LA PERSONALIDAD CORPORATIVA
12. Sinergia social Las entidades se han de ayudar por impulso de los colectivos internos, especialmente si estas son del sector servicios en el que el factor humano interviene
Many cities are becoming ‘live laboratories’ as data intensive technologies like AI and IoT are integrated into the operation of public infrastructure and spaces as a means of optimising public services355. The literature mostly covers stories of success and points to the potential of ICT enabled innovations to improve cities’ resilience and sustainability356. The reviewed scientific literature describes a range of services and their provision in the smart city context, mostly through individual case studies. These range from transportation to waste management. While significant, the yet not fully untapped nor systematically researched potential of ICT application in urban services is linked – as in other ICT applications discussed in this review – mostly to service improvement and efficiency gains357.
346 Apolitical. (2017). US police use fata to focus on places, not people, and cut crime by up to 40%. Retrieved from https://apolitical.co/solution_article/us-police-use-data-focus-places-not-people-cut-crime-40
347 Goldsmith, S., & Crawford, S. (2014). The responsive city: Engaging communities through data-smart governance. John Wiley & Sons. 348 Horvitz, E. (2016). Artificial intelligence and life in 2030. Stanford University.
349 Gangadharan, S. P., Eubanks, V., & Barocas, S. (2014). Data and discrimination: collected essays. Open Technology Institute & New America; Newman, N. (2014). How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population. US Federal Trade Commission; Barocas, S., & Selbst, A. (2016). Big Data’s Disparate Impact. California Law Review, 104(1), 671-729; Madden, M., Gilman, M. et al. (2017) Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans. Washington University Law Review, 95(1).
350 Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the Shreveport Predicitve Policing Experiment. RAND; Angwin, J., Larson, J. et al. (2016). Machine Bias. ProPublica. Retrieved from https://www.propublica.org/article/machine-bias-risk-assessments-in- criminal-sentencing; Lum, K., & Isaac, W. (2016). To predict and serve? Significance, 15-19; Obar, J., & McPhail, B. (2018). Preventing Big Data Discrimination in Canada: Addressing Design, Consent and Sovereignty Challenges. Data Governance in the Digital Age: Special Report, 56-64.
351 Wiseman, J., & Goldsmith, S. (2017). Ten great ways data can make government better. Data-smart city solutions, Harvard Kennedy School Ash Center for Democratic Governance and Innovation. Retrieved from https://datasmart.ash.harvard.edu/news/article/ten- great-ways-data-can-make-government-better-1041;
352 Madaio, M., Chen, S.-T. et al. (2016). Identifying and prioritizing fire inspections: a case study of predicting fire risk in Atlanta. Georgia Tech, College of Computing.
353 Bertot, J., Estevez, E., & Janowski, T. (2016). Universal and contextualized public services: Digital public service innovation framework. Government Information Quarterly, 33, 211-222.
354 UN Department of Economic and Social Affairs. (2018). UN E-Government Survey 2018. United Nations.
355 Bass, T., Sutherland, E. & Symons, T. (2018). Reclaiming the Smart City. Nesta; Naafs, S. (2018). 'Living laboratories': the Dutch cities amassing data on oblivious residents. Retrieved from https://www.theguardian.com/cities/2018/mar/01/smart-cities-data-privacy- eindhoven-utrecht
356 UN Department of Economic and Social Affairs. (2018). UN E-Government Survey 2018. United Nations.
Many authors discussing the effects of digitalisation in the context of smart cities relate it to the possibilities that can be unleashed by IoT. This technology has made possible the recent implementation of smart grids358, smart transportation359, smart healthcare360, which are the building blocks of the smart cities concept361. Telecommunication companies, consultancy firms and researchers speak of the potential of IoT to transform city services, primarily through changing how government entities gather data. The analysis of this information enables public officials to:
Improve services by basing them on real-time information, which can improve trust between government and citizens.
Increase citizen safety through faster and more effective emergency response, and monitoring of streets and other public areas.
