt responsible AI practices, fostering transparency and accountability.
 This exploration underscores the dynamic nature of the AI-marketing landscape, emphasizing the need for businesses to stay agile and adaptive. As the intersection of technology and marketing evolves, the abstract envisions a future where businesses, armed with innovative AI applications, forge a redefined relationship with consumers. The paper concludes by calling for a balanced approach that integrates innovation with ethical considerations, charting a course toward a future where AI and marketing coalesce for the benefit of businesses and consumers alike.

AI in Autonomous Vehicles

The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap towards safer, more efficient, and technologically advanced transportation systems. This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration AI-Powered Perception and Decision-Making: Delving into the technological core, the abstract explores how AI empowers autonomous vehicles with sophisticated perception systems, such as computer vision and sensor fusion. It discusses the role of machine learning algorithms in real-time decision-making, enabling vehicles to adapt to dynamic road conditions and unforeseen circumstances. Challenges in Autonomy: Recognizing the complexity of autonomous systems, the abstract addresses challenges such as handling edge cases, ensuring robustness against adversarial attacks, and navigating regulatory and ethical considerations. It emphasizes the importance of addressing these challenges to foster public trust and acceptance of autonomous vehicles. Human-AI Interaction in Autonomous Vehicles: Examining the interface between humans and AI-driven vehicles, the abstract discusses the importance of designing intuitive and trustworthy communication channels. It explores advancements in natural language processing and gesture recognition, fostering seamless collaboration between humans and autonomous systems. Regulatory Landscape and Ethical Considerations: Recognizing the pivotal role of regulations, the abstract discusses the evolving regulatory landscape for autonomous vehicles. It delves into ethical considerations surrounding AI decisions in critical situations, underscoring the need for a harmonized approach to ensure responsible AI deployment in autonomous driving scenarios.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry.

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation

While the rise of artificial intelligence (AI) tools holds promise for delivering benefits, it is important to acknowledge the associated risks of their deployment. In this article, we conduct a focused literature review to address two central research inquiries concerning ChatGPT and similar AI tools. Firstly, we examine the potential pitfalls linked with the development and implementation of ChatGPT across the individual, organizational, and societal levels. Secondly, we explore the role of a multi-stakeholder responsible research and innovation framework in guiding chatbots’ sustainable development and utilization. Drawing inspiration from responsible research and innovation and stakeholder theory principles, we underscore the necessity of comprehensive ethical guidelines to navigate the design, inception, and utilization of emerging AI innovations. The findings of the focused review shed light on the potential perils of ChatGPT implementation across various societal levels, including issues such as devaluation of relationships, unemployment, privacy concerns, bias, misinformation, and digital inequities. Furthermore, the proposed multi-stakeholder Responsible Research and Innovation framework can empower AI stakeholders to proactively anticipate and deliberate upon AI’s ethical, social, and environmental implications, thus substantially contributing to the pursuit of responsible AI implementation.

ARTIFICIAL INTELLIGENCE IN EDUCATION: NAVIGATING THE NEXUS OF INNOVATION AND ETHICS FOR FUTURE LEARNING LANDSCAPES

Artificial intelligence (AI) holds the potential to revolutionize teaching and learning methodologies, tackle some of the largest issues facing education today, and hasten the achievement of SDG 4. The revolutionary effects of artificial intelligence (AI) on education are explored in depth in this study paper. Researcher examined the possible advantages, challenges, and ethical issues related to this changing paradigm by examining the incorporation of AI technology in several educational environments. In the present study, researchers have discussed the use of AI in education, focusing on its benefits like personalized learning, data-driven insights, and accessibility. It also discussed the drawbacks, such as data privacy, biases, and ethical issues. This study has also emphasized the importance of accountability, transparency, and justice in AI algorithms. It also discusses future developments in AI-driven education, such as adaptive learning, augmented reality, and the potential of AI to address global education issues. The focus is on promoting a balanced viewpoint and addressing ethical concerns in AI-driven education. The integration of Artificial Intelligence (AI) in education is a transformative process, offering a wide range of applications from predictive analytics to personalized learning platforms. However, ethical issues like privacy, bias, and transparency must be addressed to ensure responsible AI adoption in the future.

AI ethics on the road to responsible AI plant science and societal welfare

The swiftness of artificial intelligence (AI) progress in plant science begets relevant ethical questions with significant scientific and societal implications. Embracing a principled approach to regulation, ethics review and monitoring, and human-centric interpretable informed AI (HIAI), we can begin to navigate our voyage towards ethical and socially responsible AI.

Towards an AI policy framework in scholarly publishing

The rapid adoption of artificial intelligence (AI) tools in academic research raises pressing ethical concerns. I examine major publishing policies in science and medicine, uncovering inconsistencies and limitations in guiding AI usage. To encourage responsible AI integration while upholding transparency, I propose an enabling framework with author and reviewer policy templates.

