Hasil untuk "Ethics"

B. Parmar, R. Freeman, J. Harrison et al.

Sarah J. Tracy

U. Flick

1. What is qualitative research? 2. From an idea to a research question 3. How to design qualitative research 4. Sampling, selecting and access 5. Resources and stumbling blocks 6. Quality in qualitative research 7. Ethics in qualitative research 8. Verbal data 9. Ethnography and visual data 10. Analyzing qualitative data 11. Beyond method: Grounded Theory, Triangulation and Mixed Methods 12. Designing qualitative research: some conclusions

W. Shadish, T. D. Cook, D. Campbell

Michael E. Brown, L. Treviño

R. Kozinets

S. Merriam

J. Green, N. Thorogood

PART ONE: PRINCIPLES AND APPROACHES IN QUALITATIVE HEALTH RESEARCH Qualitative Methodology and Health Research Developing Qualitative Research Designs Responsibilities, Ethics and Values PART TWO: GENERATING DATA In-Depth Interviews Group Interviews Observational Methods Physical and Virtual Documentary Sources PART THREE: MANAGING AND ANALYSING DATA Beginning Data Analysis Developing Qualitative Analysis PART 4 QUALITATIVE RESEARCH IN PRACTICE Reading, Appraising and Integrating Qualitative Research Mixing Methods and Designs Writing Up and Disseminating

A. Carroll

There is an impressive history associated with the evolution of the concept and definition of corporate social responsibility (CSR). In this article, the author traces the evolution of the CSR construct beginning in the 1950s, which marks the modern era of CSR. Definitions expanded during the 1960s and proliferated during the 1970s. In the 1980s, there were fewer new definitions, more empirical research, and alternative themes began to mature. These alternative themes included corporate social performance (CSP), stakeholder theory, and business ethics theory. In the 1990s, CSR continues to serve as a core construct but yields to or is transformed into alternative thematic frameworks.

E. Guba, Y. Lincoln

L. Treviño

D. Silverman

Part I: Theory and Method in Qualitative Research What Is Qualitative Research? Designing a Research Project Generalizing from Case Study Research Credible Qualitative Research Data Analysis Research Ethics Part II: Methods Interviews Focus Groups Ethnography Documents Naturally Occurring Talk Visual Images Part III: Implications Writing Your Report The Relevance of Qualitative Research The Potential of Qualitative Research: Eight Reminders

J. Habermas

Shixian Xie, Motahhare Eslami, John Zimmerman

Despite significant advances in responsible AI research, industry adoption remains limited, leaving many HCI contributions underutilized in practice. This position paper argues that current research often fails to account for the fundamental need for capitalist enterprises to create value. To achieve immediate real-world impact, responsible AI research must explore how to design responsibly within capitalism. We call for a move beyond the dichotomy of "ethics vs. business" toward a more productive framing of "ethics and business." We propose ideation as a practical design strategy for generating ethically preferable alternatives that also meet business objectives. By aligning ethics with enterprise realities, we expand the space of responsible design that can actually be built.

Ruomu Tan, Martin W Hoffmann

The integration of artificial intelligence (AI) into the industrial sector has not only driven innovation but also expanded the ethical landscape, necessitating a reevaluation of principles governing technology and its applications and awareness in research and development of industrial AI solutions. This chapter explores how AI-empowered industrial innovation inherently intersects with ethics, as advancements in AI introduce new challenges related to transparency, accountability, and fairness. In the chapter, we then examine the ethical aspects of several examples of AI manifestation in industrial use cases and associated factors such as ethical practices in the research and development process and data sharing. With the progress of ethical industrial AI solutions, we emphasize the importance of embedding ethical principles into industrial AI systems and its potential to inspire technological breakthroughs and foster trust among stakeholders. This chapter also offers actionable insights to guide industrial research and development toward a future where AI serves as an enabler for ethical and responsible industrial progress as well as a more inclusive industrial ecosystem.

Brady D. Lund, Ting Wang

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Aadarsh Padiyath, Casey Fiesler, Mark Guzdial et al.

Computing ethics education aims to develop students' critical reflection and agency. We need validated ways to measure whether our efforts succeed. Through two survey administrations (N=474, N=464) with computing students and professionals, we provide evidence for the validity of the Critical Reflection and Agency in Computing Index. Our psychometric analyses demonstrate distinct dimensions of ethical development and show strong reliability and construct validity. Participants who completed computing ethics courses showed higher scores in some dimensions of ethical reflection and agency, but they also exhibited stronger techno-solutionist beliefs, highlighting a challenge in current pedagogy. This validated instrument enables systematic measurement of how computing students develop critical consciousness, allowing educators to better understand how to prepare computing professionals to tackle ethical challenges in their work.

Myles Joshua Toledo Tan, Panayiotis V. Benos

The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et al. (2025), is essential, this approach risks oversimplifying complex socio-ethical issues, including fairness, privacy, and intersectionality. This rebuttal emphasizes the dangers of reducing multifaceted health disparities to quantifiable metrics and advocates for a more transdisciplinary approach. By incorporating insights from social sciences, ethics, and public health, AI systems can address the compounded effects of intersecting identities and safeguard sensitive data. Additionally, explainability and interpretability must be central to AI design, fostering trust and accountability. This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.

Susanne Hindennach, Mayar Elfares, Céline Gressel et al.

The proliferation of eye tracking in high-stakes domains - such as healthcare, marketing and surveillance - underscores the need for researchers to be ethically aware when employing this technology. Although privacy and ethical guidelines have emerged in recent years, empirical research on how scholars reflect on their own work remains scarce. To address this gap, we present two complementary instruments developed with input from more than 70 researchers: REFLECT, a qualitative questionnaire, and SPERET (Latin for "hope"), a quantitative psychometric scale that measures privacy and ethics reflexivity in eye tracking. Our findings reveal a research community that is concerned about user privacy, cognisant of methodological constraints, such as sample bias, and that possesses a nuanced sense of ethical responsibility evolving with project maturity. Together, these tools and our analyses offer a systematic examination and a hopeful outlook on reflexivity in eye-tracking research, promoting more privacy and ethics-conscious practice.

Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo et al.

Drones, or unmanned aerial vehicles (UAVs), have become powerful tools across domains-from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet drones also raise ethical concerns: the risk of disturbing the animals they aim to capture. This paper introduces CineWild, an autonomous UAV framework that combines robotics, cinematography, and ethics. Built on model predictive control, CineWild dynamically adjusts flight paths and camera settings to balance cinematic quality with animal welfare. Key features include adaptive zoom for filming from acoustic and visual safe distances, path-planning that avoids an animal's field of view, and smooth, low-noise maneuvers. CineWild exemplifies interdisciplinary innovation-bridging engineering, visual storytelling, and environmental ethics. We validate the system through simulation studies and will release the code upon acceptance.