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PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

PORTALE DELLA DIDATTICA

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Systemic Risks of AI (Global Challenges - Society and Politics)

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A.A. 2026/27

Course Language

Inglese

Degree programme(s)

1st degree and Bachelor-level of the Bologna process in Ingegneria Informatica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica (Mechanical Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Design E Comunicazione - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo (Automotive Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo - Torino
1st degree and Bachelor-level of the Bologna process in Electronic And Communications Engineering (Ingegneria Elettronica E Delle Comunicazioni) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dei Materiali - Torino
1st degree and Bachelor-level of the Bologna process in Architettura (Architecture) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettrica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Aerospaziale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Biomedica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Chimica E Alimentare - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Civile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Edile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Energetica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Per L'Ambiente E Il Territorio - Torino
1st degree and Bachelor-level of the Bologna process in Matematica Per L'Ingegneria - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettronica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Fisica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Del Cinema E Dei Mezzi Di Comunicazione - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Architettura - Torino
1st degree and Bachelor-level of the Bologna process in Pianificazione Territoriale, Urbanistica E Paesaggistico-Ambientale - Torino
1st degree and Bachelor-level of the Bologna process in Civil And Environmental Engineering - Torino
1st degree and Bachelor-level of the Bologna process in Architettura - Torino
1st degree and Bachelor-level of the Bologna process in Architettura (Architecture) - Torino
1st degree and Bachelor-level of the Bologna process in Civil And Environmental Engineering - Torino
1st degree and Bachelor-level of the Bologna process in Design E Comunicazione - Torino
1st degree and Bachelor-level of the Bologna process in Electronic And Communications Engineering - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Aerospaziale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Chimica E Alimentare - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Civile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dei Materiali - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Del Cinema E Dei Media Digitali - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Dell'Autoveicolo (Automotive Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Edile - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettrica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Elettronica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Energetica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Fisica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Gestionale - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Meccanica (Mechanical Engineering) - Torino
1st degree and Bachelor-level of the Bologna process in Ingegneria Per L'Ambiente E Il Territorio - Torino
1st degree and Bachelor-level of the Bologna process in Matematica Per L'Ingegneria - Torino
1st degree and Bachelor-level of the Bologna process in Pianificazione Territoriale, Urbanistica E Paesaggistico-Ambientale - Torino

