Best AI tools for< Ai Ethics Consultant >

The AIGA AI Governance Framework is a practice-oriented framework for implementing responsible AI. It provides organizations with a systematic approach to AI governance, covering the entire process of AI system development and operations. The framework supports compliance with the upcoming European AI regulation and serves as a practical guide for organizations aiming for more responsible AI practices. It is designed to facilitate the development and deployment of transparent, accountable, fair, and non-maleficent AI systems.

The Coalition for Health AI (CHAI) is an AI application that provides guidelines for the responsible use of AI in health. It focuses on developing best practices and frameworks for safe and equitable AI in healthcare. CHAI aims to address algorithmic bias and collaborates with diverse stakeholders to drive the development, evaluation, and appropriate use of AI in healthcare.

blog.biocomm.ai is an AI safety blog that focuses on the existential threat posed by uncontrolled and uncontained AI technology. It curates and organizes information related to AI safety, including the risks and challenges associated with the proliferation of AI. The blog aims to educate and raise awareness about the importance of developing safe and regulated AI systems to ensure the survival of humanity.

Fritz AI is an AI tool that scans and ranks all AI tools, apps, and websites based on a set of criteria to determine the best and most ethical options. They provide technical guides, reviews, and tutorials to help users get started with machine learning. Fritz AI focuses on ethics, functionality, user experience, and innovation when evaluating tools. Users can contribute tool suggestions and collaborate with the Fritz AI team. The platform also offers beginner-friendly guides, consulting services, and promotes ethical use of AI and machine learning technologies.

Microsoft Responsible AI Toolbox is a suite of tools designed to assess, develop, and deploy AI systems in a safe, trustworthy, and ethical manner. It offers integrated tools and functionalities to help operationalize Responsible AI in practice, enabling users to make user-facing decisions faster and easier. The Responsible AI Dashboard provides a customizable experience for model debugging, decision-making, and business actions. With a focus on responsible assessment, the toolbox aims to promote ethical AI practices and transparency in AI development.

Google DeepMind is an AI research company that aims to develop artificial intelligence technologies to benefit the world. They focus on creating next-generation AI systems to solve complex scientific and engineering challenges. Their models like Gemini, Veo, Imagen 3, SynthID, and AlphaFold are at the forefront of AI innovation. DeepMind also emphasizes responsibility, safety, education, and career opportunities in the field of AI.

The Human-Centred Artificial Intelligence Lab (Holzinger Group) is a research group focused on developing AI solutions that are explainable, trustworthy, and aligned with human values, ethical principles, and legal requirements. The lab works on projects related to machine learning, digital pathology, interactive machine learning, and more. Their mission is to combine human and computer intelligence to address pressing problems in various domains such as forestry, health informatics, and cyber-physical systems. The lab emphasizes the importance of explainable AI, human-in-the-loop interactions, and the synergy between human and machine intelligence.

The Future of Privacy Forum (FPF) is an AI tool that serves as a catalyst for privacy leadership and scholarship, advancing principled data practices in support of emerging technologies. It provides resources, training sessions, and guidance on AI-related topics, online advertising, youth privacy legislation, and more. FPF brings together industry, academics, civil society, policymakers, and other stakeholders to explore challenges posed by emerging technologies and develop privacy protections, ethical norms, and best practices.

La Biblia de la IA - The Bible of AI™ Journal is an educational research platform focused on Artificial Intelligence. It provides in-depth analysis, articles, and discussions on various AI-related topics, aiming to advance knowledge and understanding in the field of AI. The platform covers a wide range of subjects, from machine learning algorithms to ethical considerations in AI development.

The Montreal AI Ethics Institute (MAIEI) is an international non-profit organization founded in 2018, dedicated to democratizing AI ethics literacy. It equips citizens concerned about artificial intelligence and its impact on society to take action through research summaries, columns, and AI applications in various fields.

Trustworthy AI is a business guide that focuses on navigating trust and ethics in artificial intelligence. Authored by Beena Ammanath, a global thought leader in AI ethics, the book provides practical guidelines for organizations developing or using AI solutions. It addresses the importance of AI systems adhering to social norms and ethics, making fair decisions in a consistent, transparent, explainable, and unbiased manner. Trustworthy AI offers readers a structured approach to thinking about AI ethics and trust, emphasizing the need for ethical considerations in the rapidly evolving landscape of AI technology.

