How Fair is Fair?

Achieving Wellbeing AI

Thank you very much for your presentations and participation!!



[News]


The award list and pictures of participants are shown in the award section.

Description of the Symposium

Aims and New Challenges

What are the ultimate outcomes of artificial intelligence? AI has the incredible potential to improve the quality of human life, but it also presents unintended risks and harms to society. The goal of this symposium is (1) to combine perspectives from the humanities and social sciences with technical approaches to AI and (2) to explore new metrics of success for wellbeing AI, in contrast to "productive AI," which prioritizes economic incentives and values.

Given the rapid and widespread lifestyle changes provoked by the ongoing pandemic, it is important to investigate changing ideas of what a beneficial relationship between humans and AI could look like. COVID-19 may have altered the ways in which people interact with technology, leading to fewer psychological barriers and greater reliance on emerging technologies in day-to-day activities. For instance, more work-from-home and social-distancing arrangements means that more corporate meetings and ceremonies are taking place online. The pandemic has brought unexpected digital transformation, generating massive amounts of data that can increase the demand for AI. Paired with global collaboration, these technologies can achieve great feats, as shown in the recent efforts to develop COVID-19 vaccines.


We believe that humanistic perspectives are vital for a meaningful discussion of how AI can be used to help humans solve social problems. This is inspired by "AI for social good" movements, which pursue socially positive AI applications and support the UN's Sustainable Development Goals (SDGs), which promote equity, prosperity, and sustainability worldwide. In this symposium, we focus on the topics of wellbeing and fairness.

Wellbeing: We define wellbeing AI as artificial intelligence that promotes psychological wellbeing and maximizes human capabilities. For example, wellbeing AI could work to mitigate stress, a psychosomatic state that diminishes wellbeing and is exacerbated by lifestyle choices, by providing insights into how our digital experiences affect our emotional states. By increasing our access to targeted information, wellbeing AI could also help us to design more human-centered systems that could improve the quality of our lives.


Fairness: When used judiciously, AI can help humans to make fair decisions. However, AI systems can also be susceptible to biases, which prevent fairness. Furthermore, as data becomes more personal, AI systems can be used to manipulate people's cognitive biases towards certain ends, as we see with social media platforms and commercial recommendation systems. Here, AI influences people's opinions and sometimes leads to the problem of "echo-chambers," which can have disastrous political consequences. Thus, we should not overlook the risks of big data and machine learning.


Scope of Interests

This symposium aims to provide scholars with opportunities to share their latest research progress and to discuss challenges and potential applications related to AI, fairness, and wellbeing. We welcome both technical research on the possibilities and limitations of "machine intelligence" and philosophical discussions regarding wellbeing, fairness, and AI and humanity. This includes (but is not limited to) work on assistive robots, sound social media, ethical design and implementation, interpretable predictions in machine learning, fighting loneliness with AI/VR technologies, and other technologies or approaches that promote good health. We particularly welcome research that evaluates digital experiences or that furthers our understanding of human health and wellbeing.



As a point of departure, we define the following technical and ethical issues related to “AI and Humanity,” which effectively embody the scope of our interests (though other related topics are also acceptable):


(1) AI for “social good”

For example:

  • COVID-19-related AI solutions

  • AI solutions to social problems (including papers with humanities perspectives)

  • AI solutions related to SDGs


(2) AI and human wellbeing

For example:

  1. Reconsidering definitions and measurements of human wellbeing

    • New success metrics for wellbeing AI

    • Interdisciplinary research and approaches, including (but not limited to):

      • Positive psychology

      • Positive computing

      • Predictive medicine

      • Economics

      • Social computing

      • Job replacement and disparity

      • The neuroscience of happiness and pleasure

      • Multi-agent social simulations

      • Cultural algorithms

      • Flourishing environments

      • Cross-cultural analyses of values

  2. Machine learning and other advanced analyses for health and wellness

    • Machine learning methods in health and wellness domains

    • Theoretical and empirical research on wellbeing AI

    • Discussions that address the possibilities and limitations of current wellbeing AI technologies

    • Deep learning

    • Data mining

    • Knowledge modeling for wellness

    • Collective intelligence and knowledge

    • Life log analyses (e.g., vital data analyses, Twitter–based analyses)