Optimise the use of infrastructure, reduce congestion and energy use through leveraging real- time data to meet the changing demands (e.g. by reacting quickly to fast-changing traffic patterns). Improve operational performance, management and maintenance through proactive
monitoring of critical public infrastructure and optimisation of processes362.
Besides IoT, digital technologies, often based on geo-spatial data, allow citizens to articulate their demands. For example, websites and apps such as Tvarkau miestą363, FixMyCity364 and Tu Bogotá365 enable residents in their respective cities to report incidents related to municipal affairs, from issues with stray pets to public transport, by selecting a specific place on the map where the issue has occurred. Citizens can upload pictures to illustrate their complaints. Understanding citizens’ demands helps governments respond faster and more effectively.
Tracking statistics on citizens’ requests over time can help policy-makers inform strategies for cities’ development and become more responsive. Applications enabled by geo-spatial data are also inherently democratic: everyone can voice their opinions about what needs to be fixed366.
Nonetheless, governments are often criticized for employing IoT technologies and sharing geo-spatial data over privacy concerns. In Toronto, for example, Alphabet’s subsidiary Sidewalk Labs and a publicly-mandated Waterfront Toronto in 2017 made plans to develop a 12-acre area into a smart neighbourhood, but the project received public rebuke when Sidewalk Labs failed to assure the public that personal data will not be accessible to third parties367. Similarly, in 2013, Seattle’s Police Department implemented wireless sensors throughout the city to provide better emergency response, but then faced backlash because sensors could be used to track people’s wireless devices368.
358 Chen, S.-Y., Song, S.-f, Li, L., & Shen, J. (2009). Survey on smart grid technology. Power System Technology, 33(8), 1-7.
359 Adeli, H., & Jiang, X. (2009). Intelligent infrastructure: neural networks wavelets, and chaos theory for intelligent transportation systems and smart structures. CRC Press.
360 Anisetti, M., Ardagna, C. et al. (2018). Privacy-aware Big Data Analytics as a Service for Public Health Policies in Smart Cities. Sustainable Cities and Society; Demirkan, H. (2013). A smart healthcare systems framework. IT Professional, 15(5), 38-45.
361 Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65-82; Chourabi, H., Nam, T. et al. (2012). Understanding Smart Cities: An Integrative Framework. In 45th Hawaii International Conference on System Sciences (2012); Hashem, I.A.T, Chang, V. et al. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748-758.
362 Rabari, C., & Storper, M. (2014). The digital skin of cities: Urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data. Cambridge Journal of Regions, Economy and Society, 8(1), 27-42.; Bass, T., Sutherland, E. & Symons, T. (2018). Reclaiming the Smart City. Nesta; PwC. (2016). From connect to applied solutions. Data-driven cities; national Infrastructure Commission. (2018). Data for public good; McKinsey Global Institute. (2018). Smart Cities: Digital Solutions for a More Liveable Future; Ju, J., Liu, L., & Feng, Y. (2018). Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommunications Policy, 42(10), 881-896; CA Technologies, & Deloitte. (2018). Building our cities smarter: How APIs Take Smart Cities From Concept to Value; Atos. (2015). Data driven government. Preparing for the age of the citizen; Mora, L., Deakin, M., & Reid, A. (2018). Strategic principles for smart city development: A multiple case study analysis of European best practices. Technological Forecasting and Social Change; Ojo, A., Curry, E., Janowski, T., & Dzhusupova, Z. (2015). Transforming city governments for successful smart cities - the SCID framework. Transforming City Governments for Successful Smart, 43-65; UN General Assembly. (2016). Preparatory Committee for the United Nations Conference on Housing and Sustainable Urban Development (Habitat III). United Nations.
363 Tvarkau miesta. (n.d.). Retrieved from https://tvarkaumiesta.lt/new_problem 364 Fixmycity Greece. (n.d.). Retrieved from http://glyfada.intelligentcity.gr/ 365 Tu Bogotá. Retrieved from http://www.idecabogota.appspot.com/main.html