# Discrimination, Bias, Fairness, and Trustworthy AI
## 6 Analysis of the Variable Trustworthiness  
Several studies [4; 6; 7; 50; 51] agree that it requires human agency, oversight, and the use of a set of overlapping properties to define trustworthiness in the context of AI systems development and consumption. Among the most frequent highlighted properties across the studied bibliography, the following can be found:  
1. Reliability is when the system does the right thing it was designed to and is available when it needs to be accessed. 2. Reproducibility is when the systems produce the same results in similar contexts. 3. Safety is when the system induces no harm to people as a result of their outcomes. 4. Security is when the systems are invulnerable or resilient to attacks. 5. Privacy is when the system protects a person's identity and the integrity of data, indicates access permission and methods, data retention periods, and how data will be destroyed at the end of such period, which ensures a person's right to be forgotten. 6. Accuracy is when the system performs as expected despite new unseen data compared to data on which it was trained and tested. Robustness is when the system is sensitive to the outcome and to a change in the input. * Fairness is when the system's outcomes are unbiased. * Accountability is when there are well-defined responsibilities for the system's outcome such as the methods for auditing such outcomes. * Transparency is when it is clear to an external observer how the system's outcome was produced, and the decisions/predictions/classifications are traceable to the properties involved. * Explainability is when the decisions/predictions/classifications produced by the system can be justified with an explanation that is easy to be understood by humans while being also meaningful to the end-user. * Other variables such as data governance, diversity, societal and environmental well-being/friendliness, sustainability, social impact, and democracy. Altogether, as supported by [PERSON] et al. [5], it can help build a trustworthy methodology to ensure users are able to verify the claims made about the level of privacy protection guaranteed by AI systems, regulators are able to trace the steps leading to a decision/prediction/classification and evaluate them against the context described by the modeled business, academics are able to research the impacts associated with large-scale AI systems, and developers are able to verify best practices are set for each of the AI development stage within the project lifecycle. In order to achieve Trustworthy AI, the Independent High-Level Expert Group on AI [41] recommends enabling inclusion and diversity throughout the entire AI system's development project's life cycle involving all affected stakeholders throughout the process. Along with [PERSON] [50], both studies describe three components trustworthy AI should comply with throughout the system's entire life cycle: it should be lawful, complying with all applicable laws and regulations; it should be ethical, ensuring adherence to ethical principles and values; and it should be robust, both from a technical and social perspective, since, even with good intentions, AI systems can cause unintentional harm. Similarly, [PERSON] [4] proposes three other main components trustworthy AI systems should consist of the following:  
* Ethics of algorithms (respect for human autonomy, prevention of harm, fairness, explicability);
* Ethics of data (human-centered, individual data control, transparency, accountability, equality), and;
* Ethics of Practice (responsibility, liability, codes, regulations). This actually represents an attempt to harness unintended discrimination produced by AIS, from the perspective of the policymaking and legal norms, specifically with a basis on the International Law of Human Rights. Given that engineering methods alone could not be sufficient enough to protect, according to [PERSON] et al. [31], the fundamental rights from unintended harms of AI systems. As seen above, the Principled AI International Framework presented by [PERSON] et al.