    • Data visualization

    • Human computation

    • Biomedical informatics

    • Personal medicine

  3. Better system design for wellbeing

    • Social data analyses and social relation design

    • Mood analyses

    • Human–computer interaction

    • Healthcare communication systems

    • Natural language dialogue system

    • Personal behavior discovery

    • "Flow" and creativity

    • Compassion

    • Calming technologies

    • Affective (Kansei) engineering

    • Gamification

    • Assistive technologies

    • Ambient Assisted Living (AAL) technologies

    • Medical recommendation systems

    • Care support systems for aged persons

    • Web services for personal wellness

    • Games for health and happiness

    • Life log applications

    • Disease improvement experiments (e.g., metabolic syndrome, diabetes)

    • Sleep improvement experiments

    • Healthcare/disability support systems

    • Community computing platforms

(3) AI and human fairness

For example:

  1. Definition and measurement of fairness

  • “Human in the loop" computational systems

  • Fairness criteria and metrics

  • New success metrics for fair AI

  • Bias and fairness in machine learning

  • Responsible AI

  • Trusting AI

  • Social computing for trusting "human in the loop" computations

  • Multi-agent simulations of fairness

  • Game-theory-based analyses of fairness


  1. Interpretable AI

  • Human bias vs. computational (data) bias

  • Interpretability of machine learning systems

  • Accountability of black box prediction models

  • Interpretable AI for precision medicine

  • Interpretability in human–robot communications

  • Bias analysis on social media

  • Political orientation analyses

  • Accuracy and efficiency issues in health

  • Economics and other fields

  • Causal inferences about fairness

  • Actionable recommendations based on causal inference


  1. Better fairness systems design

  • Empirical and technical research on better fairness systems design

  • Criteria and metrics for fairness

  • Fairness in robotics

  • Fairness in machine-learning systems/software,

  • Fairness in social media

  • Fairness in “human in the loop systems”

  • Fairness in collective systems, recommendation systems, and personalized search engines

(4) Ethical issues regarding “AI and humanity”: Desirable human–AI partnerships

For example:

  • Ethical and philosophical discussions on desirable human–AI partnerships

  • Machine intelligence vs. human intelligence

  • Effects of AI on human societies and human "ways of thinking"

  • Issues related to AI-mitigated job insecurity and basic income

  • Mis/disinformation on social media

Format

The symposium will consist of invited talks, presentations, posters, and interactive demonstrations.

Submission Information

Submission Format and Guideline


Authors should submit either full papers of up to 8 pages (minimum 6 pages) or extended abstracts of up to 2 pages. Extended abstracts should state your presentation type (short paper (1–2 pages), demonstration, or poster presentation). All submissions should be uploaded to AAAI's EasyChair site at https://easychair.org/conferences/?conf=sss22, and in addition, email your submissions to aaai2022-hfif@cas.lab.uec.ac.jp by December 3, 2021.


*The deadline date has been extended! New deadline date: December 3rd.

Important Dates

Submission deadline: December 3, 2021

Author notification: December 10, 2021

Camera-ready papers: February 4, 2022 (subject to change)

Registration deadline: March 4, 2022

Symposium: March 21–23, 2022


Day 1 March 21, 2022 Monday


[Welcome and Self-Introduction]

9:00 am–10:00 am, March 21 (1:00 am–2:00 am, March 22 (JST))


[Overview]

10:00 am–10:30 am, March 21 (2:00 am–2:30 am, March 22 (JST))


How Fair is Fair? Achieving Wellbeing AI

Takashi Kido



10:30 am–11:00 am, March 21 Break


[Overview]

11:00 am–11:30 am, March 21 (3:00 am–3:30 am, March 22 (JST))

How to cope with bias in Well-being AI - Towards fairness in Well-being AI by personal and long-term evaluation?

Keiki Takadama


[Social Interaction] (Presentations from Stanford)

11:30 am–12:00 pm, March 21 (3:30 am–4:00 am, March 22 (JST))

Should Social Robots in Retail Manipulate Customers?

