SOTAVerified

Ethics

Papers

Showing 426–450 of 832 papers

TitleStatusHype
Applying Standards to Advance Upstream & Downstream Ethics in Large Language Models—0
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning—0
The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices—0
AI Imagery and the Overton Window—0
Responsible Design Patterns for Machine Learning PipelinesCode0
RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems—0
AI Audit: A Card Game to Reflect on Everyday AI Systems—0
Mapping ChatGPT in Mainstream Media to Unravel Jobs and Diversity Challenges: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis—0
Deep Learning and Ethics—0
Science in the Era of ChatGPT, Large Language Models and Generative AI: Challenges for Research Ethics and How to Respond—0
On the Origins of Bias in NLP through the Lens of the Jim Code—0
Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs—0
Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems—0
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors—0
The Ethics of AI in Games—0
ChatGPT: Vision and Challenges—0
Asymmetric quantum decision-making—0
The Future of Artificial Intelligence (AI) and Machine Learning (ML) in Landscape Design: A Case Study in Coastal Virginia, USA—0
Uncertain Machine Ethical Decisions Using Hypothetical RetrospectionCode0
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation—0
Towards ethical multimodal systems—0
An Audit Framework for Adopting AI-Nudging on Children—0
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare—0
Towards a Praxis for Intercultural Ethics in Explainable AI—0
A Group-Specific Approach to NLP for Hate Speech DetectionCode0
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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