Overview
Understanding social, ethical, and legal issues arising from artificial intelligence and data science. Topics include bias, transparency, control, data sovereignty, employment and regulation. No knowledge of data science is assumed.
Artificial Intelligence is increasingly important in government, commerce, and daily life. This interdisciplinary course examines how we might best develop, deploy, regulate, and work alongside AI. It addresses issues such as bias, control, transparency, data sovereignty, liability, and the likely social and economic effects of AI. No expertise in computing or data science is required.
About this paper
| Paper title | When Machines Decide |
|---|---|
| Subject | Digital Humanities |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $955.05 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- One 200-level paper
- Schedule C
- Arts and Music, Commerce, Science
- Eligibility
No expertise in computing or data science is required.
- Contact
- Teaching staff
Course co-ordinator: Professor James Maclaurin
This course is taught by a selection of academics from programmes including: Computer Science, Data Science, Information Science, Law, Philosophy, Sociology, Social work and Statistics.- Teaching Arrangements
Teaching will consist of one lecture (50 minutes) and one seminar (1hr 50 minutes each) each week. The seminars will include a mix of lecture material and small group teaching. They will also be used for student presentations. Lectures and seminars will be common to students in 303 and 403.
- Textbooks
Zerilli, J., Danaher, J., Maclaurin, J., Gavaghan, C., Knott, A., Liddicoat, J., and Noorman, M. E. (2021) A Citizen's Guide to Artificial Intelligence, Cambridge Massachusetts, MIT Press. For more information see https://mitpress.mit.edu/books/citizens-guide-artificial-intelligence.
- Graduate Attributes Emphasised
Information literacy, communication, critical thinking, ethics, global perspective
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will:
- Understand and communicate key concepts in the development, use, and commercialisation of data science and artificial intelligence (Lifelong Learning; Critical Thinking)
- Understand common impacts and drivers of the use of data science and artificial intelligence
- Demonstrate familiarity with ethical, regulatory, and public policy debates relating to artificial intelligence and data science (Critical Thinking; Interdisciplinary Perspective)
- Debate and communicate the risks associated with data science and artificial intelligence in scientific, government and business contexts (Critical Thinking; Communication; Information Literacy; Teamwork)
- Work in team-based learning groups with the opportunity to engage in constructive discussions, collaborative workflow and completion of a group-based assessment (Lifelong Learning; Teamwork; Communication; Information Literacy)
- Understand key debates relating to the ownership, control and use of data
- Understand and apply selected software industry guidelines and tools for identifying and reducing societal risks in the development and deployment of AI systems
- Demonstrate cultural awareness and describe how the concepts of data sovereignty and equity apply to the use of artificial intelligence and indigenous data (cultural understanding)
Timetable
Overview
Understanding social, ethical, and legal issues arising from artificial intelligence and data science. Topics include bias, transparency, control, data sovereignty, employment and regulation. No knowledge of data science is assumed.
Artificial Intelligence is increasingly important in government, commerce, and daily life. This interdisciplinary course examines how we might best develop, deploy, regulate, and work alongside AI. It addresses issues such as bias, control, transparency, data sovereignty, liability, and the likely social and economic effects of AI. No expertise in computing or data science is required.
About this paper
| Paper title | When Machines Decide |
|---|---|
| Subject | Digital Humanities |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2024 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- One 200-level paper
- Schedule C
- Arts and Music, Commerce, Science
- Eligibility
No expertise in computing or data science is required.
- Contact
- Teaching staff
Course Co-ordinator: Professor Brendan McCane
This course is taught by a selection of academics from programmes including: Computer Science, Data Science, Information Science, Law, Philosophy, Sociology, Social work and Statistics.
- Teaching Arrangements
Teaching will consist of one lecture (50 minutes) and one seminar (1hr 50 minutes each) each week. The seminars will include a mix of lecture material and small group teaching. They will also be used for student presentations. Lectures and seminars will be common to students in DHUM 303 and DHUM 403.
- Textbooks
Zerilli, J., Danaher, J., Maclaurin, J., Gavaghan, C., Knott, A., Liddicoat, J., and Noorman, M. E. (2021) A Citizen's Guide to Artificial Intelligence, Cambridge Massachusetts, MIT Press. For more information see https://mitpress.mit.edu/books/citizens-guide-artificial-intelligence.
- Graduate Attributes Emphasised
Information literacy, communication, critical thinking, ethics, global perspective
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will:
- Understand and communicate key concepts in the development, use, and commercialisation of data science and artificial intelligence (Lifelong Learning; Critical Thinking)
- Understand common impacts and drivers of the use of data science and artificial intelligence
- Demonstrate familiarity with ethical, regulatory, and public policy debates relating to artificial intelligence and data science (Critical Thinking; Interdisciplinary Perspective)
- Debate and communicate the risks associated with data science and artificial intelligence in scientific, government and business contexts (Critical Thinking; Communication; Information Literacy; Teamwork)
- Work in team-based learning groups with the opportunity to engage in constructive discussions, collaborative workflow and completion of a group-based assessment (Lifelong Learning; Teamwork; Communication; Information Literacy)
- Understand key debates relating to the ownership, control and use of data
- Understand and apply selected software industry guidelines and tools for identifying and reducing societal risks in the development and deployment of AI systems
- Demonstrate cultural awareness and describe how the concepts of data sovereignty and equity apply to the use of artificial intelligence and indigenous data (cultural understanding)