This document summarizes a presentation on using AI to better inform policymakers. It discusses how social media is increasingly used to provide feedback on government services and the challenges this data poses. Specifically, it notes that social media data contains both structured and unstructured elements. It also presents two case studies, where AI was used to analyze social media posts about public services. However, the results varied between groups analyzing the same data. This highlights the difficulty in reliably interpreting social media for policymaking. The discussion advocates for more research on building ethical AI solutions and ensuring data analysis results are representative. The next steps focus on developing theories around using AI in government and addressing issues like reliability and data representation.

1. AI as a mean to better inform
policy-makers: True or False?
Sehl Mellouli
Professor
Department of Information Systems
Université Laval
Samos Summit 2021

2. Introduction
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• People are more and more using social media to express themselves about the
different services that their governments are delivering.
• They can either provide positive or negative comments on government services.
• It becomes important for policy-makers to have the necessary tools to extract this
valuable knowledge in a comprehensive way and that they may consider in their
decision-making processes.

3. Introduction
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• There are different platforms that can be used for e-participation.
• As depicted in (Boudjelida et al., 2016):
ü 41% of e-Participation activities are implemented through social networks,
ü 24% through online forums,
ü 22% through government's' official web sites and
ü 13% through other online technologies.

4. Introduction
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5. Outline
• Introduction
• You said information ….
• Challenges for Policy-Makers
• Case Study
• Discussion
• Next steps
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6. You said information ….
• “Gather citizens of Limoilou, add more activities in our magnificent neighborhood,
and have shows more often!”
• “At 8h30 yesterday morning, At the most frequented roundabout by cars and
pedestrians. Two trucks are parked on the sidewalk while police are right next
door to manage traffic, impunity is the main cause of offenses committed. Please
say hello to the State: I haven't taken the photo while driving”
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7. You said information ….
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8. Outline
• Introduction
• You said information ….
• Challenges for Policy-Makers
• Case Study
• Discussion
• Next steps
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9. Challenges for Policy-Makers
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• Social media data is based on structured and unstructured data.
• The structured data (i.e. identified also as metadata) compromise for example the
user's information (e.g. name, description, spatial, temporal, etc.) and the content
information (e.g., number of likes, retweets, mentions, etc.).
• The unstructured data is related to the "user-generated" textual content that can
be for example tweets, replies and comments.

10. Challenges for Policy-Makers
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11. Outline
• Introduction
• You said information ….
• Challenges for Policy-Makers
• Case Study
• Discussion
• Next steps
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12. Case study 1
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13. Case Study 1
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• Public administration: “Plus que deux ann es... @Rue de l' le de Rhodes...
responsables de la SONEDE o tes vous?” (“More than two years... @ Ile
Rhodes street...SONEDE team, where are you?”)
• Anarchic construction: “Le Duplex ‘Av Kheireddine Bacha’: A l'instant
Destruction de l'extension sur le trottoir” (“Keireddine Bacha
Avenue: At the moment Destruction of the extension on the sidewalk”)
• Environment: “La beaute de la cite ghazela #ariana #avons nous un
etat???” (“The beauty of ghazela city #ariana #do we have a state???”)

14. Case Study 1
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15. Case Study 2
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16. Outline
• Introduction
• You said information ….
• Challenges for Policy-Makers
• Case Study
• Discussion
• Next steps
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17. Discussion
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18. Discussion
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19. Discussion
• Setting: 24 groups, each group with 4-5 participants
• Work on the same dataset
• Different results and different interpreation of results
• Governments have to develop strategies on how data can be interepreted
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20. Discussion
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21. Discussion
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Kotsiantis, S. B., Zaharakis, I., & Pintelas, P.
(2007). Supervised machine learning: A review
of classification techniques. Emerging artificial
intelligence applications in computer
engineering, 160, 3-24.

22. Outline
• Introduction
• You said information ….
• Challenges for Policy-Makers
• Case Study
• Discussion
• Next steps
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23. Next steps
• More theories to understand how to build and use AI solutions in Governments
• Ethical issues need to be considered in AI solutions
• To what extent the data analysis can be reliable for policy-makers?
• To what extent the data is representative of citizens opinions?
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24. References
1. Driss, O. B., Mellouli, S., & Trabelsi, Z. (2019). From citizens to government policy-makers: Social media data
analysis. Government Information Quarterly, 36(3), 560-570.
2. Boukchina, E., Mellouli., S., and Menif., E. (2018). From Citizens to Decision-Makers, A Natural Language
Processing Approach in Citizens’ Participation. International Journal of E-Planning Research. Vol.7 (2), p.20-34.
3. Corbett, J., and Mellouli., S. (2017). Winning the SDG battle in cities: how an integrated information ecosystem
can contribute to the achievement of the 2030 sustainable development goals. Information Systems Journal.
Vol. 27(4), 427-461.
4. Marzouki., A., Mellouli., S., and Daniel., S. (2017). Towards a Context-based Citizen Participation Approach: a
Literature Review of Citizen Participation Issues and a Conceptual Framework. 10th International Conference
on Theory and Practice of Electronic Governance (ICEGOV 2017). New Delhi, India, 7-9 March 2017.
5. Boudjelida., A., Mellouli., S., and Lee., J. (2016). Electronic Citizens Participation: State of the Art. 9th
International Conference on Theory and Practice of Electronic Governance (ICEGOV 2016). Montevideo,
Uruguay, 1-3 March 2016.
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