This year 24 students have been selected to participate in the conference. These students received a free registration and got a travel grant. Moreover, the accepted students
- will present a poster (during the student poster session, in the afternoon of Sunday Jan27th),
- will meet senior members of the community and representatives of the sponsoring companies,
- are invited at the student lunch on Jan 28th,
- will have the opportunity to publish a 2-page extended abstract of their work in the proceedings.
Student lunch – for students participating to the student program only
Jan 28th, 12:30PM-2:00PM
MW Restaurant (1538 Kapiolani Blvd, http://mwrestaurant.com).
This restaurant is about 15-20 minutes’ walk from the Hilton (see attached map.) Brent Venable will lead a group that walk over together, and will depart from the main AIES meeting space promptly as soon as the session ends, or you can travel there on your own.
For accepted students, mentors, program chairs, student program chairs.
Accepted Student Papers (Poster Presentation)
Ryan Blake Jackson. Generating Appropriate Responses to Inappropriate Robot Commands.
Maayan Shvo. Empathetic Planning and Plan Recognition.
Filip Michalsky. Fairness Criteria for Face Recognition Applications.
Himan Abdollahpouri. Popularity Bias in Ranking and Recommendation.
Amanda Coston. Risk Assessments and Fairness under Missingness.
Michelle Ausman. Artificial Intelligence’s Impact on Mental Health Treatments.
Daniel McNamara. Algorithmic Stereotypes: Implications for Fairness of Generalizing from Past Data.
Carla Zoe Cremer. An Inquiry into Reasons and Assumptions in AI forecasting .
Nripsuta Saxena. What does it mean to be fair?
Vasanth Sarathy. Learning Context-Sensitive Norms under Uncertainty.
Kacper Sokol. Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models.
Aaron Springer. Enabling Effective Transparency.
Elija Perrier. Quantum machine learning and its connections to philosophical and logical research.
De’Aira Bryant. Towards Emotional Intelligence in Social Robots designed for Children.
Duncan Mcelfresh. A Framework for Technically- and Morally-Sound AI.
Meir Friedenberg. Towards Formal Models of Blameworthiness
Sina Mohseni. Toward Design and Evaluation Framework for Interpretable Machine Learning Systems.
Alan Mishler. Modeling Risk and Achieving Algorithmic Fairness Using Potential Outcomes.
Fernando Delgado. Machine Learning in Legal Practice: Notes from Recent History.
Mckane Andrus. Critical Technical Practice for Human-Compatibility in AI.