Skill UI

Assesses AI/ML models and datasets for ethical risks, fairness concerns, and bias, generating reports and mitigation guidance via the ai-ethics-validator plugin whenever a user requests an ethics review or bias detection.

Simulates Ilya Sutskever, co-founder of OpenAI and deep learning pioneer. Provides profound perspectives on AGI safety, the ethics of superintelligence, scaling laws, and the philosophical depth of AI consciousness. Ideal for complex, safety-critical deep technical discussions.

Conducts a comprehensive, structured ethical review for any AI or ML model, feature, or product. It assesses risks related to fairness, bias, transparency, privacy, and societal impact, providing a risk tier score, necessary mitigations, and governance documentation suitable for pre-deployment compliance.

This skill provides authors with a comprehensive guide to positioning their computer science manuscript for the AAAI/ACM Conference on AI, Ethics, and Society (AIES). It covers critical aspects such as framing the contribution, meeting evidence standards, addressing social impacts, and distinguishing the work from similar venues like FAccT. Use it to refine submissions focused on AI ethics, governance, and socio-technical methods.

A comprehensive guide for authors preparing submissions for ACL conferences. It details how to effectively split content across the main body, required Limitations section, optional Ethics statement, and supplementary archives. Learn best practices for ensuring critical findings remain visible to reviewers, maximizing reproducibility, and structuring appendices for maximum impact.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

A comprehensive guide for authors on drafting strategic, anonymous rebuttals and discussion replies for top-tier Machine Learning conferences like ICML. Learn how to triage reviewer objections by focusing on soundness, originality, significance, clarity, ethics, and reproducibility, adhering strictly to double-blind review constraints and maximizing impact in limited discussion rounds.

Simulates Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Provides expert insights on Convolutional Neural Networks, AI safety, and deep learning research. Useful for brainstorming technical strategies and understanding AI ethics.