Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| AI, Data And Society | |||
| Reference | CM1122 | Version | 1 |
| Created | December 2023 | SCQF Level | SCQF 7 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To introduce students to the core concepts in artificial intelligence and data science, and to explore the legal, ethical, social and security issues inherent in the field. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Employ fundamental terminology involved in Artificial Intelligence and Data Science |
| 2 | Describe the aims and achievements of the fields of Artificial Intelligence and Data Science. |
| 3 | Outline current and emerging impacts of Artificial Intelligence and Data Science on society. |
| 4 | Recognise the main legal and security issues that affect a given Artificial Intelligence or Data Science application. |
| 5 | Identify ethical and professional issues in the deployment of Artificial Intelligence and Data Science systems in the real world. |
| Indicative Module Content |
|---|
| Philosophy of Artificial Intelligence, the Turing test, the Chinese room argument, self awareness, Artificial General Intelligence, Impacts of automation, distribution of benefits and wealth, computer vision and the surveillance society, deepfakes and visual/audio manipulation, cyberstalking, lethal autonomous weapons. Data protection laws - GDPR, data protection act, the right to be forgotten. Data security - encryption, anonymisation, retention policies. Ethical issues - algorithmic bias, explainability |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures and directed reading. In the practical sessions, students will explore, consider and discuss issues involved in AI and data Science, including through small group and whole class discussions. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 40 | N/A |
| Non-Contact Hours | 110 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5 |
| Description: | A report investigating and analysing the legal, ethical, security and societal impacts of a current Artificial Intelligence or Data Science application. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of Component 1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student must achieve an A in C1. |
| B | The student must achieve a B in C1. |
| C | The student must achieve a C in C1. |
| D | The student must achieve a D in C1. |
| E | The student must achieve an E in C1. |
| F | The student must achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | INTRODUCTION TO RESPONSIBLE AI a guide to responsible practices in ai. MANURE, AVINASH. BENGANI, SHALEEN. S, SARAVANAN. 2024 |
| 2 | AI ethics : a textbook. Boddington, Paula, 2023 |
| 3 | AI ethics and governance : black mirror and order. Liu, Zhiyi, author.; Zheng, Yejie. 2022 |
| 4 | Management of information security. Whitman, Michael E., 1964- author.; Mattord, Herbert J. 2017 |
| 5 | Ethics in computing : a concise module. Kizza, Joseph Migga. 2016 |