Online discussion platforms are perceived as the next-generation method of citizen involvement. Such platforms can collect, integrate, and synthesize opinions to achieve social good. Crowd-scale platforms are being developed and deployed in social experiments that involve citizens and local governments. In such platforms, human facilitation is often used to preserve the quality of the discussions. Human facilitators often face difficulties when the discussions grow in size. In this paper, we present “D-agree,” a crowd-scale discussion support system based on an automated facilitation agent. The agent extracts discussion structures from online discussions, analyzes them, and posts facilitation messages. We conducted small- and large-scale social experiments in Japan to assess the social impact of the platform. The results showcase the success of our automated facilitation agents in gathering valuable opinions from citizens. In addition, our experiments highlight the effect of an automated facilitation agent on online discussions. In particular, we find that combining the agent facilitator with human facilitators leads to higher user satisfaction.

Planning a city is a systematic process that includes time, space, and groups of people who must communicate. However, due to security problems in such war-ravaged countries as Afghanistan, the traditional forms of public participation in the planning process are untenable. In particular, due to gathering space difficulties and culture issues in Afghanistan, women and religious minorities are restricted from joining male-dominated powerholders’ face-to-face meetings which are nearly always held in fixed places called masjids (religious buildings). Furthermore, conducting such discussions with human facilitation biases the generation of citizen decisions that stimulates an atmosphere of confrontation, causing another decision problem for urban policy-making institutions. Therefore, it is critical to find approaches that not only securely revolutionize participative processes but also provide meaningful and equal public consultation to support interactions among stakeholders to solve their shared problems together. Toward this end, we propose a joint research program, namely, crowd-based communicative and deliberative e-planning (CCDP), a blended approach, which is a mixture of using an artificial-intelligence-led technology, decision-support system called D-Agree and experimental participatory planning in Kabul, Afghanistan. For the sake of real-world implementation, Nagoya Institute of Technology (Japan) and Kabul Municipality (Afghanistan) have formed a novel developed and developing world partnership by using our proposed methodology as an emerging-deliberation mechanism to reframe public participation in urban planning processes. In the proposed program, Kabul municipality agreed to use our methodology when Kabul city needs to make a plan with people. This digital field study presents the first practical example of using online decision support systems in the context of the neighborhood functions of Gozars, which are Kabul’s social and spatial urban units. The main objective was to harness the wisdom of the crowd to innovative suggestions for helping policymakers making strategic development plans for Gozars using open call ideas, and for responding to equal participation and consultation needs, specifically for women and minorities. This article presents valuable insights into the benefits of this combined approach as blended experience for societies and cities that are suffering long-term distress. This initiative has influenced other local Afghan governments, including the cities of Kandahar and Herat as well as the country’s central government’s ministry of urban planning and land, which has officially expressed its intention to collaborate with us.

Intergroup contact occurring through indirect means such as the internet has the potential to improve intergroup relationships and may be especially beneficial in high conflict situations. Here we conducted a three-timepoint online experiment to ascertain whether the use of a conversational agent in E-contact platforms could mitigate interethnic prejudices and hostility among Afghanistan’s historically segregated and persistently conflictual ethnic groups. 128 Afghans of Pashtun, Tajik, and Hazara backgrounds were assigned to one of four E-contact conditions (control with no conversational agent and three experimental groups that varied in the conversational agent settings). Participants in the experimental conditions contributed more ideas and longer opinions and showed a greater reduction in outgroup prejudice and anxiety than those in the control group. These findings demonstrate that E-contact facilitated by a conversational agent can improve intergroup attitudes even in contexts characterized by a long history of intergroup segregation and conflict.

Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal.