Themes;Description;Source Fairness;"Using training data and models that are free of bias in order to avoid unfair treatment of certain groups";"Pillars of AI proposed by IBM – (Mojsilovic, 2018)" Robustness;"AI systems should be safe and secure and not vulnerable to tampering or comprising that data that is being trained on." Explainability;"Providing decision and suggestions that can be understood by their users or developers" Lineage;"Including the details of the development, deployment, and maintenance in order to be audited throughout the life cycle." Fairness;"Making sure that AI systems are treating all people fairly.";"Microsoft principles on AI – (“Microsoft AI principles,” 2019)" "Reliability & Safety";"Making sure that AI systems are performing reliably and safely." "Privacy & Security";"Making sure that AI systems are secured & are respecting privacy." Transparency;"Making sure that AI systems are understandable." Fairness;"1.Using representative datasets to train and test the model.";"Responsible AI practices by Google – (“Responsible AI Practices,” 2019)" "2.Checking the system for unfair biases." "3.Analyzing the performance of the system." Interpretability;"1.Designing a model that is interpretable." "2.Understanding the trained model." Privacy;"1.Collecting and handling data responsibly" "2.Safeguarding the privacy of AI/ML models." Security;"1.Identifying the potential threats to the system" Fairness;"Whether the bias in the data and AI model is minimized and Are the bias been addressed when AI is being used?";"Dimensions of Responsible AI by PwC – (Building Trust in AI and Data Analytics, 2018)" Interpretability;"Can we explain how an AI model is making decisions and ensuring that the decisions made by AI models are accurate?" "Robustness & Security";"Can we rely on the performance of the AI system and whether AI systems are vulnerable to attack?" Governance;"Who is accountable for the AI system and ensuring whether proper controls are in place?" "Technical robustness and Safety";"Including resilience to attack and security, fall back plan and general safety, accuracy, reliability, and reproducibility";"Some of the key requirements for building a trustworthy AI – (EUROPEAN COMMISSION, 2019)" "Privacy and data governance";"Including respect for privacy, quality, and integrity of data, and access to data." Transparency;"Including traceability, explainability, and communication" "Bias evaluation";"Understanding, monitoring, and documenting the biases in development and production";"Some of the key machine learning principles by Institute of AI & Machine learning – (“The Institute for Ethical AI & Machine Learning,” n.d.)" Explainability;"Developing tools and process to improve transparency and explainability" "Trust by Privacy";"Protect and handle data with stakeholders that interact with the system directly or indirectly" "Security risks";"Develop and improve process to ensure data and model security are taken into consideration."