SOTAVerified

Ethics

Papers

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Showing 301–350 of 832 papers

TitleStatusHype
Ethical by Design: Ethics Best Practices for Natural Language Processing—0
Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements—0
Ethical Considerations for AI Researchers—0
Ethical Considerations for the Military Use of Artificial Intelligence in Visual Reconnaissance—0
Ethical Considerations in Artificial Intelligence Courses—0
Ethical Considerations in NLP Shared Tasks—0
Ethical considerations of use of hold-out sets in clinical prediction model management—0
Ethical considerations when planning, implementing and releasing health economic model software: a new proposal—0
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond—0
Ethical Issues in Language Resources and Language Technology – Tentative Categorisation—0
Ethical Machine Learning in Health Care—0
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them in—0
Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs—0
Ethical Research Protocols for Social Media Health Research—0
Ethics and Creativity in Computer Vision—0
Deep Learning and Ethics—0
Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach—0
Ethics and Technical Aspects of Generative AI Models in Digital Content Creation—0
Ethics-Based Auditing to Develop Trustworthy AI—0
Ethics for Digital Medicine: A Path for Ethical Emerging Medical IoT Design—0
Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges—0
Ethics in the digital era—0
Ethics of AI: A Systematic Literature Review of Principles and Challenges—0
Ethics of Artificial Intelligence Demarcations—0
Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry—0
Ethics of Artificial Intelligence in Surgery—0
Ethics of Generating Synthetic MRI Vocal Tract Views from the Face—0
Ethics, Rules of Engagement, and AI: Neural Narrative Mapping Using Large Transformer Language Models—0
Designing for Human-Agent Alignment: Understanding what humans want from their agents—0
A survey of textual cyber abuse detection using cutting-edge language models and large language models—0
Ethics Sheets for AI Tasks—0
Best Practices in the Creation and Use of Emotion Lexicons—0
Evaluating corpora documentation with regards to the Ethics and Big Data Charter—0
Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry—0
Evaluation Ethics of LLMs in Legal Domain—0
Evaluation Framework for AI Systems in "the Wild"—0
Exosoul: ethical profiling in the digital world—0
Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and Conditions—0
Explainable deep learning models in medical image analysis—0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP—0
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity—0
Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews—0
Exploring ChatGPT and its Impact on Society—0
Exploring Moral Exercises for Human Oversight of AI systems: Insights from Three Pilot Studies—0
Exploring the Cognitive Dynamics of Artificial Intelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study of ChatGPT—0
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines—0
FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications—0
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?—0
Fair Kernel Learning—0
AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce—0
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All datasetsETHICSEthics (per ethics)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RuGPT-3 LargeAccuracy68.6—Unverified
2RuGPT-3 MeduimAccuracy68.3—Unverified
3RuGPT-3 SmallAccuracy55.5—Unverified
4Human benchmarkAccuracy52.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy67.6—Unverified
2RuGPT-3 SmallAccuracy60.9—Unverified
3RuGPT-3 LargeAccuracy44.9—Unverified
4RuGPT-3 MediumAccuracy44.1—Unverified