Course 2024-2025 a.y.

This course, taught by Maria Cucciniello from the Department of Social and Political Sciences and Omiros Papaspiliopoulos from the Department of Decision Sciences, is designed to prepare students to manage innovation and effectively utilize AI within the service system and the public sector. Covered topics include:

• Exploration of the Innovation Ecosystem in Public Services: This module would provide a detailed overview of the current landscape of innovation within public services, exploring different types of innovations ranging from technological advancements to process improvements and service delivery models. It would also analyze the impact of these innovations on service efficiency, effectiveness, and public value creation.
• An analytical framework for assessing needs, setting priorities, and facilitating decision-makingto implement innovation processes tailored to a public organization's specific context. This part of the course would introduce students to a comprehensive framework designed to assess organizational needs and readiness for innovation. It would include methods for setting strategic priorities and making informed decisions to tailor innovation processes to the unique contexts of public organizations. This framework would be supported by real-world case studies and interactive sessions where students can apply these concepts in simulated environments.
• Data, Models and Algorithms: Overview of foundational concepts and comparatives: statistics vs machine learning, data science vs AI, data modelling vs algorithmic modelling, predictive modelling vs policy evaluation, structural vs predictive models, augmented vs articifial intelligence.
• Overview of learning and algorithms: This part will focus on types of learning: supervised, unsupervised, semi-supervised, self, reinforcement, active and connections; Types of predictive models: high-dimensional linear models, decision trees, neural networks; Causal inference, intervention analysis and policy evaluation; Experiments and Big Data; Use of machine learning for policy evaluation.
• Predictability-Explainability-Stability: Reproduciblity crisis in Science; Stability of Machine Learning algorithms: illustrations and risks; The explainability-predictability frontier
• Data and Algorithms in the Society: Role of data: data as a phenomenon, the Big Data mythodology, "end of theory", data journeys, data to the public; Algorithmic bias: how and why, algorithms and inequality, people analytics; Adoption of AI by decision makers
• Exploring AI's Societal Impact, focus on the ethical, legal, and social implications of AI technologies, examining their effects on privacy, employment, and societal norms. Students will engage with current case studies to critically analyze how AI is reshaping social expectations and behaviors. Key topics include data ethics, algorithmic bias, and the balance between innovation and regulation.
• Innovation in Practice: Focus on how AI is driving innovation across various sectors, enhancing productivity and addressing complex challenges. This part of the course explores practical applications of AI in healthcare, and urban planning, education and arts and culture among others. Students will learn about the integration of AI tools in real-world scenarios, evaluating both successes and limitations, and the skills necessary to implement AI solutions effectively. 
• Future Trends in AI: Investigate the cutting-edge developments and emerging trends in AI, assessing their potential impacts on future societal and technological landscapes. 

Enhancing the learning experience, the course will feature guest speakers from diverse fields, providing insights into the challenges and successes of AI implementation and on-site visits to organizations actively utilizing AI will give students practical exposure to the dynamics of AI in action. Additionally, during the course through discussions and project-based learning, students will explore speculative scenarios and prepare for the evolving roles that AI might play in shaping our world.

•   Analyze the Role of AI in Public Service Innovation: Understand and critically assess how AI drives innovation in public services, enhancing service delivery in sectors such as healthcare, education, and urban development.
•  Communicate AI Concepts Effectively: Articulate complex AI concepts, findings, and recommendations clearly and effectively to diverse audiences, including stakeholders and decision-makers within public organizations.
•  Critically Engage with AI's Broader Impacts: Engage critically with discussions on the broader implications of AI, including stability, predictability, explainability, and the role of AI in data-driven societies.

•  Evaluate Ethical and Social Implications of AI: Examine and articulate the ethical, legal, and social impacts of AI technologies on privacy, employment, and societal norms, demonstrating an understanding of data ethics and algorithmic bias.
•  Implement AI Technologies in Practical Settings: Gain hands-on experience through case studies, on-site visits, and interactions with AI applications in real-world environments, developing practical skills in integrating AI tools and methodologies.
•  Use Analytical Frameworks to Drive Decision-Making: Employ comprehensive frameworks to assess organizational needs, set strategic priorities, and facilitate decision-making processes tailored to the unique contexts of public organizations.
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These outcomes aim to equip students with a robust set of skills and knowledge to navigate and influence the evolving landscape of AI and innovation within the public sector and broader society.

• Lectures
• Guest speaker's talks (in class or in distance)
• Company visits
• Practical Exercises
• Collaborative Works / Assignments
• Interaction/Gamification

The learning experience in this course encompasses more than traditional lectures. It incorporates a dynamic blend of teaching methodologies aimed at providing practical, hands-on understanding of AI and its application in public services.

Interactive Problem-Solving: Throughout the course, students will tackle problem sets designed to apply the analytical tools discussed in lectures. These exercises will challenge students to explore models of AI implementation, devise innovative solutions, and strategize organizational designs that enhance public service delivery.

Case Studies and Practical Application: Stylized cases will be introduced and discussed in class to apply theoretical models to real-world scenarios. These will help students make competitive assessments of markets, evaluate the effects of organizational changes or business practices, and understand the implications of policy actions. This approach encourages students to integrate their own insights and perspectives into the learning process.

Expert Insights and Field Exposure: The course features guest speakers, including  experts and officials from diverse fields such as healthcare, urban development, and international organizations, who will share their experiences with AI. These sessions aim to deepen understanding of how AI and data-driven strategies are applied in real-world contexts, covering both challenges and success stories.

On-Site Visits: Students will engage in an on-site visit to an organization actively utilizing AI technologies. This visit is designed to provide a practical perspective on how AI tools are implemented in the field, offering students a closer look at the dynamics of AI in action.

By the end of this course, students will not only grasp the theoretical underpinnings of AI in public service innovation but will also be well-equipped to apply these concepts in practical, ethically informed ways that enhance public value and societal well-being.

The final grade will be determined by weighting grades for the following components:

 Active Participation (10%)

• Students are expected to actively participate in class activities and other interactive components of the course.
• Participation will be assessed based on engagement, discussion contribution, and peer collaboration.

Group Work, Presentation, and Discussion (40%)

• Students will work in groups to complete a project related to the course topics. 

Grading will be based on the quality of the research, the clarity of the presentation, and the ability to facilitate a meaningful discussion.

Final Written Exam (50%)

• The final exam will consist of open and/or closed answer questions designed to test students' knowledge and understanding of the course material.
• The exam will cover all topics discussed throughout the course, including fundamentals of innovation and AI concepts for the public service context, case studies of AI in various sectors (i.e. healthcare, urban development, education, arts and culture) and the societal impacts of AI technologies.

Reading List: The required readings will be available on Blackboard.