Responsible AI in Farming: A Multi-Criteria Framework for Sustainable Technology Design

The continuous fusion of artificial intelligence (AI) and autonomous farming machinery (e.g., drones and field robots) provides a significant shift in the daily work experience of farmers. Faced with new technological developments, many risks and opportunities arise that need to be carefully translated into technological requirements to enable a sustainable production environment. Analyzing the complex relationship between social, ecological, and technological dependencies is a crucial step to understanding the different perspectives and systemic effects of technological functionalities. By providing a comprehensive overview of the state of the art, this article qualitatively analyzes the potential impact of AI on the autonomy of farmers and the technological developments to mitigate the risks. Fair data management practices, transparent AI approaches, and designs for an intuitive user experience are presented as key mechanisms for supporting responsible model development. Based on the defined social, technological, and ecological challenges in AI development, the knowledge to provide a high-level framework for the responsible creation of AI technologies is further systematized. By focusing on the multifaceted relationships and their effects on the autonomy of farmers, this article exemplifies the complex design decisions that must be faced in creating trustworthy and responsible AI tools.

A survey of energy concerns for software engineering

There is growing attention to energy efficiency in the software engineering field. This has been driven by modern technologies, for example, Internet of Things (IoT), Social Networking Services (SNS) and quantum computing. In addition to this, recent trends and concerns such as Environment, Social, and Governance (ESG) and human/societal/environmental well-being for responsible Artificial Intelligence (AI) have accelerated the use of energy efficient software. Despite this, energy concerns in this field have been less explored and studied. This limitation results in falling short to address and overcome greenability issues at the software level, and leaving critical challenges to be solved in this space. This study aims to address this limitation and fill the gap between previous studies. We survey green in software engineering framed by the ten knowledge areas of software engineering to not only cover the entire development life-cycle but also widen the scope of discussion to software process, method, and model management. Based on our comprehensive investigation, we discuss open challenges, trade-offs and implications of this study for both researchers and practitioners.

Responsible AI (RAI) in Manufacturing: A Qualitative Framework

Artificial Intelligence (AI) has profound economic influence in manufacturing, but its unmindful integration can also pose societal and environmental risks. This paper provides a quantified overview of manufacturing areas that are highly advanced in AI capability research, such as maintenance. Integrating Responsible AI (RAI) in further studies of those areas is essential to mitigate risks and deliver business benefits. To enable this, manufacturing specific RAI dimensions are defined to represent accountability, explainability, fairness, human-centricity, sustainability (Green AI) and privacy & security. Further, a qualitative RAI framework consisting of responsibility areas (human involvement, decision making, business focus, system design) is proposed. Practical considerations to align the framework with manufacturing requirements are made by discussing it within an AI systems lifecycle.

Responsible Artificial Intelligence and Journal Publishing

The aim of this opinion piece is to examine the responsible use of artificial intelligence (AI) in relation to academic journal publishing. The work discusses approaches to AI with particular attention to recent developments with generative AI. Consensus is noted around eight normative themes for principles for responsible AI and their associated risks. A framework from Shneiderman (2022) for human-centered AI is employed to consider journal publishing practices that can address the principles of responsible AI at different levels. The resultant AI principled governance matrix (AI-PGM) for journal publishing shows how countermeasures for risks can be employed at the levels of the author-researcher team, the organization, the industry, and by government regulation. The AI-PGM allows a structured approach to responsible AI and may be modified as developments with AI unfold. It shows how the whole publishing ecosystem should be considered when looking at the responsible use of AI—not just journal policy itself.

Artificial Intelligence Applications in Prostate Cancer Management: Success Stories and Future Ahead

Summary This review summarizes and provides an outlook for developments around the use of artificial intelligence (AI) in the diagnosis and treatment of prostate cancer. We searched existing literature on the design and development of new AI-based systems using a non-systematic approach. Areas targeted by AI include the diagnosis, Gleason scoring, biomarker identification, and prognosis of prostate cancer (PCa) from digitised histopathology, segmentation, detection, and classification of PCa from magnetic resonance imaging, AI applications for prostate ultrasound, AI in radiotherapy for PCa including synthetic computed tomography generation and treatment planning and AI in measuring and improving surgical outcomes and education. Recent work has focused on deep learning techniques. Algorithms have achieved results that outperform or are similar to those of experts. However, few proposed algorithms are clinically oriented and can be practically deployed. Future progress needs to be made in data availability, prospective evaluation, regulation, responsible AI, explainability, and practical aspects of clinical deployment.

Machine ex machina: A framework decentering the human in AI design praxis

Artificial intelligence (AI) design typically incorporates intelligence in a manner that is affirmatory of the superiority of human forms of intelligence. In this paper, we draw from relevant research and theory to propose a social-ecological design praxis of machine inclusivity that rejects the presumption of primacy afforded to human-centered AI. We provide new perspectives for how human-machine communication (HMC) scholarship can be synergistically combined with modern neuroscience’s integrated information theory (IIT) of consciousness. We propose an integrated theoretical framework with five design practice recommendations to guide how we might think about responsible and conscious AI environments of the future: symbiotic design through mutuality; connectomapping; morethan- human user storytelling, designing for AI conscious awakenings; and the revising of vernaculars to advance HMC and AI design. By adopting the boundaries HMC scholarship extends, we advocate for replacing ex machina mentalities with richer understandings of the more-than-human world formed by interconnected and integrated human, humanmade, and nonhuman conscious machines, not superior or inferior but each unique.