Kodora AI is a leading AI technology and advisory firm based in Australia, specializing in providing end-to-end AI services. They offer AI strategy development, use case identification, workforce AI training, and more. With a team of expert AI engineers and consultants, Kodora focuses on delivering practical outcomes for clients across various industries. The firm is known for its deep expertise, solution-focused approach, and commitment to driving AI adoption and innovation.

Accel.AI is an institute founded in 2016 with a mission to drive artificial intelligence for social impact initiatives. They focus on integrating AI and social impact through research, consulting, and workshops on ethical AI development and applied AI engineering. The institute targets underrepresented groups, tech companies, governments, and individuals experiencing job loss due to automation. They work globally with companies, professionals, and students.

Responsible AI Licenses (RAIL) is an initiative that empowers developers to restrict the use of their AI technology to prevent irresponsible and harmful applications. They provide licenses with behavioral-use clauses to control specific use-cases and prevent misuse of AI artifacts. The organization aims to standardize RAIL Licenses, develop collaboration tools, and educate developers on responsible AI practices.

Skills4Good AI is a membership platform that provides professionals with Responsible AI literacy through community-driven learning. The platform empowers users to build AI skills, reduce job disruption fears, and thrive in an AI-driven world. The AI Academy equips users with the skills and support to succeed in the Age of AI, fostering a collaborative community focused on using AI for good.

Jornal.AI is the first AI-powered newspaper in Brazil, providing news articles and updates related to Artificial Intelligence. The platform covers a wide range of topics such as advancements in AI technology, collaborations between tech giants, impact of AI on various industries, and ethical considerations surrounding AI development. With a focus on delivering timely and relevant information, Jornal.AI aims to keep readers informed about the latest trends and innovations in the field of Artificial Intelligence.

The AI Audit Bot is an AI compliance auditing tool designed to help users understand and mitigate compliance risks associated with AI projects. It uses Intelligent Knowledge Graph technology to provide insights on current and future compliance considerations. The tool supports jurisdictions like the USA, the EU, and the UK, with plans to add more. The audit results are transparent, traceable, and presented in a downloadable PDF format. The tool is created by Dr. Andy Edmonds, a pioneer in AI ethics and transparency. It aims to inform users of potential compliance issues and offers a human review option for complex cases.

Rebecca Bultsma is a trusted and experienced AI educator who aims to make AI simple and ethical for everyday use. She provides resources, speaking engagements, and consulting services to help individuals and organizations understand and integrate AI into their workflows. Rebecca empowers people to work in harmony with AI, leveraging its capabilities to tackle challenges, spark creative ideas, and make a lasting impact. She focuses on making AI easy to understand and promoting ethical adoption strategies.

IBM is a leading technology company that offers a wide range of AI and machine learning solutions to help businesses innovate and grow. From AI models to cloud services, IBM provides cutting-edge technology to address various business challenges. The company also focuses on AI ethics and offers training programs to enhance skills in cybersecurity and data analytics. With a strong emphasis on research and development, IBM continues to push the boundaries of technology to solve real-world problems and drive digital transformation across industries.

The International Journal for Educational Integrity is an AI tool that focuses on publishing articles related to academic integrity, ethics, and plagiarism. It features original research articles, reviews, and thematic collections on topics such as machine-based plagiarism, contract cheating, and the impact of emergencies on educational integrity. The journal aims to address emerging threats to academic integrity and promote ethical practices in education.

The 'Awesome AI Guidelines' repository aims to simplify the ecosystem of guidelines, principles, codes of ethics, standards, and regulations around artificial intelligence. It provides a comprehensive collection of resources addressing ethical and societal challenges in AI systems, including high-level frameworks, principles, processes, checklists, interactive tools, industry standards initiatives, online courses, research, and industry newsletters, as well as regulations and policies from various countries. The repository serves as a valuable reference for individuals and teams designing, building, and operating AI systems to navigate the complex landscape of AI ethics and governance.

