SOTAVerified

Ethics

Papers

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Showing 751–800 of 832 papers

TitleStatusHype
The State of AI Ethics Report (Volume 5)—0
The State of Documentation Practices of Third-party Machine Learning Models and Datasets—0
The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists—0
The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems—0
The Tragedy of the AI Commons—0
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability—0
The Virtuous Machine - Old Ethics for New Technology?—0
Three Kinds of AI Ethics—0
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods—0
Too sick for surveillance: Can federal HIV service data improve federal HIV surveillance efforts?—0
Toward Constraint Compliant Goal Formulation and Planning—0
Toward Ethical AIED—0
Towards a Feminist Metaethics of AI—0
Towards a Formalisation of Value-based Actions and Consequentialist Ethics—0
Towards a Framework Combining Machine Ethics and Machine Explainability—0
Towards a Governance Framework for Brain Data—0
Towards AI Logic for Social Reasoning—0
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach—0
Towards an Environmental Ethics of Artificial Intelligence—0
Towards An Ethics-Audit Bot—0
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens—0
Towards a Practical Ethics of Generative AI in Creative Production Processes—0
Towards a Praxis for Intercultural Ethics in Explainable AI—0
Exploring and steering the moral compass of Large Language ModelsCode0
EALM: Introducing Multidimensional Ethical Alignment in Conversational Information RetrievalCode0
Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive LearningCode0
Learning From Revisions: Quality Assessment of Claims in Argumentation at ScaleCode0
Learning Human Action Recognition Representations Without Real HumansCode0
ACL Ready: RAG Based Assistant for the ACL ChecklistCode0
More RLHF, More Trust? On The Impact of Preference Alignment On TrustworthinessCode0
Towards a multi-stakeholder value-based assessment framework for algorithmic systemsCode0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine TranslationCode0
A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach ServicesCode0
HumaniBench: A Human-Centric Framework for Large Multimodal Models EvaluationCode0
Decorrelation using Optimal TransportCode0
Surveying Professional Writers on AI: Limitations, Expectations, and FearsCode0
ApplE: An Applied Ethics Ontology with Event ContextCode0
What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerging in Online Social Movements through the Latent Dirichlet Allocation ModelCode0
Analyzing the Safety of Japanese Large Language Models in Stereotype-Triggering PromptsCode0
Data Defenses Against Large Language ModelsCode0
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning AgentsCode0
A Group-Specific Approach to NLP for Hate Speech DetectionCode0
Semantics derived automatically from language corpora contain human-like biasesCode0
When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social DilemmasCode0
An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical PerspectivesCode0
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language ProcessingCode0
A History of Philosophy in Colombia through Topic ModellingCode0
Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative ValuesCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
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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