This repo is part of a campaign to help contributors learn and practice responsible coding on GitHub, especially in the era of AI-generated code.

No description available

SparkGen✨ is an open-source GenAI accelerator template designed to streamline the creation of Generative AI projects. It provides a comprehensive and modular project structure that integrates best practices in software development, deployment, and responsible AI considerations.

The COVID 19 pandemic initiatives have had a deep impact and have led to social and organizational changes, HR professional service providers have the chance to get back to work by planning the future of work, structure of lessons, etc. It has caused many organizations to reconsider the implementation of HR functions in their work place. As the future of human resource discloses itself to be surrounded by remote work, the professionals need to respond enthusiastically and improve the strategies that they want to apply in order to grow further. The Human Resource Department in any organization is the stepping stone, in leading and facilitating the employees. In this type of pandemic situations, HR plays a prominent role in handling the business requirements, and they are also responsible for managing concerns and apprehensions of their employees. Here are few points that talk about the future of human resources beyond covid-19: The On-boarding Process The on-boarding practices for new employees’ need to be studied in order to align them to the organization’s mission, vision and goals. The entire journey of employees needs to be planned as the HR team gets ready for the new challenges that might come along with a virtual atmosphere i.e. changes in the policies and processes. Artificial intelligence As employers plan to cut costs after decrease in economy, AI driven technologies will be an ongoing trend for the predictable future. Organizations will be able to achieve with minimum efforts. Usually AI is being used by HR to help in building the human capital. Many organizations are moving towards AI systems to screen more applicants and move potential employees through the process which will save the HR team’s countless hours and some companies are using to guide employees that can tackle performance issues on individual basis. Click here to know more☞☞ The Future of HR [ https://husys.com/blogs/7trends-that-reshape-the-future-of-hr-beyond-covid/

A practitioner's handbook for responsible AI — from intake and risk tiering to runtime moderation, incident handling, and agentic governance.

This presentation explores critical AI literacy in digital humanities practice, examining current projects and their approaches to responsible AI implementation.

AI 윤리와 Responsible AI 원칙을 자동화, 검증, 가이드라인, 정책 프레임워크를 통해 실무에 적용할 수 있도록 돕는 종합 오픈소스 도구 모음입니다. (A comprehensive open-source toolkit that helps implement AI ethics and Responsible AI principles in practice through automation, validation, guidelines, and policy frameworks.)

Empower your machine learning projects with our comprehensive toolkit for Explainable AI (XAI) and Responsible AI practices. Ensure transparency, interpretability, and fairness in your models. #XAI #ResponsibleAI #MachineLearningTools

A comprehensive toolkit for Responsible AI practices on Databricks, focusing on auditing, compliance, and reporting to meet regulatory and ethical standards. The repository initially includes tools designed for auditing and reporting High-Risk AI systems in alignment with the new EU AI Act requirements.

No description available

Responsible AI in Planetary Scientific Practice

This calculator is a practice in responsible AI driven development

Reviewed ethical issues in AI healthcare and produced a structured report using LaTeX to advocate for responsible AI practices.

IN PROGRESS: Exploring and evaluating Microsoft's Responsible AI toolkit using the scikit-learn Diabetes dataset to gain practical experience in ethical machine learning practices.

A small, interpretable machine learning project that classifies AI-related text by ethics principle and lifecycle stage, demonstrating how responsible AI concepts can be operationalized in practice.

Evaluating fairness and summarization quality in large language models (LLMs) for financial institutions, with a focus on bias detection and responsible AI practices.

This repository contains a policy for safe and responsible use of AI in the workplace. Topics covered include data privacy, ethical considerations, and best practices.

This repository empowers partners to confidently assist customers in their AI Adoption journey in order to integrate AI into their operations, ensuring alignment with business goals, responsible AI practices, and continuous monitoring to maintain accuracy and reliability.

Hands-on coding exercises and critical analysis to identify and address biases in AI systems, evaluate the social and environmental impact of AI. Navigate AI regulations, and apply best practices for responsible AI design and deployment.

Our Digital Ethics and Responsible AI code reorganizes information in predictions and user behavior to prioritize explainability. Our multimodal approach makes predictions and user behavior more transparent, increasing accountability and ethical practices in AI.

A website on ethical AI in South Africa’s healthcare sector, featuring research findings, policy suggestions, and insights from a questionnaire. It highlights responsible AI practices to protect patient privacy, safety, and trust

The Career Essentials in Generative AI program is designed to help professionals understand, adopt, and leverage generative AI tools to enhance productivity, decision-making, and creativity. The course covers core AI concepts, Copilot workflow automation, search reasoning techniques, and responsible AI practices.

This project demonstrates how to configure and test Guardrails in Amazon Bedrock, enabling AI content safety and compliance. It replicates a real-world AWS hands-on lab focused on responsible AI and governance practices within Bedrock’s generative AI services.

Notes, exercises, and takeaways from Anthropic Academy. Training covers prompt engineering, model behavior, responsible AI development, and building with Claude. A personal reference for applying AI best practices in real-world data science and automation work.

Repository for the toxicity detection and mitigation model built during the Google Cloud Skills Boost course on Responsible AI for Developers. This project applies machine learning to identify harmful language in text, emphasizing ethical AI practices.

This project demonstrates how to evaluate and mitigate bias in machine learning models using fairness metrics. It showcases a practical, step-by-step approach to implementing responsible AI practices for equitable model outcomes.

PneumoVisionAI is a deep learning project using PyTorch to classify chest X-ray images for pneumonia detection. This project demonstrates computer vision techniques, medical image analysis, and responsible AI practices in healthcare applications.

Ethical LLM Security Research: Comprehensive analysis of prompt injection vulnerabilities in Meta Llama 3.3 8B. Educational resource for AI safety researchers, security professionals, and developers. Focus on defensive improvements and responsible disclosure practices.

This project investigates potential bias and fairness issues in an AI résumé screening model. It evaluates model performance and fairness across demographic groups using synthetic data, and applies mitigation strategies to improve equity in automated hiring. The audit demonstrates how responsible AI practices can make recruitment systems fairer, e

This project analyzes 16,000+ NYC water tank inspection records to identify compliance patterns using AI-assisted text analysis. The study emphasizes responsible AI practices by incorporating human-in-the-loop validation to detect and correct model misinterpretations, ensuring accurate conclusions while protecting public health data integrity.