References
This page contains all the resources I've gathered over the past two years and used for my research during the creation of this library.
- Code of Ethics
- Google AI Blog: Introducing the Model Card Toolkit for Easier Model Transparency Reporting
- Algorithmic discrimination in Europe - Publications Office of the EU
- Risk in AI & Algorithmic Auditing - YouTube
- Quantitative_Privacy_Risk_Analysis
- FAIR Institute
- Privacy Impact Assessment - Canada
- NIST Risks Assessment Tools
- ISO 27557 Privacy Risk Management
- A (more) visual guide to the proposed EU Artificial Intelligence Act | by Nikita Lukianets | Apr, 2021 | Medium
- AI FactSheets 360
- Types of harm - Azure Application Architecture Guide | Microsoft Docs
- GitHub - Trusted-AI/adversarial-robustness-toolbox: Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
- 50 Years of Test (Un)fairness: Lessons for Machine Learning
- Enabling access, erasure, and rectification rights in AI systems | ICO
- Berryville Institute of Machine Learning
- Experts Doubt Ethical AI Design Will Be Broadly Adopted as the Norm Within the Next Decade | Pew Research Center
- 10 steps to educate your company on AI fairness | World Economic Forum
- Voortgang AI en algoritmen | Tweede Kamer der Staten-Generaal
- Machine learning compliance considerations
- Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers | Radiology: Artificial Intelligence
- Reproducible Deep Learning - Simone Scardapane
- Incorporate Ethics by Design Concepts Unit | Salesforce Trailhead
- Challenges and limits of an open source approach to Artificial Intelligence
- The Right to Process Data for Machine Learning Purposes in the EU
- GitHub - tensorflow/model-card-toolkit: a tool that leverages rich metadata and lineage information in MLMD to build a model card
- Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials
- Artificial intelligence: the opinion of the CNIL and its counterparts on the future European regulation | CNIL
- “A Proposal for Identifying and Managing Bias in Artificial Intelligence”. A draft from the NIST | Montreal AI Ethics Institute
- How to Force Our Machines to Play Fair | Quanta Magazine
- Blog: New toolkit launched to help organisations using AI to process personal data understand the associated risks and ways of complying with data protection law | ICO
- MFML Part 2 has arrived! - by Cassie Kozyrkov - Decision Intelligence
- Blog: Reflecting on the first year of the ‘Explaining decisions made with AI’ guidance | ICO
- Introducing Twitter’s first algorithmic bias bounty challenge
- News Release: DHS S&T Releases Artificial Intelligence & Machine Learning Strategic Plan | Homeland Security
- OpenAI's Codex model turns ordinary language into computer code - Axios
- Hogan Lovells responds to the European Commission’s consultation on AI
- Federal Register: Artificial Intelligence Risk Management Framework
- PCPD Publishes “Guidance on Ethical Development and Use of AI” Media Statement
- Framework of Meaningful Engagement
- The Launch Space - A roadmap to more sustainable AI systems - YouTube
- AI Ethics Living Dictionary | Montreal AI Ethics Institute
- Stasis in AI Ethics - YouTube
- Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion
- A Beginner’s Guide for AI Ethics | Montreal AI Ethics Institute
- Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
- AI Ethics Maturity Model | Montreal AI Ethics Institute
- Mapping value sensitive design onto AI for social good principles | Montreal AI Ethics Institute
- Six Essential Elements Of A Responsible AI Model
- Warning Signs: The Future of Privacy and Security in an Age of Machine Learning (Research summary) | Montreal AI Ethics Institute
- The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
- Concrete Problems in AI Safety
- What Do We Need to Build More Sustainable AI Systems? | GSF
- Ethics-based auditing of automated decision-making systems: intervention points and policy implications | Montreal AI Ethics Institute
- AI Ethics: Enter the Dragon! | Montreal AI Ethics Institute
- Examining the Black Box: Tools for Assessing Algorithmic Systems (Research Summary) | Montreal AI Ethics Institute
- NIST Taxonomy of AI risks
- Putting AI ethics to work: are the tools fit for purpose? | Montreal AI Ethics Institute
- UK government publishes pioneering standard for algorithmic transparency - GOV.UK
- Artificial Intelligence and the Privacy Paradox of Opportunity, Big Data and The Digital Universe | Montreal AI Ethics Institute
- Explaining the Principles to Practices Gap in AI | Montreal AI Ethics Institute
- UNESCO’s Recommendation on the Ethics of AI | Montreal AI Ethics Institute
- Leidraad kwaliteit AI in de zorg opgeleverd door en voor het veld | Nieuwsbericht | Data voor gezondheid
- Can a Model Be Differentially Private and Fair?