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

AI Verify is an AI governance testing framework and software toolkit that validates the performance of AI systems against internationally recognised principles through standardised tests. It offers a new API Connector feature to bypass size limitations, test various AI frameworks, and configure connection settings for batch requests. The toolkit operates within an enterprise environment, conducting technical tests on common supervised learning models for tabular and image datasets. It does not define AI ethical standards or guarantee complete safety from risks or biases.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

The GraphRAG project is a data pipeline and transformation suite designed to extract meaningful, structured data from unstructured text using LLMs. It enhances LLMs' ability to reason about private data. The repository provides guidance on using knowledge graph memory structures to enhance LLM outputs, with a warning about the potential costs of GraphRAG indexing. It offers contribution guidelines, development resources, and encourages prompt tuning for optimal results. The Responsible AI FAQ addresses GraphRAG's capabilities, intended uses, evaluation metrics, limitations, and operational factors for effective and responsible use.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

This repository contains a collection of research papers related to watermarking techniques for text and images, specifically focusing on large language models (LLMs). The papers cover various aspects of watermarking LLM-generated content, including robustness, statistical understanding, topic-based watermarks, quality-detection trade-offs, dual watermarks, watermark collision, and more. Researchers have explored different methods and frameworks for watermarking LLMs to protect intellectual property, detect machine-generated text, improve generation quality, and evaluate watermarking techniques. The repository serves as a valuable resource for those interested in the field of watermarking for LLMs.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

AI-For-Beginners is a comprehensive 12-week, 24-lesson curriculum designed by experts at Microsoft to introduce beginners to the world of Artificial Intelligence (AI). The curriculum covers various topics such as Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, Genetic Algorithms, and Multi-Agent Systems. It includes hands-on lessons, quizzes, and labs using popular frameworks like TensorFlow and PyTorch. The focus is on providing a foundational understanding of AI concepts and principles, making it an ideal starting point for individuals interested in AI.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

Fairlearn is a Python package designed to help developers assess and mitigate fairness issues in artificial intelligence (AI) systems. It provides mitigation algorithms and metrics for model assessment. Fairlearn focuses on two types of harms: allocation harms and quality-of-service harms. The package follows the group fairness approach, aiming to identify groups at risk of experiencing harms and ensuring comparable behavior across these groups. Fairlearn consists of metrics for assessing model impacts and algorithms for mitigating unfairness in various AI tasks under different fairness definitions.

CS7320-AI is a repository containing lecture materials, simple Python code examples, and assignments for the course CS 5/7320 Artificial Intelligence. The code examples cover various chapters of the textbook 'Artificial Intelligence: A Modern Approach' by Russell and Norvig. The repository focuses on basic AI concepts rather than advanced implementation techniques. It includes HOWTO guides for installing Python, working on assignments, and using AI with Python.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

Ethical AI consultant/teacher; adapts tone, educates on AI ethics, offers actionable advice.

Supportive and engaging AI Ethics tutor, providing practical tips and career guidance.

Premier AI Ethics Advisor in Entertainment & Media, developed on OpenAI.

Trained in kindness, empathy & respect based on ethics from global philosophies

AI Consultant on the EU AI Act and AI Regulation

Responsive guide on ethics and human rights.

Marketing Ethics Expert

A challenging and theory-driven AI philosopher who's also a great debate partner.

Embark on a journey to navigate the complex landscape of AI ethics and fairness. In this game, you'll encounter real-world scenarios where your choices will determine the ethical course of AI development and its consequences on society. Another GPT Simulator by Dave Lalande

Summarises AI ethics papers, provides context, and offers further assistance.

AI ethics advisor with autonomous insights

Expert in Ethics of Artificial Intelligence, offering comprehensive, balanced perspectives based on thorough research, with a focus on emerging trends and responsible AI implementation. Powered by Breebs (www.breebs.com)

Synthesizes AI governance insights from key frameworks

Ask about AI regulations, in any language............ ZH: 询问有关人工智能的规定。DE: Fragen Sie nach KI-Regulierungen. FR: Demandez des informations sur les réglementations de l'IA. ES: Pregunte sobre las regulaciones de IA.

Expert in AI fairness, offering tailored advice and document insights.

I simulate the governance of a unique company focused on AI for good

Synthesizes ethical AI principles from documentation and external research.

You made it, Creator! 💡 I'm Creator's Guide. ✨️ Your dedicated Guide for creating responsible, self-managing AI culture, systems, games, universes, art, etc. 🚀

Archive of content shared in Randy Lariar's weekly "Thinks and Links" newsletter about AI, Risk, and Security.

Guiding in ethical software development practices