Ethical Implication of AI: Assessing Trustworthy AI in Practice: Lecture Notes (Open Access)
Target Students
Master students and PhD students from interdisciplinary background. (e.g. Computer Science, Data Science, Machine Learning, Law, Medicine, Social Science, Ethics, Public Policy, etc.).
Course Description
Applications based on Machine Learning and/or Deep Learning carry specific (mostly unintentional) risks that are considered within AI ethics. As a consequence, the quest for trustworthy AI has become a central issue for governance and technology impact assessment efforts, and has increased in the last four years, with focus on identifying both ethical and legal principles.
As AI capabilities have grown exponentially, it has become increasingly difficult to determine whether their model outputs or system behaviors protect the rights and interests of an ever-wider group of stakeholders –let alone evaluate them as ethical or legal, or meeting goals of improving human welfare and freedom.
For example, what if decisions made using an AI-driven algorithm benefit some socially salient groups more than others?
And what if we fail to identify and prevent these inequalities because we cannot explain how decisions were derived?
Moreover, we also need to consider how the adoption of these new algorithms and the lack of knowledge and control over their inner workings may impact those in charge of making decisions.
This course will help students to assess Trustworthy AI systems in practice by using the Z-Inspection® process.
The Z-Inspection® process is the result of 5 years of applied research of the Z-Inspection® initiative, a network of high class world experts lead by Prof. Roberto V. Zicari.
Z-Inspection® is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI.
Students will work in small groups and will evaluate the trustworthiness of real AI systems in various domains, e.g. healthcare, government and public administrations, justice.
Syllabus
1. Fundamental rights as moral and legal entitlements
2. From fundamental rights to ethical principles
3. Introduction to the EU guidelines for Trustworthy AI
3.1. Trustworthy AI Ethical principles:
– Respect for human autonomy
– Prevention of harm
– Fairness
– Explicability
3.2 Ethical Tensions and Trade offs
3.3. Seven Requirements (+ sub-requirements) for Trustworthy AI
– Human agency and oversight.Including fundamental rights, human agency and human oversight
– Technical robustness and safety.Including resilience to attack and security, fall back plan and general safety
– Privacy and data governance. Including respect for privacy, quality and integrity of data, and access to data
– Transparency. Including traceability, explainability and communication
– Diversity, non-discrimination and fairness. Including the avoidance of unfair bias, accessibility and universal design, and stakeholder participation
– Societal and environmental wellbeing. Including sustainability and environmental friendliness, social impact, society and democracy
– Accountability. Including auditability, minimisation and reporting of negative impact, trade-offs and redress.
4. Assessing Trustworthy AI in Practice.
4. 1 The Z-Inspection® process in detail
– Set Up Phase
– Assess Phase
– Resolution Phase
4.2 . Additional Frameworks
– The Claim, Arguments and Evidence Framework (CAE)
4.3 Tool
– The ALTAI Web tool