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1. Meeting the SDGs: Enabling the Goals by
Cooperation with Crowd using a Conversational
Agent Platform
Co-authors: Jawad Haqbeen1, Takayuki Ito2, Sofia Sahab1and, Rafik Hadfi1,
Takumi Sato1 and Shun Okuhara1
1 Nagoya Institute of Technology
2 Kyoto University
15th International Conference on Knowledge, Information and Creativity
Support System
How conversational agent helps crowd and institution work together?

2. Abstract
• The SDGs, in which the world has pledge to “Leaving no one behind”
• Responding to Goal No. 4 on the agenda
• Efforts to establish a sustainable city and community to all population
must be pushed forward
• How to reach everyone, that is the challenge?
• To challenge the SDGs and especially respond to G4 promoting civic
engagement by using Civic technologies such as social platform.
• However, Facebook and Twitter, mediated to engage crowd but can not
collect people’s result-driven insight.
• Moreover, getting the participants real insights without is difficult by
using self-administered survey.
• Therefore we used Internet-based online discussion platform, to collect
people’s real insights on neighborhood functions.
• In this research we will evaluate and verify how artifacts discussion
mediation leads discussion and generate more ideas. 2

3. OVERVIEW
Background
 Why it is important to do things collectively?
 Mediating Crowd, Navigating their opinions from finding the soft solutions
 How Civic Tech let institutions and crowd work together
Method
▪ Materials and proposed method
Results
 Discussion the advantages and limitations fr
Conclusion
▪ Contribution of the research
▪ Future research
3

4. Background and Related Study
• With the development of CIVIC technologies such as the Facebook, Twitter,
and the Internet-based discussion forums, big data is increasingly
becoming a new emerging data collection means in the field of city
planning and sustainability. These platforms challenging the SDGs as the
these mediate groups of individuals to engage collectively that leads to
Collective Intelligent for efficient planning [1].
4
A project of the MIT Center for
Collective Intelligence in
collaboration with many other
organizations. Lead by [1]
Prof.Thomas W. Malone
Helping communities in Least Developed
Countries (LDCs) become more climate
resilient?
Nagoya published a planning booklet which includes opinions
gathered by COLLAGREE. A discussion support platform

5. Previous Study
1) Promoting Urban Dialogues with Collective Intelligence
2) Cyber-Physical Town Meeting: Kabul Municipality
3) Insights from Large-scale discussion on COVID-19 in Collective
Intelligence
5
Kabul City Mayor
Chief of Staff
1. [1] Jawad Haqbeen, Takayuki Ito, Rafik Hadifi, Tomohiro Nishida, Zoia Sahab, Sofia Sahab, Shafiq Roghmal and Ramin Amiryar, “Promoting Discussion with AI-based Facilitation: Urban Dialogue with Kabul
City”. The 8th ACM Collective Intelligence 2020, Boston-Copenhagen, June 18-19, 2020
2. [2] Jawad Haqbeen, Takayuki Ito, Rafik Hadfi, Zoia Sahab, Sofia Sahab, Ramin Amiryar, and Tomohiro Nishida. “Usage & application of AI-based discussion facilitation system for urban renewal in selected
districts of Kabul city: Afghanistan Experimental View”, The Japanese Society for Artificial Intelligence (JSAI2020), Kumamoto, Japan. June 9-12, 2020
3. [3] Jawad Haqbeen, Takayuki Ito, Sofia Sahab, Takumi Sato, Shun Okuhara and Murtaza Hofaini,. “Insights from a Large-Scale Discussion on COVID-19 in Collective Intelligence”. The 20th IEEE/WIC/ACM
International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '20), 2020
Citizens Discussing during
district meeting, D5, Kabul.