Oliver Bendel and Liliana Margarida Dos Santos Alves


12:00 pm–12:30 pm, March 21 (4:00 am–4:30 am, March 22 (JST))

The SPACE THEA Project

Martin Spathelf and Oliver Bendel



12:30 pm–2:00 pm Lunch


[Social Interaction] (Presentations from Stanford)

2:00 pm–2:30 pm, March 21 (6:00 am–6:30 am, March 22 (JST))

Monitoring and Maintaining Student Online Classroom Participation Using Cobots, Edge Intelligence, Virtual Reality, and Artificial Ethnographies

Robert Reynolds, Ana Djuric, Meina Zhu, Weisong Shi, and Thomas Palazzolo


2:30 pm–3:00 pm, March 21 (6:30 am–7:00 am, March 22 (JST))

AI agents for facilitating social interactions and wellbeing

Hiroaki Hamada and Ryota Kanai


[Education ] (Presentation from Japan)

3:00 pm– 3:30 pm, March 21 (7:00 am –7:30 am, March 22 (JST)

How to teach AI programming for Elementary Students? - A Case Study of AI Conversation Robot Programming at UEC Programming School

Hiromitsu Yamaguchi, Hirofumi Abe, Toshiyuki Shimazaki, Kenzo Ozaki, and Masayuki Numao


3:30 pm–4:00 pm, March 21 Break


[Care for Elderly Persons] (Presentations from Japan)

4:00 pm–4:30 pm, March 21 (8:00 am–8:30 am, March 22 (JST)

Automatic Textual Care Record Generation for Smart Nursing

Masayuki Numao and Hayate Kondo

4:30 pm–5:00 pm, March 21 (8:30 am–9:00 am (JST))

Modeling, Monitoring and Measuring of Social Isolation for Community-based Care in Nursing Home

Masayuki Numao and Shintaro Nagama


[Data Analysis] (Presentation from Japan)

5:00 pm–5:30 pm, March 21 (9:00 am–9:30 am, March 22 (JST))

Well-being Data Origination Using MROCs with Variable Quest: A Case Analysis of Gloom during COVID-19 Pandemic

Teruaki Hayashi, Yumiko Nagoh, Kai Ishikawa, Hirohiko Ito, Kenichiro Tsuda, and Yukio Ohsawa



6:00 pm–7:00 pm, March 21 Reception


Day 2 March 22, 2022 Tuesday


Welcome

9:00 am–10:00 am, March 22 (1:00 am–2:00 am, March 23 (JST))


[Cognitive Bias] (Presentation from Greece)

10:00 am–10:30 am, March 22 (19:00 pm –19:30 pm, March 22 (EET))

Sense and Sensitivity: Knowledge Graphs as Training Data for Processing Cognitive Bias, Context and Information Not Uttered in Spoken Interaction

Christina Alexandris


10:30 am–11:00 am, March 22 Break


[Fairness] (Presentation from Germany)

11:00 am–11:30 am, March 22 (19:00 pm–19:30 pm, March 22 (CET))

Fairness for Drivers with Additive Costs in Emerging Vehicle Routing Problems

Martin Aleksandrov

Fairness (Presentations from Stanford)

11:30 am–12:00 pm, March 22 (3:30 am–4:00 am, March 23 (JST))

Fairness-aware Naive Bayes Classifier for Data with Multiple Sensitive Features

Stelios Boulitsakis Logothetis


12:00 pm–12:30 pm, March 22 (4:00 am–4:30 am, March 23 (JST))

Advancing Fairness in Public Funding Using Domain Knowledge

Thomas Goolsby, Sheikh Rabiul Islam, and Ingrid Russell


12:30 pm–2:00 pm, March 22 Lunch


[Sleep] (Presentations from Japan)

2:00 pm– 2:30 pm, March 22 (6:00 am–6:30 am, March 23 (JST))

REM Detection Based on Combination of Multi-scale Detections and Automatic Adjustment of Personal Bio-vibration Data of Mattress Sensor

Iko Nakari, Naoya Matsuda, and Keiki Takadama


2:30 pm– 3:00 pm, March 22 (6:30 am–7:00 am, March 23 (JST))

Analysis of Circadian Rhythm Estimation Process for Improving the Accuracy of Alzheimer Dementia Detection

Naoya Matsuda, Taiki Senju, Iko Nakari, and Keiki Takadama


3:00 pm–3:30 pm, March 22 (7:00 am –7:30 am, March 23 (JST))

A thermal environment that promotes efficient napping

Ohga Takahiro, Ashikaga Tomoyoshi, Nakai Miki, and Keiki Takadama


3:30 pm–4:00 pm, March 22 Break


[Agent Simulation] (Presentation from Japan)

4:00 pm–4:30 pm, March 22 (8:00 am–8:30 am, March 23 (JST))

Centralized versus decentralized digital identity architectures: Simulation models of data exchange

Yoshiaki Fukami, Takumi Shimizu, Teruaki Hayashi, Hiroki Sakaji and Hiroyasu Matsushima


[Market] (Presentations from Japan)

4:30 pm–5:00 pm, March 22 (8:30 am–9:00 am, March 23 (JST))

Feature Concepts as Pattern Language for Data-Federative Innovations

Yukio Ohsawa, Sae Kondo, and Teruaki Hayashi


5:00 pm–5:30 pm, March 22 (9:00 am–9:30 am, March 23 (JST))

Change Explanation in Financial Markets by Graph-Based Entropy and Inter-regional Interactions

Yosuke Nishikawa, Teruaki Hayashi, Takaaki Yoshino, Toshiaki Sugie, Yoshiyuki Nakata, Kakeru Itou, and Yukio Ohsawa


6:00 pm–7:00 pm, March 22 Plenary Session

Day 3 March 23, 2022 Wednesday

[Welcome]

9:00 am–10:00 am, March 23 (1:00 am –2:00 am, March 24 (JST))


[Symposium Wrap-up]

10:00 am–10:30 am, March 23 (2:00 am–2:30 am, March 24 (JST))

Discussion on fairness and Wellbeing AI


10:30 am–11:00 am Break


[Symposium Wrap-up]

11:00 am–11:30 am, March 23 (3:00 am–3:30 am, March 24 (JST))

Summary of New Insights and Questions


[Award Selection]

11:30 am–12:00 pm, March 23 (3:30 am–4:00 am, March 24 (JST))

Awards

Best Presentation Awards

1st place

AI agents for facilitating social interactions and wellbeing

Hiroaki Hamada and Ryota Kanai

2nd place

The SPACE THEA Project

Martin Spathelf and Oliver Bendel

3rd place

Should Social Robots in Retail Manipulate Customers?

Oliver Bendel and Liliana Margarida Dos Santos Alves


Special Paper Award

Fairness for Drivers with Additive Costs in Emerging Vehicle Routing Problems

Martin Aleksandrov


Organizing Committee

Co-Chairs


Takashi Kido (Teikyo University, Japan)

Keiki Takadama (The University of Electro-Communications, Japan)

Program Committee


Amy Ding (Carnegie Mellon University, USA)

Melanie Swan (DIYgenomics, USA)

Katarzyna Wac (Stanford University, USA and University of Geneva, Switzerland)

Ikuko Eguchi Yairi (Sophia University, Japan)

Fumiko Kano Glückstad (Copenhagen Business School, Denmark)

Takashi Maruyama (Stanford, USA)

Chirag Patel (Harvard University, USA)

Rui Chen (Stanford University, USA)

Ryota Ka nai (University of Sussex, UK)

Yoni Donner (Stanford, USA)

Yutaka Matsuo (University of Tokyo, Japan)

Eiji Aramaki (Nara Institute of Science and Technology, Japan)

Pamela Day (Stanford, USA)

Tomohiro Hoshi (Stanford, USA)

Miho Otake (Riken, Japan)

Yotam Hineberg (Stanford, USA)

Yukiko Shiki (Kansai University, Japan)

Yuichi Yoda (Ritsumeikan University, Japan)

Maki Sugimoto (Teikyo University, Japan)

Mitsuhiro Ogawa (Teikyo University, Japan)


Advisory Committee


Atul J. Butte (University of California San Francisco, USA)

Seiji Nishino (Stanford University, USA)

Katsunori Shimohara (Doshisha University, Japan)

Takashi Maeno (Keio University, Japan)

Robert Reynolds (Wayne University, USA)

*(Note) We are still in the process of finalizing the list of committee members for this symposium, so more researchers will be added.


Contact Information

Takashi Kido

Email: kido.takashi@gmail.com

Institution: Teikyo University, Advanced Comprehensive Research Organization

Institutional address: Kaga 2-1 1-1, Itabashi-ku, Tokyo, Japan, 173-8605