PODS Program Overview
PODS is a field in our one-year MA program that provides students with a unique opportunity to engage in interdisciplinary research on issues arising from new developments in Data Science, Artificial Intelligence, and Machine Learning (DS/AI/ML).
PODS students will develop original and creative solutions to problems arising from the development and advancement of new technologies and do so using a comprehensive set of interdisciplinary skills. The PODS field will also nurture student abilities in effective communication through teaching and through interaction with students and faculty in other disciplines.
PODS draws on resources outside Philosophy and beyond the Humanities to offer students the competencies in Statistics and Programming, which are required to produce effective research in the applications of emerging technologies:
- Students without basic competence in programming will take CIS*1500 as an additional course in their first semester. This is an open course that the School of Computer Science readily welcomes non-computer scientists into, and will serve as a great resource to PODS students.
- Students will take a graduate level statistics course, STAT*6950 (which involves facility with the programming language R) in their first semester.
- To complete the Major Research Project (MRP), PODS students will have the option to complete an Ethics Audit (this could take them to the program requirements page) in collaboration with students from the Collaborative Specialization in Artificial Intelligence (CSAI).
- PODS students will take two elective graduate courses, in Philosophy or in another discipline.
PODS aims to contribute to the many pressing problems that will arise as a result of society’s increasing reliance on data-driven technologies across nearly all sectors of the economy. Graduates of this program will therefore be well-positioned to pursue both research-oriented careers in academia and applied projects in industry.
PODS students will:
- Build a working knowledge of applications of Data Science, Artificial Intelligence, and Machine Learning across disciplinary boundaries.
- Identify and critically evaluate foundational assumptions, presuppositions, arguments, and cultural biases that might inform applications of DS/AI/ML.
- Pinpoint central normative issues in applications of DS/AI/ML and formulate original contributions to ongoing debates about their use.
- Communicate and defend an empirically informed philosophical position clearly and persuasively in both written and oral contexts, to both specialized and nonspecialized audiences.
- Identify the normative and technical considerations central to understanding applications of DS/AI/ML and their social impacts.
- Develop leadership skills in themselves and others through teaching, facilitating, and networking.
- Collaborate ethically and professionally with diverse disciplinary groups and community partners, with integrity and respect to the needs and interests of others.
- Be positioned to bring their skills and knowledge into industry contexts.
For more information about the program requirements, click here