Citizen-generated data (CGD) refers to people’s involvement in generating data to directly monitor, demand or drive change on issues that affect them (DataShift, 2015). CGD can create opportunities for citizens to play a more active role in examining a situation and taking action, such as, for example, in local development and collaborative strategies for monitoring, auditing, planning and decision-making. It is also argued that CGD can help create new kinds of relationships, participation and forms of governance involving citizens and the public sector (Lämmerhirt, Gray, Venturini, & Meunier, 2019). To this end, citizens need to use data for new forms of governance and go beyond data production. This requires data literacy which we define here as “the desire and ability to constructively engage in society through and about data” (Letouzé et al., 2015, p. iv). According to Letouzé et al. (2015), the attitudes and skills implied by this definition go beyond the ability to produce, evaluate, use and share data, and include computational literacy, which encourages individuals to seek algorithmic approaches to problems and use modelling as a way to identify relationships. The above definition of data literacy pushes us to consider how citizens could participate in forms of algorithmic governance by being engaged in a matter of concern “through or about data”, without the need to conduct advanced analytics or having coding skills. A small scenario is proposed of how citizens could use CGD to develop computational literacy and support decision-making for local development. The scenario involves data about quiet areas collected by citizens using the Hush City mobile app in Berlin. One of the main goals is "to exploit data collected through the Hush City app in integrated city planning processes, in order to develop policies and planning guidelines grounded on people preferences” (Radicchi, 2017, p. 507). The CGD could be used to develop, in collaboration with the Municipality of Berlin, a fair algorithmic matching system that matches the quiet areas identified by citizens with the areas localised by the Municipality based on their degree of environmental justice. The Municipality of Berlin has an Atlas of Environmental Justice and related open-access map. Several case studies show a relationship between a lower social status and the distribution of environmental hazards, like air pollutants, noise and toxic sites, among others. For the sake of simplicity, this scenario comprises only the hypothetical construction of an individual belief model (Lee at al., 2019) in which citizens participate together with other stakeholders. This model includes three steps: 1) Feature selection: the identified quiet areas will be used to compare characteristics of these areas with the degrees of environmental justice of the areas localized by the municipality. Citizens also weigh each of these characteristics. 2) Machine learning model: development and training of an algorithm reflecting citizens’ decision criteria. The machine learning method will use pairwise comparisons between a pair of alternatives that vary along with the characteristics derived from the previous step. Pairwise comparisons have been used to encourage moral deliberation and reach a reflective equilibrium in determining fairness principles (Lee at al., 2019). This method will allow people to become familiar with different areas and develop and refine their beliefs. 3) Model visualization: once the model is built, it is visualized to show example decisions made by the model so that citizens can understand and see if the model reflects their beliefs. DataShift (2015). What is citizen-generated data and what is the DataShift doing to promote it? Available at: http://civicus.org/images/ER cgd_brief.pdf (accessed 14 February 2019.). Lämmerhirt, D., Gray, J., Venturini, T., and Meunier, A. (2019). Advancing sustainability together? Citizen-generated data and the Sustainable Development Goals. Available at: http://www.data4sdgs.org/sites/default/files/services_files/Advancing%20Sustainabilit y%20Together%20CGD%20Report_0.pdf (accessed 14 October 2019.). Lee, M. K., Kusbit, D., Kahng, A., Kim, J. T., Yuan, X., Chan, A., Noothigattu, R., See, D., Lee, S., Psomas, C. A., & Procaccia, A. (2019). WeBuildAI: Participatory framework for algorithmic governance. In Proceedings of the ACM : Human-Computer Interaction: Volume 3 Issue CSCW. (CSCW 2019). Article 181, 35 pages. Letouzé, E., Noonan, A., Bhargava, R., Deahl, E., Sangokoya, D., & Shoup, N. (2015, September). Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data. Data-Pop Alliance. Radicchi, A. (2017). Hush City. A new mobile application to crowdsource and assess "everyday quiet areas" in cities. Conference Paper, Invisible Places 7-9 April 2017, São Miguel Island, Azores, Portugal.