Course structure
Teaching Hours
Lezioni 25,5
Esercitazioni in aula 35,5
Tutoraggio 36
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Quercia Daniele   Professore Ordinario IINF-05/A 13,5 21 0 0 1
Co-lectures
Espandi
Context
SSD CFU Activities Area context
IINF-05/A
PHIL-03/A		 3
3		 D - A scelta dello studente
D - A scelta dello studente		 A scelta dello studente
A scelta dello studente
Statistiche superamento esami
2026/27
Artificial intelligence is changing society at a systemic level, creating risks that propagate across whole economies and institutions rather than affecting individuals in isolation. This course examines these systemic risks of AI. Its main case is the impact of AI on work: how AI reshapes jobs by changing tasks, demanding new skills, and raising the risk of exclusion, job loss, or lower-quality work. It also gives particular attention to a second systemic risk: the concentration of power and the governance questions that arise when AI capabilities are held by a few large actors. Students examine what their own future profession might look like, starting from the tasks their chosen job involves and how AI could change them. Using tools for long-term thinking and human values, they identify the values that matter most in their field and design practical, policy-oriented responses to make change at work fair, inclusive, and socially beneficial. The course concludes with a group project and a final presentation in which students show how their profession could change with AI and justify their choices on ethical, social, and technical grounds.
The course is part of the “Global Challenges” catalogue, designed to offer a learning experience focused on the analysis of complex, cross-cutting issues across different study programmes. Its aim is to provide students with the tools needed to understand and address the major challenges of the present and the future with awareness, responsibility, and a collaborative mindset. The catalogue promotes a broad and integrated perspective by bringing together STEM disciplines — Science, Technology, Engineering and Mathematics — with the humanities and social sciences. The courses address current, interdisciplinary topics and aim to develop innovative technological solutions through a critical approach, with particular attention to ethics and the social, cultural, and environmental impacts of the proposed solutions. Teaching activities foster active and multidisciplinary learning, encouraging the integration of technical, scientific, social, and humanistic competencies. Through team-based project work, students develop design skills, interdisciplinary dialogue, shared responsibility, and the ability to understand, contextualise, and tackle complex problems. The “Global Challenges” catalogue will be launched each year with a lectio magistralis in English dedicated to a highly relevant theme. Artificial intelligence is changing society at a systemic level, creating risks that propagate across whole economies and institutions rather than affecting individuals in isolation. This course examines these systemic risks of AI. Its main case is the impact of AI on work: how AI reshapes jobs by changing tasks, demanding new skills, and raising the risk of exclusion, job loss, or lower-quality work. It also gives particular attention to a second systemic risk: the concentration of power and the governance questions that arise when AI capabilities are held by a few large actors. Students examine what their own future profession might look like, starting from the tasks their chosen job involves and how AI could change them. Using tools for long-term thinking and human values, they identify the values that matter most in their field and design practical, policy-oriented responses to make change at work fair, inclusive, and socially beneficial. The course concludes with a group project and a final presentation in which students show how their profession could change with AI and justify their choices on ethical, social, and technical grounds.
Knowledge and understanding — by the end of the course the student will know and understand: * the notion of systemic risk and the mechanisms by which the effects of AI propagate across economic and institutional systems; * the main effects of AI on occupations: task automation, task transformation, change in required skills, and the risk of exclusion or job loss; * the dynamics of power and resource concentration associated with AI, and the principal governance and policy responses to them; * methods and frameworks for value-sensitive analysis, stakeholder mapping, and assessment of the automatability of tasks. Ability to apply knowledge — the student will be able to: * analyse a profession by decomposing it into tasks and classifying them as automatable, AI-augmentable, or intrinsically human; * apply value-sensitive design and long-term-thinking tools to identify the stakeholders and values at stake in technology-driven occupational change; * evaluate the systemic risks of a given AI application — including labour-market and power-concentration effects — and formulate appropriate governance or policy responses; * design and justify, on ethical, social, and technical grounds, proposals that rethink job roles through the responsible use of AI; * produce and present clear, well-structured written reports and oral arguments addressed to a mixed technical and non-technical audience.
Knowledge and understanding — by the end of the course the student will know and understand: * the notion of systemic risk and the mechanisms by which the effects of AI propagate across economic and institutional systems; * the main effects of AI on occupations: task automation, task transformation, change in required skills, and the risk of exclusion or job loss; * the dynamics of power and resource concentration associated with AI, and the principal governance and policy responses to them; * methods and frameworks for value-sensitive analysis, stakeholder mapping, and assessment of the automatability of tasks. Ability to apply knowledge — the student will be able to: * analyse a profession by decomposing it into tasks and classifying them as automatable, AI-augmentable, or intrinsically human; * apply value-sensitive design and long-term-thinking tools to identify the stakeholders and values at stake in technology-driven occupational change; * evaluate the systemic risks of a given AI application — including labour-market and power-concentration effects — and formulate appropriate governance or policy responses; * design and justify, on ethical, social, and technical grounds, proposals that rethink job roles through the responsible use of AI; * produce and present clear, well-structured written reports and oral arguments addressed to a mixed technical and non-technical audience.
None.
None.
The programme is organised in macro-blocks of lectures and classroom exercises (indicative hours; about 61.5 h of in-class teaching in total, 12 h of which are co-taught): * Foundations and method (≈ 8 h): clear thinking and writing; how to write and present a report; what makes a risk “systemic.” * AI and the future of work (≈ 12 h): how AI is changing work; decomposing a job into tasks; automatable, augmentable, and human tasks. * Power, governance, and inequality (≈ 8 h): concentration of power and resources; the governance of AI; the AI-driven economic divide. * Values and meaningful work (≈ 8 h): what work we value and why; jobs with little meaning (Graeber); value-sensitive mapping tools. * Futures and design (≈ 8 h): planning better futures; designing and arguing design and policy proposals. * Supervised project and Design Court (≈ 17.5 h): team sector/job analysis, vignettes and video, and a final structured debate.