- Privacy and responsible AI
- Provisions on the Administration of Algorithm Recommendations for Internet Information Services
- RGPD compliance of processings that embed Artificial Intelligence: An introduction
- An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems - ScienceDirect
- Fairgen | Biased Data
- Enhancing Trust in AI Through Industry Self-Governance | Montreal AI Ethics Institute
- Advancing accountability in AI
- OECD AI Principles
- OECD, AI Language Models
- NIST AI Risk Framework
- IEEE Standards on Autonomous and Intelligent Systems
- Algoritmische Transparantie
- Unfairness By Algorithm: Distilling the Harms of Automated Decision-Making - Future of Privacy Forum
- Governance Guidelines for Implementation of AI Principles
- AI: Decoded: China’s deepfake law — Synthetic data — Selling sensitive data for profit – POLITICO
- Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
- The winter, the summer and the summer dream of artificial intelligence in law
- Representation and Imagination for Preventing AI Harms | Montreal AI Ethics Institute
- Google AI Blog: Federated Learning with Formal Differential Privacy Guarantees
- Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? | Montreal AI Ethics Institute
- Explanability:Robustness and Usefulness in AI Explanation Methods | Montreal AI Ethics Institute
- The AI Carbon Footprint and Responsibilities of AI Scientists | Montreal AI Ethics Institute
- AI Risk Management Framework
- Ten-guidelines-for-product-leaders-to-implement-ai-responsibly
- One machine learning question every day - bnomial
- Children Rights Impact Assessment
- Adversarial-robustness-toolbox
- Github, adversarial-robustness-toolbox
- Threat Modeling AI/ML Systems and Dependencies
- Vulnerabilities of Connectionist AI Applications: Evaluation and Defense
- ENISA: Artificial Intelligence Cybersecurity Challenges
- NCSC AI Security
- WEF Artificial Intelligence for Children 2022
- We need redress by design for AI systems
- Privacy Preserving Machine Learning: Threats and Solutions
- Security and Privacy Issues in Deep Learning
- AI BLIND SPOT
- AI Liability Key Challenges
- AI Liability Considerations
- EU guidelines on ethics in artificial intelligence: Context and implementation
- Artificial Intelligence and Data Protection How the GDPR Regulates AI
- Does the Correspondence Bias Apply to Social Robots?: Dispositional and Situational Attributions of Human Versus Robot Behavior
- The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
- An EU Artificial Intelligence Act for Fundamental Rights: A Civil Society Statement
- Guiding Principles on Business and Human Rights
- Accountability Principles for Artificial Intelligence (AP4AI) in the Internal Security Domain
- Getting the future right: Artificial Intelligence and Fundamental Rights
- Operational Guidance on taking account of Fundamental Rights in Commission Impact Assessments
- Responsibility and AI
- AI for Healthcare Robotics
- Stride-ML Threat Model
- STRIDE-AI: An Approach to Identifying Vulnerabilities of Machine Learning Assets
- Securing Machine Learning Algorithms, ENISA
- Data Minimization for GDPR Compliance in Machine Learning Models
- Does Dimensionality curse effect some models more than others?
- Towards Breaking the Curse of Dimensionality for High-DimensionalPrivacy
- Differential Privacy Blog Series
- Hunting for Discriminatory Proxies in Linear Regression Models
- What-if Tool: Playing with AI Fairness
- ICO: Lawful basis for processing
- Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
- EDPS Guidelines on assessing the proportionality of measures that limit the fundamental rights to privacy and to the protection of personal data
- Charter of Fundamental Rights of the European Union
- AI Fairness - Explanation of Disparate Impact Remover
- Mitigating Bias in AI/ML Models with Disparate Impact Analysis
- Certifying and removing disparate impact
- Avoiding Disparate Impact with Counterfactual Distributions
- Random Oversampling and Undersampling for Imbalanced Classification
- Requisitos para Auditorías de Tratamientos que incluyan IA
- Oversampling and Undersampling
- Explainable Artificial Intelligence (XAI)
- LIME
- Z-Inspection
- Why Should I Trust You? Explaining the Predictions of Any Classifier
- SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
- IBM: Explainable AI
- Text Mining in Survey Data
- Automation Bias
- The Flaws of Policies Requiring Human Oversight of Government Algorithms
- Adecuación al RGPD de tratamientos que incorporan Inteligencia Artificial. Una introducción
- The False Comfort of Human Oversight as an Antidote to AI Harm
- A Proposal of Accessibility Guidelines for Human-Robot Interaction
- ISO/IEC 40500:2012 Information technology — W3C Web Content Accessibility Guidelines (WCAG) 2.0
- ISO/IEC GUIDE 71:2001 Guidelines for standards developers to address the needs of older persons and persons with disabilities
- ISO 9241-171:2008(en) Ergonomics of human-system interaction
- Mandate 376 Standards EU
- UNSDGs United Nations Sustainable Development goals
- Ethics guidelines for trustworthy AI
- Evolution in Age-Verification Applications
- Generalization in quantitative and qualitative research: Myths and strategies
- Generalizing statistical results to the entire population
- A Proposal for Identifying and Managing Bias in Artificial Intelligence
- Hidden dangers of ChatGPT
- The role of reciprocity in human-robot social influence
- Reciprocity in Human-Robot Interaction
- Social robots and the risks to reciprocity
- Value alignment
- AI Values and Alignment
- Online Ethics Canvas
- Synthetic Data – Anonymisation Groundhog Day
- Datasheets for Datasets
- OWASP API Security Project