NCI Courses

Data management principles and challenges; Data lifecycle; Data quality; Data provenance; Data integrity.

Data policies, standards, guidelines, and procedures; Business metrics and KPIs; Roles and responsibilities; DG maturity levels; Data governance frameworks, operating models, and tools.

How to implement a data governance program (e.g., Ladley’s methodology); Fundamentals of Research Data Management (e.g., research data lifecycle, data sharing, research data management planning).

Sustainability Terminologies and Meanings; UN Sustainable Development Goals (SDGs); Environmental, social, and corporate governance (ESG); Sustainable IT.

Brief history of human rights; Types of EU legislation; The Right to Privacy; Key legislative frameworks;

Key provisions in the GDPR (e.g., data protection principles, privacy by design; data subjects rights, data processor and data controller, international data transfers, informed consent, data protection impact assessment).

Data Privacy and Anonymization; Privacy and regulatory compliance issues pertaining to specific sectors (e.g., fintech, cloud computing); Surveillance; EU digital agenda (e.g., AI regulation).

Nature and sources of ethics (e.g., personal, professional, social, business); Branches of normative ethics (deontology, utilitarianism, virtue theory, social justice, etc.); Frameworks for ethical design and decision making (e.g., Ethical Impact Assessment).

Ethical perspective of data governance (how DG supports ethics, principles, and modes of governance with ethics considerations); Ethics in Research: considerations Before, During, and After; Codes of ethics and professional conduct (e.g., ACM)

Ethic concerns in various technologies and sustainable socio-technical systems (e.g., IoT, machine learning); IT Ethics in specific sectors (e.g., spam, anonymity, cyberbullying, copyright, etc.)

Principles of AI Governance; the meaning of fairness, accountability, and transparency with respect to algorithmic systems; Unconscious Bias and techniques to address it; Perceptions of algorithmic bias and unfairness; Interventions to mitigate biases in systems; Methods and tools for enhancing fairness in algorithms (e.g., IEEE P7003 TM).

Principles and Strategies for designing accountable algorithms and systems; Trade-offs between privacy and transparency; Tools and methodologies for conducting algorithm audits (e.g., Algorithmic Impact Assessments).