Ethical Considerations with AI
The Brandeis Center for Teaching and Learning has done a great job working on Ethics frameworks for the use of AI over the past many months. Below you will find links to the work the group has done, along with other resources to learn more about things such as spotting algorithmic bias and learning how to mitigate the risk of obtaining bad data.
Brandeis Resources
- Ethical and Privacy Concerns - Overview by the Brandeis Center for Teaching and Learning
This website discusses information on equity, ethical, privacy and accessibility concerns in using AI at our institution. - Brandeis University Ethics Framework for Students - Ethics AI Framework
This website provides a visual set of general guidelines for using generative AI in coursework, assignments and university research. These guidelines can be applied to faculty and staff as well as students.
Additional Resources
The following articles were reviewed and hand-picked by faculty members of the AI Task Force to help our community better understand potential shortcomings of AI platforms.
- Understand Algorithmic Biases -When AI Gets it Wrong
Generative AI has the potential to transform higher education—but it’s not without its pitfalls. These technology tools can generate content that’s skewed or misleading (Generative AI Working Group, n.d.). They’ve been shown to produce images and text that perpetuate biases related to gender, race (Nicoletti & Bass, 2023), political affiliation (Heikkilä, 2023), and more. As generative AI becomes further ingrained into higher education, it’s important to be intentional about how we navigate its complexities.
- Algorithmic bias continues to impact minoritized students - Diverse: Issues In Higher Education
(2024, July 15)
- Helping students understand the biases in generative AI - Center for Teaching Excellence.
(2024) The University of Kansas.
- Algorithmic bias and fairness: A critical challenge for AI - Just Think AI
(2024, May 21)
- CCA’s resources for generative AI and student success - Complete College America (2023, November 16)
- Algorithmic bias playbook: A guide for C-suite leaders, technical teams, policymakers, and regulators - Center for Applied Artificial Intelligence
(2024). The University of Chicago Booth School of Business.
- Bias in AI algorithms and recommendations for mitigation - PLOS Digital Health
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., ... & Mathur, P. (2023)
- Mitigating algorithmic bias: Strategies for addressing discrimination in data - SciTech Lawyer, 20(4). American Bar Association
Gipson Rankin, S. M. (2024)
- What are the risks of algorithmic bias in higher education? - Every Learner Everywhere (2021, May)