DHUM303 When Machines Decide

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. 

About this paper

Prerequisite

One 200-level paper

Schedule C

Arts and Music, Commerce, Science

Eligibility

No expertise in computing or data science is required.

Contact

Professor Brendan McCane

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

Each week there are two sessions each of 1 hour and 50 minutes. Each session may be a mix of lecture, workshop or student presentations. Sessions are a joint class of both DHUM303 and DHUM403.

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)

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.

About this paper

Prerequisite

One 200-level paper

Schedule C

Arts and Music, Commerce, Science

Eligibility

No expertise in computing or data science is required.

Contact

Dr Andrew Richmond

Teaching staff

Course Co-ordinator: Dr Andrew Richmond

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

Each week, there are two sessions, each of 1 hour and 50 minutes. Each session may be a mix of lecture, workshop or student presentations. Sessions are a joint class of both 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 A Citizen's Guide to Artificial Intelligence.

Graduate Attributes Emphasised

Communication, Critical thinking, Ethics.
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)