Skill UI

A comprehensive guide for researchers submitting NLP papers, detailing best practices for achieving high reproducibility. It instructs users to pin model versions, log data provenance, report compute costs, and adhere rigorously to the Responsible NLP checklist. Essential for ensuring academic integrity and enabling rigorous verification of ML experiments.

A comprehensive guide detailing the strict requirements for submitting research papers to EMNLP via the ACL Rolling Review (ARR). Key areas covered include template compliance, page length restrictions (long/short format), mandatory limitations sections, double-blind anonymization techniques, and adherence to the Responsible NLP checklist. Use this audit guide before submission to ensure full compliance and avoid desk-rejects.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This guide helps researchers determine the most appropriate academic venue for their responsible AI work. It provides detailed criteria and rigorous testing methods (like the Delete-the-equity test) to assess whether fairness, accountability, or transparency is a primary, core contribution, guiding authors away from merely 'bolted-on' ethical framing.

A comprehensive project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.

Provides comprehensive guidelines for ethical handling, data transparency, and documentation in New Media & Society research. It covers crucial issues like informed consent, anonymization best practices, scraping legal constraints (ToS), and methods for making qualitative or computational claims credible, ensuring responsible data sharing and academic rigor.

A comprehensive guide for structuring and positioning computer science manuscripts for The Web Conference (WWW). It advises authors on leveraging open-web data, focusing on web systems, web mining, and responsible AI, while adhering to the latest venue-specific standards and evidence requirements.

A rigorous guide for drafting high-stakes, technical security papers for the USENIX Security Symposium. It mandates a threat-model-first structure, requiring authors to quantify all security claims with measurable data to avoid hyperbole. The guide also details integrating responsible disclosure narratives and adhering to strict academic formatting rules.

This specialized skill is designed for deep forensic analysis of compromised Linux environments, specifically tracing the sequence of credential usage (tokens, keys, service accounts) that leads to lateral movement or privilege escalation. It helps map the decisive path from a recovered artifact to an accepted replay mechanism, identifying the exact trust edge or capability abuse responsible for the host-to-host pivot.

Audits AI-assisted legal, compliance, governance, and institutional decisions before reliance to determine whether the responsible human or institution has enough access to evidence, reasoning, uncertainty, authority, and review path to genuinely own the decision rather than merely approve, sign, or transmit it.

An expert advisor for implementing, auditing, and achieving certification under ISO/IEC 42001:2023. This skill guides organizations (AI providers and users) through establishing an AI Management System (AIMS). It covers critical areas including AI risk assessment, AI impact assessments, responsible AI principles, governance framework setup, and preparing for formal ISO certification.