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ORCIDAAAI Spring Symposium 2019 - Interpretable AI for Well-being: Palo Alto, CA, USA
- Takashi Kido, Keiki Takadama:
Proceedings of the Symposium Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness co-located with Association for the Advancement of Artificial Intelligence 2019 Spring Symposium (AAAI-Spring Symposium 2019), Stanford, CA, March 25-27, 2019. CEUR Workshop Proceedings 2448, CEUR-WS.org 2019
Session 1: Overview
- Takashi Kido, Keiki Takadama:
The Challenges for Interpretable AI for Well-being. - Keiki Takadama:
What Makes It Difficult To Apply AI Into Well-being and Its Solution: An Example of Sleep Apnea Syndrome. AAAI Spring Symposium: Interpretable AI for Well-being 2019
Session 2: Explainable AI
- Amy Wenxuan Ding:
Treat Everyone Fairly: A Model of Unbiased and Explainable Algorithmic Decision Making. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Umang Bhatt, Brian Davis, José M. F. Moura:
Diagnostic Model Explanations: A Medical Narrative.
Session 3: Interpretable AI
- David Agogo, Leona Chandra Kruse:
Open Affect-Responsive Systems: Toward Personalized AI to Beat Back the Waves of Technostress. - Yuichi Yoda, Kosuke Mizokushi, Seiichiro Honjo:
A Study of Basis on AI-based Information Systems: The Case of Shogi AI System "Ponanza". - Changeun Yang, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada:
TGIF!: Selecting the most healing TNT by optical flow. - Saveli Goldberg, Boris Galitsky, Ben Weisburd:
Framework for interaction between expert users and Machine Learning Systems. - Akinori Abe:
Interpretable AI as Curation.
Session 4: Well-being AI
- Takeshi Konno, Hiroaki Kingetsu, Daisuke Fukuda, Toshihiro Sonoda:
Fall Risk Detection for the Elderly using Contactless Sensors. - Ryo Takano, Sho Kajihara, Satoshi Hasegawa, Eiki Kitajima, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto:
Toward Good Circadian Rhythm through an valuate of Stress Condition. - Iko Nakari, Yusuke Tajima, Akari Tobaru, Keiki Takadama:
WAKE Detection During Sleep using Random Forest for Apnea Syndrome Patients. - Akari Tobaru, Yusuke Tajima, Keiki Takadama:
Sleep Stage Estimation using Heart Rate Variability divided by Sleep Cycle with Relative Evaluation. - Nobuyuki Oishi, Masayuki Numao:
Measuring Functional Independence of an Aged Person with a Combination of Machine Learning and Logical Reasoning.
Session 5: Social Embeddedness
- Oliver Bendel:
Are Robot Tax, Basic Income or Basic Property Solutions to the Social Problems of Automation? - Teruaki Hayashi, Yukio Ohsawa:
Context-based Network Analysis of Structured Knowledge for Data Utilization. - Sadeq Rahimi:
Extended Mind, Embedded AI, and "the Barrier of Meaning". - Miwa Nishinaka, Yusuke Kishita, Hisashi Masuda, Kunio Shirahada:
Concept of Future Prototyping Methodology to Enhance Value Creation within Future Contexts. - Anas Al-Tirawi, Robert G. Reynolds:
Maintaining Knowledge Distribution System's Sustainability Using Common Value Auctions.
Session 6: Cognitive Bias
- Morteza Shahrezaye, Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich:
Estimating the Political Orientation of Twitter Users in Homophilic Networks. - Xuehui Leng, Masanao Ochi, Takeshi Sakaki, Junichiro Mori, Ichiro Sakata:
A Cross-lingual Analysis on Culinary Perceptions to Understand the Cross-cultural Difference. - Christina Alexandris:
Evaluating Cognitive Bias in Two-Party and Multi-Party Spoken Interactions.
Session 7: Invited Talks
- Judea Pearl:
What is Causal Inference? AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Sidharth Goel:
DeepVariant: Deep Learning for Genomic Variant Calling. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Pang Wei Koh:
Identifying and exploiting influential training examples. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Peter Pirolli:
Interpretable AI for Well-Being Using Mobile Health. - Avanti Shrikumar:
Suggested Best Practices for Interpreting Deep Learning Models via Input-Level Importance Scores. AAAI Spring Symposium: Interpretable AI for Well-being 2019
Session 8: Poster and Demonstration
- Madeleine Schneider, Robert Thomsons:
What Makes a Good Diagnosis: An Algorithm to Detect Biased Training Data. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Miwa Nishinaka:
Future Prototyping Methodology: What is a well-being for the Future Society and how AI handles "appropriateness. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Ryo Takano, Akari Tobaru, Iko Nakari, Keiki Takadama:
Sleep Stage Estimation Through Mattress Sensor. AAAI Spring Symposium: Interpretable AI for Well-being 2019
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