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DHUM303 When Machines Decide

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.

Paper title When Machines Decide
Paper code DHUM303
Subject Digital Humanities
EFTS 0.1500
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $929.55
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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Prerequisite
One 200-level paper
Schedule C
Commerce, Science
Eligibility

No expertise in computing or data science is required.

Contact

james.maclaurin@otago.ac.nz

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)

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Timetable

Semester 2

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
A1 Monday 15:00-16:50 28-34, 36-38, 40-41
Thursday 09:00-09:50 28-34, 36-41

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.

Paper title When Machines Decide
Paper code DHUM303
Subject Digital Humanities
EFTS 0.1500
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees Tuition Fees for 2023 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
One 200-level paper
Schedule C
Commerce, Science
Eligibility

No expertise in computing or data science is required.

Contact

james.maclaurin@otago.ac.nz

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)

^ Top of page

Timetable

Semester 2

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
A1 Monday 15:00-16:50 28-34, 36-41
Thursday 09:00-09:50 28-34, 36-41