HAI Postdoctoral Fellowship with Professor Sanmi Koyejo and Professor Michael Bernstein, Department of Computer Science
Deployed AIs must navigate challenging tradeoffs. How much should a medical AI risk a false positive vs. a false negative? How much harm is allowable vs. the benefit provided by the AI? The research will develop algorithms, interfaces, and applications of metric elicitation from human feedback.
Metric elicitation is a framework designed to aid humans in identifying and balancing trade offs relevant to decision-making with AIs in settings where humans are the best judge of preferred outcomes, i.e., where a gold standard does not exist, and ideal outcomes depend on stakeholder preferences. Once identified, these preferences can be used for auditing decision systems, training automated decision-making models, and explaining decision-making processes. However, its success relies on developing effective interactive techniques for eliciting preferences and tradeoffs from people.
The postdoctoral scholar will work at the intersection of artificial intelligence and human-computer interaction to investigate some of the following questions: What are efficient algorithms to reduce the number (and increase the quality) of human queries?
- How do biases play into the elicitation procedure and outcomes?
- What new benchmarks and datasets might help measure progress in joint human-AI decision-making?
- What elicitation interfaces best capture peoples' intent in navigating these challenging tradeoffs?
The postdoctoral fellowship will be co-hosted by Professor Sanmi Koyejo (machine learning) and Professor Michael Bernstein (human-computer interaction). The PIs will support the postdoc fellow in the development of all the components of their IDP, career counseling, grant proposals, development of teaching and mentoring skills, and instruction in professional practices.
The success of the mentoring plan will be assessed by monitoring the postdoctoral researchers' personal progress by tracking their progress toward their career goals. An interview will be conducted towards the end of the postdoctoral program year to evaluate how well the program helped the researcher to achieve their career goals.
Stanford HAI is also committed to creating a diverse community of scholars who are engaged in contributing to the understanding and advancement of Human-Centered AI. Postdoctoral fellows will have the opportunity to engage with one another and with the broader Stanford HAI research community. They are also expected to participate in professional development, cohort-building, and other programmatic activities organized by HAI.
- This is a multidisciplinary project, and we welcome candidates from either field with demonstrated multidisciplinary interests.
- The ideal candidate will have either a background (Ph.D.) in artificial intelligence, mechanism design, or related fields with an interest or expertise in human-computer interaction or a background in human-computer interaction with an interest or expertise in artificial intelligence.
- Completion of all doctoral requirements within the last three years and no later than September 1, 2023
- Expertise or background in AI, algorithms, mechanism design, or related areas, as evidenced by degree, publications, research statement, or recommendation letters.
- Expertise or background in human-computer interaction, as evidenced by degree, publications, research statement, or recommendation letters.
- Application Deadline: March 20, 2023 (Applicants advancing in the review process may be asked to submit additional materials, including letters of recommendation, and may be invited to interview.)
- Selections to be made by mid-April, 2023
- This is a 1-year appointment starting Fall 2023
For full consideration, send a complete application in a single PDF to HAI-Fellowships@stanford.edu with the subject line: “HAI Postdoc Fellowship - Koyejo/Bernstein.”
Complete applications will include:
The expected base pay for this position is $80,000/yr. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.