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21 results for “AI ethics”
Story Map of the AI Ethics Lab of the Austrian Institute of Technology (AIT)
<p>The Co-Change Lab at AIT, the Austrian Institute of Technology, focuses on addressing the promises and challenges associated with research work on and the application of machine learning and artificial intelligence. An interdisciplinary team of social and data scientists is working on AI ethics.</p>
Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects - Supplementary Material
<p>This is the Supplementary Material provided for the work accepted on HICSS 2021.</p> <p>Title of work: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects.</p> <p> </p> <p> </p> <p>This dataset depicts 589 GitHub README files explored in our article. We devised each repository into 4 categories. AI Applications (78), reference lists (486), Explainable AI tool (6), Ethical AI tool (15). Moreoveer, 4 repositories could not be found, and 181 of them had a Programming Language. In addition, we classified them according to our judgement of Interesting (82) or Great (33), regarding the objectives of our work. 75 repositories were not related to AI Ethics, and 7 of them mentioned COVID-19 in their repositories in some manner. Highlighted repositories in red pertain to categories 3 and 4, that is, tools for implementing AI Ethics, while those in yellow are marked as “Great”.</p> <p>Explainable AI tool and Ethical AI tool were added to 21 occurrences of tools for implementing AI ethics publicly available in repositories. Furthermore, it is seen that many papers open-sourced their codes on GitHub (as in https://github.com/lopusz/awesome-interpretable-machine-learning), meaning that the academic field has also made good progress in implementing ethics in AI, hence, the combined energy of both sources fosters an enhanced debate and stimulates progress towards AI ethics in practice.</p>
Ethics guidelines for AI
<p>This is the dataset corresponding to the supplementary information (Table S2) published in the following article:</p> <p>Jobin, A., Ienca, M. & Vayena, E. The global landscape of AI ethics guidelines. <em>Nat Mach Intell</em> <strong>1</strong>, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2</p> <p>You are free to use any of its data on the condition of citing this dataset: Jobin, A., Ienca, M., & Vayena, E. (2019). Ethics guidelines for AI [Data set]. <em>Zenodo/The Authors</em>. https://doi.org/10.5281/zenodo.10966287</p>
AI Ethics
<p>This repository contains datasets to support our paper titled <code>The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis</code> submitted to IEEE Transactions on Artificial Intelligence.</p>
eDoer - A Human-AI based Learning Environment - Ethics and Privacy related issues
<p>The eDoer platform was presented on the first Ethical, Legal, and Societal Aspects (ELSA) workshop of the German NFDI FAIR Data Spaces community. </p> <p>eDoer platform: <a href="http://edoer.eu/%C2%A0">http://edoer.eu/ </a></p>
Interactive ethical AI quiz
<p>Watch this demo video for a step-by-step explanation of how to use the <a href="https://interactive-ai-ethics-quiz.herokuapp.com/">Interactive Ethical AI quiz.</a></p>
Dataset of ICITS'24 - article "Ethics and AI in Higher Education: A Study on Students' Perceptions"
<p>This dataset encompasses responses from first-year undergraduate students enrolled in an ICT program at Fluminense Federal University. The survey was completed by 61 students via an online platform between June 28th and July 4th, 2023. The findings were subsequently published in an article titled "<strong>Ethics and AI in Higher Education: A Study on Students' Perceptions</strong>" presented at the ICITS'24 conference.</p> <p> </p> <p><strong>Article abstract</strong>:This initial study investigates the complex ethical concerns surrounding the integration of artificial intelligence (AI) in education, with a particular focus on undergraduate students' perceptions of AI tools in their academic efforts. A notable issue is the educational application of these tools, coupled with a pressing need for comprehensive public policies that ensure ethics and privacy are protected. Through qualitative research involving 61 students in an Information and Communication Technology (ICT) course, we explored the teaching of ethics related to the use of AI tools in an academic context. Our findings reveal a tendency among students to favor commercial AI tools that are not tailored for educational use, pointing to a potential shortfall in the educational AI domain. This study underscores the importance of promoting critical thinking and the responsible use of AI tools in academic settings. However, it is crucial to conduct further research with university students and instructors to refine and improve educational frameworks utilizing AI tools.</p>
AGIMUS Interviews with experts in AI ethics
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SLR AI Ethics - Screening records (Phase 1 & 2)
<p>These two spreadsheets detail the rigorous screening carried out by the four members of the research team during phase 1 and phase 2. </p>
New Challenges for Gender Equality in AI: Navigating the Ethical and Social Implications.- 1st SPATIAL podcast episode
<p>Gender bias can penetrate AI systems through multiple avenues, posing a significant challenge in ensuring fair and equitable outcomes. From historical biases ingrained in the data to biased data selection methods, these factors contribute to biased AI systems. Recognizing the implications of gender bias in AI is crucial, as it impacts various domains such as social media advertising, job recruitment, smart devices, facial recognition, and voice recognition. To strive for equity, it is essential to understand the interconnectedness of different social and political identities, emphasizing intersectionality and the need to avoid generalizing experiences.</p> <p>In the first episode of the <a href="https://spatial-h2020.eu/">SPATIAL </a>podcast, we had the privilege of conversing with Marcus Westberg, postdoctoral researcher and project manager at <a href="https://www.tudelft.nl/">TU Delft</a>, the coordinator of the SPATIAL project.</p>
Data from: A community-based approach to ethical decision-making in AI for health care
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Exploring Ethical Implications: Unraveling Factors Influencing Data Governance Awareness Behavior in Generative AI Chatbot
<p>Data set</p>
Teaching the Ethics of AI
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Teaching the Ethics of AI
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Conveying the Ethics of AI
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Conveying the Ethics of AI
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Guidelines for conducting ethical AI research in neurology: Supplementary materials
<p>Pre-emptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of pre-empting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.</p>
Guidelines for conducting ethical AI research in neurology: Supplementary materials
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The Next Wave: An Ethical Compass For AI-Based Software Development
<p>Ethical principles are fundamental for the long-term sustainable growth of AI/ML; however, recent research highlights that many projects have yet to full integrate these guidelines. This research work aims to assess the current rate of adoption of ethical principles in AI/ML within the software development space. We collected 96,254 pull requests from 28 AI/ML GitHub projects and randomly selected 400 pull requests for manual labeling based on the seven EU ethical guidelines. To address the challenge of scalability and consistency in manual labeling, we investigated the use of a zero-shot large language model (LLM), OpenAI’s GPT-4o. This<br>LLM was leveraged to automatically detect ethical AI principles in our sample of pull requests. Our findings demonstrate that GPT-4o has the potential to support ethical compliance in software development. Looking ahead, we envision automating the scanning of code changes for ethical concerns, similar to vulnerability detection models. This tool would flag high-risk pull requests for ethical review, aiding AI risk assessment in open-source projects and supporting the automatic generation of an AI Bill of Materials (AI BOM).</p>
Harms and Ethical Issues of Generative AI in Mental Health Care: A Delphi Study
<p>bibliometric data</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.