search
search
reviewers
reviewers
feeds
feeds
assignments
assignments

settings
logout

AAAI Spring Symposium: Interpretable AI for Well-being

Keyphrases

Publications

2019

Changeun Yang, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada
TGIF!: Selecting the most healing TNT by optical flow. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
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. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
David Agogo, Leona Chandra Kruse
Open Affect-Responsive Systems: Toward Personalized AI to Beat Back the Waves of Technostress. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Takeshi Konno, Hiroaki Kingetsu, Daisuke Fukuda, Toshihiro Sonoda
Fall Risk Detection for the Elderly using Contactless Sensors. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Iko Nakari, Yusuke Tajima, Akari Tobaru, Keiki Takadama
WAKE Detection During Sleep using Random Forest for Apnea Syndrome Patients. 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)
Christina Alexandris
Evaluating Cognitive Bias in Two-Party and Multi-Party Spoken Interactions. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Sadeq Rahimi
Extended Mind, Embedded AI, and "the Barrier of Meaning". AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Akari Tobaru, Yusuke Tajima, Keiki Takadama
Sleep Stage Estimation using Heart Rate Variability divided by Sleep Cycle with Relative Evaluation. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
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)
Takashi Kido, Keiki Takadama
The Challenges for Interpretable AI for Well-being. 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)
Umang Bhatt, Brian Davis, José M. F. Moura
Diagnostic Model Explanations: A Medical Narrative. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Yuichi Yoda, Kosuke Mizokushi, Seiichiro Honjo
A Study of Basis on AI-based Information Systems: The Case of Shogi AI System "Ponanza". AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Teruaki Hayashi, Yukio Ohsawa
Context-based Network Analysis of Structured Knowledge for Data Utilization. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
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)
Judea Pearl
What is Causal Inference? AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Akinori Abe
Interpretable AI as Curation. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Peter Pirolli
Interpretable AI for Well-Being Using Mobile Health. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Nobuyuki Oishi, Masayuki Numao
Measuring Functional Independence of an Aged Person with a Combination of Machine Learning and Logical Reasoning. 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)
Pang Wei Koh
Identifying and exploiting influential training examples. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Xuehui Leng, Masanao Ochi, Takeshi Sakaki, Junichiro Mori, Ichiro Sakata
A Cross-lingual Analysis on Culinary Perceptions to Understand the Cross-cultural Difference. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Avanti Shrikumar
Suggested Best Practices for Interpreting Deep Learning Models via Input-Level Importance Scores. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Anas Al-Tirawi, Robert G. Reynolds
Maintaining Knowledge Distribution System's Sustainability Using Common Value Auctions. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Saveli Goldberg, Boris Galitsky, Ben Weisburd
Framework for interaction between expert users and Machine Learning Systems. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Miwa Nishinaka, Yusuke Kishita, Hisashi Masuda, Kunio Shirahada
Concept of Future Prototyping Methodology to Enhance Value Creation within Future Contexts. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Amy Wenxuan Ding
Treat Everyone Fairly: A Model of Unbiased and Explainable Algorithmic Decision Making. AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Oliver Bendel
Are Robot Tax, Basic Income or Basic Property Solutions to the Social Problems of Automation? AAAI Spring Symposium: Interpretable AI for Well-being (2019)
Morteza Shahrezaye, Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich
Estimating the Political Orientation of Twitter Users in Homophilic Networks. AAAI Spring Symposium: Interpretable AI for Well-being (2019)

volume 2448, 2019

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. AAAI Spring Symposium: Interpretable AI for Well-being 2448 (2019)