The programme is organised in macro-blocks of lectures and classroom exercises (indicative hours; 60 h of in-class teaching in total, 12 h of which are co-taught): * Foundations and method (≈ 8 h): clear thinking and writing; how to write and present a report; what makes a risk “systemic.” * AI and the future of work (≈ 12 h): how AI is changing work; decomposing a job into tasks; automatable, augmentable, and human tasks. * Power, governance, and inequality (≈ 8 h): concentration of power and resources; the governance of AI; the AI-driven economic divide. * Values and meaningful work (≈ 8 h): what work we value and why; jobs with little meaning (Graeber); value-sensitive mapping tools. * Futures and design (≈ 8 h): planning better futures; designing and arguing design and policy proposals. * Supervised project and Design Court (≈ 17.5 h): team sector/job analysis, vignettes and video, and a final structured debate.
The course is taught in English
The course is taught in English
The course delivers approximately 61.5 hours of in-class teaching, organised as 25.5 hours of lectures (Lezioni) and 36 hours of classroom exercises (Esercitazioni in aula); 12 of these hours are co-taught by the two instructors. No laboratory hours are foreseen. Tutoring (Tutoraggio, about 36 hours) supports the supervised team project. Overall the activity is split as roughly 20% shared with the Grand Challenges program, 30% lectures, and 50% supervised group project work. Student deliverables include two individual short stories/vignettes, an individual analysis of a specific job, a group report on the chosen sector, a group video, and participation in a final structured debate (Design Court). Non-attending students complete an equivalent individual project in place of the group deliverables.
The course delivers 60 hours of in-class teaching, organised as 24 hours of lectures (Lezioni) and 36 hours of classroom exercises (Esercitazioni in aula); 12 of these hours are co-taught by the two instructors. No laboratory hours are foreseen. Tutoring (Tutoraggio, about 36 hours) supports the supervised team project. . Student deliverables include two individual short stories/vignettes, an individual analysis of a specific job, a group report on the chosen sector, a group video, and participation in a final structured debate (Design Court). Non-attending students complete an equivalent individual project in place of the group deliverables.
All materials are in English. The instructors share the main reading list at the start of the course; readings for the shared Grand Challenges section are set by the university. Reference texts (method and tools) * B. Minto, The Pyramid Principle: Logic in Writing and Thinking, Financial Times/Prentice Hall, 1981. * S. Umbrello and O. Gambelin, A Value Sensitive Design Toolkit for Agile Project Management, 2021. * J. Weissman, Presenting to Win, Pearson, 2021. Recommended further reading * N. Primlani et al., Design Courts: Workshops for Exploring Emerging Technology Ethics, ACM CHI 2025. * A. A. Septiandri et al., The Potential Impact of AI Innovations on U.S. Occupations, PNAS Nexus, 2024. * A. A. Septiandri et al., AI and the Economic Divide: How Artificial Intelligence Could Widen the Divide in the U.S., EPJ Data Science, 2025.
All materials are in English. The instructors share the main reading list at the start of the course; readings for the shared Grand Challenges section are set by the university. Reference texts (method and tools) * B. Minto, The Pyramid Principle: Logic in Writing and Thinking, Financial Times/Prentice Hall, 1981. * S. Umbrello and O. Gambelin, A Value Sensitive Design Toolkit for Agile Project Management, 2021. * J. Weissman, Presenting to Win, Pearson, 2021. Recommended further reading * N. Primlani et al., Design Courts: Workshops for Exploring Emerging Technology Ethics, ACM CHI 2025. * A. A. Septiandri et al., The Potential Impact of AI Innovations on U.S. Occupations, PNAS Nexus, 2024. * A. A. Septiandri et al., AI and the Economic Divide: How Artificial Intelligence Could Widen the Divide in the U.S., EPJ Data Science, 2025.
Slides;
Lecture slides;
Modalita di esame: Prova orale facoltativa; Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Optional oral exam; Individual project; Group project;
... Exam format and what it verifies Assessment combines an oral exam with individual and group written/project work; the same modes apply in every examination session. The oral exam (about 20–30 minutes, no materials, manuals, or calculator permitted) verifies the knowledge outcomes — systemic risk, AI and work, power concentration and governance — and the ability to justify the student's analysis. The individual written components (the two vignettes and the examination of a specific job) verify the ability to decompose a profession and analyse AI's task-level impact. The group components (sector report, video, and Design Court) verify the ability to apply value-sensitive analysis, evaluate systemic risks, and communicate results; the project grade also depends on active participation in the team. Final grade — attending students (weighted average) * 15% – Two short personal stories/vignettes (individual). * 30% – Report on AI's impact on the job sector (group). * 15% – Examination of AI's impact on a specific job (individual). * 20% – Video with critical reflections on the sector (group). * 20% – Design Court debate (group). Final grade — non-attending students (weighted average) Non-attending students must inform the instructor at the start of the course and follow an individual track covering the same material, including the systemic-risk themes: * 15% – Two short personal stories/vignettes on a chosen job before and after AI (individual). * 20% – Examination of AI's impact on a specific job (individual). * 40% – Individual written report on a chosen sector, with a dedicated section on power concentration and governance (replaces the group report and video). * 25% – Oral exam on the course readings and the systemic-risks framework (replaces the Design Court).
Gli studenti e le studentesse con disabilita o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unita Special Needs, al fine di permettere al/la docente la declinazione piu idonea in riferimento alla specifica tipologia di esame.
Exam: Optional oral exam; Individual project; Group project;
Exam format and what it verifies Assessment combines an oral exam with individual and group written/project work; the same modes apply in every examination session. The oral exam (about 20–30 minutes, no materials, manuals, or calculator permitted) verifies the knowledge outcomes — systemic risk, AI and work, power concentration and governance — and the ability to justify the student's analysis. The individual written components (the two vignettes and the examination of a specific job) verify the ability to decompose a profession and analyse AI's task-level impact. The group components (sector report, video, and Design Court) verify the ability to apply value-sensitive analysis, evaluate systemic risks, and communicate results; the project grade also depends on active participation in the team. Final grade — attending students (weighted average) * 15% – Two short personal stories/vignettes (individual). * 30% – Report on AI's impact on the job sector (group). * 15% – Examination of AI's impact on a specific job (individual). * 20% – Video with critical reflections on the sector (group). * 20% – Design Court debate (group). Final grade — Students who do not consistently engage in course activities and group work. (weighted average) Non-attending students must inform the instructor at the start of the course and follow an individual track covering the same material, including the systemic-risk themes: * 15% – Two short personal stories/vignettes on a chosen job before and after AI (individual). * 20% – Examination of AI's impact on a specific job (individual). * 40% – Individual written report on a chosen sector, with a dedicated section on power concentration and governance (replaces the group report and video). * 25% – Oral exam on the course readings and the systemic-risks framework (replaces the Design Court).
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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