「fairness」の検索結果

Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.

日本語の概要は準備中です。原文の説明を表示しています。

Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

日本語の概要は準備中です。原文の説明を表示しています。

Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.

日本語の概要は準備中です。原文の説明を表示しています。

Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.

日本語の概要は準備中です。原文の説明を表示しています。

Review IEEE conference, journal, Transactions, Letters, or magazine manuscripts from a technical reviewer perspective using routed IEEE review gates. Use when evaluating scope fit, novelty, validity, data, clarity, compliance, advancement, engineering significance, method soundness, experiment sufficiency, baseline fairness, figure/table quality, formatting risks, rejection risk, major revision strategy, pre-submission readiness, or the quality and specificity of reviewer comments that may be generic, careless, contradictory, or possibly AI-generated.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

日本語の概要は準備中です。原文の説明を表示しています。

Designs LLM, agent, responsible-AI, and multimodal evals. Use when measuring quality, fairness, grounding, safety, or judge reliability. Not for test harnesses (qa-agent-testing).

日本語の概要は準備中です。原文の説明を表示しています。

Builds ML pipelines: EDA, features, leakage checks, evaluation. Use when doing data science or explaining fairness, privacy, speech, vision-language, or diffusion mechanics.

日本語の概要は準備中です。原文の説明を表示しています。

Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.

日本語の概要は準備中です。原文の説明を表示しています。

Tokenomics Analysis Agent — supply mechanics, unlock schedules, vesting, inflation, staking economics, distribution fairness, and token utility with Tokenomics Score (0-100)

日本語の概要は準備中です。原文の説明を表示しています。

Audit AI systems for bias and fairness. TRIGGERS - Use when user needs help with ai-bias-audit related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

日本語の概要は準備中です。原文の説明を表示しています。

Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.

日本語の概要は準備中です。原文の説明を表示しています。

Document a machine learning model in a structured, transparent format for stakeholders, reviewers, and future maintainers. Use when the user says "document this model", "write a model card", "model documentation", "how should I document my ML model", "bias and fairness report", "model transparency", "what does this model do", "model handoff", "production model documentation", or needs to communicate what a model does, how it was built, where it works, and where it fails.

日本語の概要は準備中です。原文の説明を表示しています。

Pre-launch compliance and security gate for fintech apps: audit PCI DSS payment card handling, review financial API integrations for idempotency and error handling, evaluate fraud detection coverage, validate credit risk models for fairness, then run preflight checks.

日本語の概要は準備中です。原文の説明を表示しています。

Audit a job matching platform for relevance, fairness, and candidate experience. Triggers: building or reviewing job boards, ATS matching engines, internal mobility platforms, or workforce marketplaces.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze in-game economy systems including soft and hard currency source-sink balance, inflation projection modeling, loot table drop rate fairness and pity system evaluation, gacha probability disclosure, player marketplace health and price manipulation risks.

日本語の概要は準備中です。原文の説明を表示しています。

Audit a dynamic pricing engine for revenue optimization and fairness. Evaluates price elasticity models, competitive intelligence feeds, promotional ROI, markdown optimization, price image management, and legal compliance including Robinson-Patman and price gouging regulations..

日本語の概要は準備中です。原文の説明を表示しています。

Drafts a Notice of Removal under the Class Action Fairness Act (CAFA), 28 U.S.C. § 1332(d), transferring a state court class action to federal court. Covers minimal diversity, aggregate amount in controversy exceeding $5 million, 100+ class members, CAFA exceptions, unanimity, timeliness, and required exhibits. Use when a defendant needs to remove a state-filed class action to federal court within 30 days of service.

日本語の概要は準備中です。原文の説明を表示しています。

Holistic evaluation framework for language models measuring accuracy, calibration, robustness, and fairness

日本語の概要は準備中です。原文の説明を表示しています。

Expert en éthique des agents IA (tests de biais, équité, transparence, explicabilité, déploiement responsable)

日本語の概要は準備中です。原文の説明を表示しています。

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

日本語の概要は準備中です。原文の説明を表示しています。

Audit AI systems for bias and fairness. TRIGGERS - Use when user needs help with ai-bias-audit related tasks.

日本語の概要は準備中です。原文の説明を表示しています。