How Can AI Revolutionize The Public Sector
A 2016 US Government report, titled “Preparing for the Future of Artificial Intelligence” notes that one area of great optimism about AI and machine learning lies in its potential to improve people’s lives, and showed that we are facing a scenario in which there is a multitude of practical applications with which AI could materialize this improvement in the lives of citizens. The public sector can significantly increase its effectiveness and efficiency thanks to the incorporation of AI solutions through three main cross-cutting application domains: the first, the streamlining of internal operations; the second, the improvement of quality in the provision of public services to citizens and companies and, lastly, the contribution to the creation of an ecosystem in which technology-based companies can flourish in the field of AI, relying on the triple helix model -Industry, Academia, and Government. If we look at sector-wise application areas, we can identify a wide variety of use cases, of which a few are mentioned below: Tourism: Intelligent reservation management, and location and personalization services to meet the specific needs of each visitor. Healthcare: Chatbots for the patient, sequencing of the human genome; applications that make use of the electronic medical record, based on the International Classification of Diseases (ICD-10); validation of whether a patient has ingested a certain substance; heart NMR processing, etc. All this leads to a paradigm shift in the doctor-patient relationship, putting the patient at the center of the equation. Education: Automatic correction of exams, training of drone pilots, improvement of reading efficiency, curriculum design, etc. The key lies in personalization: it is about focusing on the person, on their abilities and possibilities, rather than on following a specific program throughout the class. Security: One sector where AI will make great progress is security, where AI-based solutions that unify criteria, share information, and globally analyze all inputs, are already being applied. Autonomous driving: In this area, it is necessary to have regulation as soon as possible, in line with that already established for the use of assisted parking systems for cars. It is necessary to take into account the relationship between the human factor, the infrastructure, and the vehicle, within the framework of the Vision 0 victims in traffic accidents in the 2050 horizon. A combination of science, technology & willingness It is important to highlight the following facts: • Most companies are reluctant to develop the technology. They generally prefer to outsource it. • Academic researchers have difficulties in selling their projects to companies. Sometimes it is justified to use the figure of an intermediary to help transfer technology from laboratories to the market. • Developing AI solutions do not require large infrastructures and those that are necessary can be shared if they are hosted in the cloud, which is termed as “Cloudsourcing”. • Sometimes, it is about developing microservices that accompany the decision-maker in decision making. The demonstration of the benefits that these small developments can bring constitutes a powerful element of revitalization. • The available technology (apps, libraries, sensors, Arduino, etc.) is very affordable, which provides numerous opportunities. We are in a favorable technological moment, due to the advancement of standardization and the ease of sharing, along with the ongoing Work From Home (WFH) policy. • Collaboration between different types of agents favors the development of innovative applications. This collaboration can be articulated by creating environments of innovation in which people can test them. The Artificial Intelligence market is constantly evolving and presents great opportunities for private investments, but it must also be the object of interest on the part of public entities, both from the point of view of investment and at the level of regulation. Regarding the prioritization of AI investments by government administrations, two issues must be addressed: 1. Prioritization by technological areas and by areas of application. In many cases, AI can provide support services with a high customization component. This personalization represents a true paradigm shift in relations between the administration and the citizens. 2. The use of these instruments shall be associated with the satisfaction of certain social challenges. The roadmap must go through experimentation and then scale the systems. Recommendations These are a series of proposals that would contribute to taking advantage of the opportunities offered by AI, as a catalyst for the digital transformation in the public sector: • Prepare a national plan for Artificial Intelligence within the framework of a digital agenda for the nation, that helps to highlight application niches in public services where AI could be applied but has not been done yet. • Favour the creation of innovation groups in academia that serve to identify opportunities to apply new technologies, such as AI, to the provision of public services, considering efficiency and profitability. • Create test environments in which public employees and academics can collaborate with government officials. • Loosen up the limitations of the budget with regards to emerging technologies. • Decrease the rigidity of the legal framework, both in the budgetary and contracting fields in order to ensure quality research for the benefit of society.
A 2016 US Government report, titled “Preparing for the Future of Artificial Intelligence” notes that one area of great optimism about AI and machine learning lies in its potential to improve people’s lives, and showed that we are facing a scenario in which there is a multitude of practical applications with which AI could materialize […]
A 2016 US Government report, titled "Preparing for the Future of Artificial Intelligence" notes that one area of great optimism about AI and machine
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["Go", "artificial intelligence", "machine learning", "AI", "chatbots", "R", "ML", "Git", "microservices", "ViT", "arduino"]
["AI", "artificial intelligence", "machine learning", "ML", "chatbots", "microservices", "R", "Go", "Git", "ViT"]
Dean at Woxsen School of Business. He is a registered expert in Artificial intelligence, Intelligent Systems, Multi-agent Systems at the European Commission, and has been nominated for the Forbes 30 Under 30 Europe 2020 list.