Module Code: |
H9DGC |
Long Title
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Data Governance, Ethics, and Sustainability
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Title
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Data Governance, Ethics, and Sustainability
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
Vanessa Ayala-Rivera |
Module Author: |
Maurice Keady |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
Lecturer PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
Tutor PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Demonstrate critical understanding of the governance and regulatory frameworks associated with the key data lifecycle stages for an effective and ethical management of data assets. |
LO2 |
Demonstrate critical awareness and interpretation of the fundamental principles and regulatory regimes of data protection and data privacy in socio-technical environments. |
LO3 |
Critically analyse and evaluate the main ethical, legal, sustainability, and social implications of using data-driven technologies. |
LO4 |
Apply core concepts of sustainability, data governance, ethics, and data protection to address sustainability challenges in a global context and to support ethical and sustainable decision making. |
LO5 |
Appraise the interplay of fairness, accountability, and transparency in algorithmic decision-making systems and evaluate operational and technical solutions to address these concerns. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
Programme entry requirements must be satisfied.
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Module Content & Assessment
Indicative Content |
Data Management and Governance I
Data management principles and challenges; Data lifecycle; Data quality; Data provenance; Data integrity.
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Data Management and Governance II
Data policies, standards, guidelines, and procedures; Business metrics and KPIs; Roles and responsibilities; DG maturity levels; Data governance frameworks, operating models, and tools.
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Data Management and Governance III
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).
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Governance for Sustainable Development
Sustainability Terminologies and Meanings; UN Sustainable Development Goals (SDGs); Environmental, social, and corporate governance (ESG); Sustainable IT.
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Regulatory Compliance I
Brief history of human rights; Types of EU legislation; The Right to Privacy; Key legislative frameworks;
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Regulatory Compliance II
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).
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Regulatory Compliance III
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).
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Ethical Issues Pertaining to Data I
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).
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Ethical Issues Pertaining to Data II
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)
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Ethical Issues Pertaining to Data III
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.)
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Model Governance and Explainable AI - Part 1
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).
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Model Governance and Explainable AI - Part 2
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).
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Assessment Breakdown | % |
Coursework | 40.00% |
End of Module Assessment | 60.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Continuous Assessment |
% of total: |
40 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
No |
Assessment Description: This assessment will assess learners’ insights and evaluation of data management and governance, legal, and sustainability issues relating to situational contexts and scenarios. Students will work in groups. |
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Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5 |
Non-Marked: |
Yes |
Assessment Description: Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning. |
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End of Module Assessment |
Assessment Type: |
Terminal Exam |
% of total: |
60 |
Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5 |
Non-Marked: |
No |
Assessment Description: The examination will be of two hours duration and may include a mix of: theoretical, applied and interpretation questions. |
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Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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Reassessment Description The reassessment strategy for this module will consist of a terminal examination that will assess all learning outcomes.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
Classroom and demonstrations |
24 |
Per Semester |
2.00 |
Tutorial |
Mentoring and small-group tutoring |
12 |
Per Semester |
1.00 |
Independent Learning Time |
Independent learning |
89 |
Per Semester |
7.42 |
Total Weekly Contact Hours |
3.00 |
Workload: Blended |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Independent Learning Time |
Independent learning |
89 |
Per Semester |
7.42 |
Directed Learning |
Directed e-learning |
12 |
Per Semester |
1.00 |
Tutorial |
Mentoring and small-group tutoring |
12 |
Per Semester |
1.00 |
Lecture |
Classroom and demonstrations |
12 |
Per Semester |
1.00 |
Total Weekly Contact Hours |
3.00 |
Workload: Part Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
Classroom and demonstrations |
24 |
Per Semester |
2.00 |
Tutorial |
Mentoring and small-group tutoring |
12 |
Per Semester |
1.00 |
Independent Learning |
Independent learning |
89 |
Per Semester |
7.42 |
Total Weekly Contact Hours |
3.00 |
Module Resources
Recommended Book Resources |
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Dama International. (2017), DAMA-DMBOK: Data Management Body of Knowledge, 2nd Ed. Technics Publications, [ISBN: 978-1634622349].
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John Ladley. (2019), Data Governance: How to design, deploy, and sustain an effective data governance program, Academic Press, p.300, [ISBN: 9780128158319].
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Katherine O'Keefe,Daragh O Brien. (2018), Ethical Data and Information Management, Kogan Page, [ISBN: 978-0749482046].
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Sanjay Sharma. (2019), Data Privacy and GDPR Handbook, John Wiley & Sons, p.496, [ISBN: 978-1119594246].
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Barocas, S., Hardt, M. and Narayanan, A.,. (2019), Fairness and Machine Learning: Limitations and Opportunities,, fairmlbook.org..
| Supplementary Book Resources |
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Robert F. Smallwood. (2019), Information Governance: Concepts, Strategies and Best Practices, John Wiley & Sons, p.548, [ISBN: 978-1119491446].
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Michael Kearns,Aaron Roth. (2019), The Ethical Algorithm: The Science of Socially Aware Algorithm Design., Oxford University Press, USA, p.229, [ISBN: 978-0190948207].
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HERMAN T. TAVANI. ETHICS AND TECHNOLOGY, [ISBN: 978-1119355311].
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Reynolds, George W.. (2019), Ethics in Information Management, 6th Ed. Cengage Learning (Inc.),,, Boston, [ISBN: 978-337-40587-4].
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West, S.M., Whittaker, M. and Crawford, K.,. (2019),. Discriminating systems., AI Now..
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Jennifer L. Eberhardt, PhD. (2020), Biased, Biased: Uncovering the hidden prejudice that shapes what we see, think, and do., p.370, [ISBN: 978-0735224957].
| This module does not have any article/paper resources |
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This module does not have any other resources |
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