6. Methodology
Our general Methodology is to conduct experiment using conversational
agent as facilitator where the agent expected to encourage participants to
engage in themed discussion.
1. Benefits Natural Language Processing and Generation Technologies
2. Crowd Support in collaboration with Kabul Municipality and A-SDGs.
3. Used accidental sampling to collect participants by posting call for
participation on Facebook
4. We boost the post by using Facebook Ad.
6

7. Methodology
The agent operate the content according to following algorithm
1. Collect and restructure the content as a tree.
2. Classify the node of tree base on IBIS type: issues, positions, pros or cons.
3. Construct or update the IBIS discussion tree
4. Set the desired argumentation semantics.
5. Infer the target argument(s) based on semantics and more than 200 predefined
facilitation rules which is based on support facilitator’s mechanism.
6. Apply the Natural Language Generation (NLG) on the obtained target argument (s).
7. Post the facilitation messages to the discussion at a particular argument point. 8. Go to
1 and repeat.
7

8. IBIS(Issue—Based Information System)
・SDGs is more about fixing social issues in sustain approach
How to challenge to fix the wicked problem?
Discussion Structure
Annotation of discussion files

9. Participants Recruiting
9
Call For participation
Participant recruitment workflow
1099:5104
Participants: Opinioons
Reached to 144 K
1.1 K participatns
1.1 K participatns

10. System Login Demo
10

11. 11
Login Demo Video
Kabul Municipality Created Demo video on how to login D-Agree and called citizens to
participate

12. System Interface
12

13. Experiment Setting
1. First two months
• How to Promote Civic endearment in A-SDGs?
• First month: Discussion with without Agent (Jan 20-Feb 16, 2020)
• Second month: Discussion with agent (Feb 17-Mar 18, 2020)
• Research Objectives: To verify effects of agent facilitation in opinion
generation
• Organizational Objective: To collect people’s opinion related localizing of
SDGs and civic engagement related to SDGs discussion.
2. We extended the second month discussion
• Duration: Mar 18- May 28, 2020.
• The same setting of second month apply to collect the significant.
• Research Objectives: Collect more data to verify that conversational AI has
sustainable effects or not compare to that with second month.
Organizational Objective: To collect people’s opinion related 4 other
discussion themes.
• Discussion theme no. 5
13

14. Results
• First month Discussion without agent
• Number of Ideas and Pros: 402 and 223
• Number of Issues and Cons: 393 and 264
• Second month Discussion with agent
• Number of Ideas and Pros: 484 and 294
• Number of Issues and Cons: 272 and 121
• All Discussion
• Number of Ideas and Pros: 1862 and 756
• Number of Issues and Cons: 1682 and 626
• Number of opinions 5144, from 1099 participants
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Ideas + Pros > Issues + Cons
It is because of agent effect
Ideas + Pros < Issues + Cons
It is because of no facilitation
to raise the ideas for issues
Ideas + Pros > Issues + Cons
It is because of agent effect
and deliberation

15. Results
15
The pros generated and
extracted more compare to
cons in discussion with agent
Pros > Cons
The idea generated and
extracted more compare to
issues in discussion with
agent
Issues < Ideas
Issues < Ideas
Discussion with agent
Pros < Cons
Without agent

16. 0
200
400
600
800
1000
1200
1400
1600
1800
2000
Idea Issue Pros Cons
Results
16
The pros generated and
extracted more than that to
cons in discussion
*First month is without agent
Pros > Cons
The ideas generated and
extracted more than that to
issues in discussion
*First month is without agent
Issues < Ideas

17. Results
17
Average Responsive time
among two posted items
If at day d there were n messages posted by the participants and if
message i was posted at time ti then the average responsiveness rd
of day d is computed as rd = 1 n P i (ti+1 − ti).
Peak of delay discussion without agent

18. Conclusion
• We conducted a real social experiment where conversational agent used
to gather, analyze, facilitate the discussion and lead the generation of
ideas for fixing the issues.
• To meet the SDGs engaging people at large, mediating to lead the
discussion, deliberation and extraction the valuable insights.
• Agent-based facilitation verified for large-scale discussion
• The agent facilitation plays an important role to engage citizen with SDGs
relation meeting.
• Our next direction is to conduct 10-k scales discussion to harness the
super minds from crowd through qualitative content analysis of the
opinions and scoring of qualitative opinion from crowd for policy-making
of specific domain such as solid waste management.
18

19. 19
Images
Inauguration of Kabul Municipality Discussion League Project.

20. 20
Experiment Images
Cyber-Physical Kabul Municipality Town meeting