Overview
Analysis of 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
| Paper title | When Machines Decide |
|---|---|
| Subject | Digital Humanities |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,393.95 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- 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
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:
- Have an enhanced understanding and communication of the development, use, and commercialisation of data science and artificial intelligence (Lifelong learning, Critical thinking).
- Have an enhanced understanding of common impacts and drivers of the use of data science and artificial intelligence.
- Analyse ethical, regulatory, and public policy debates in light of practical considerations for those provisioning and developing data science and artificial intelligence (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).
- Develop detailed analysis of a practical application of data science and artificial intelligence, including design considerations, risk factors and social impact (Critical thinking, Interdisciplinary perspective, Information literacy).
- Understand key debates relating to the ownership, control and use of data, including an ability to critically analyse the differential impacts of AI for different populations.
- Have an enhanced understanding of the application of 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
Analysis of 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
| Paper title | When Machines Decide |
|---|---|
| Subject | Digital Humanities |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Eligibility
No expertise in computing or data science is required.
- Contact
- 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:
- Have an enhanced understanding and communication of the development, use, and commercialisation of data science and artificial intelligence (Lifelong learning, Critical thinking).
- Have an enhanced understanding of common impacts and drivers of the use of data science and artificial intelligence.
- Analyse ethical, regulatory and public policy debates in light of practical considerations for those provisioning and developing data science and artificial intelligence (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).
- Develop detailed analysis of a practical application of data science and artificial intelligence, including design considerations, risk factors and social impact (Critical thinking, Interdisciplinary perspective, Information literacy).
- Understand key debates relating to the ownership, control and use of data, including an ability to critically analyse the differential impacts of AI for different populations.
- Have an enhanced understanding of the application of 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).