Technological Change and Social Development

 

Course Code:  ECO125 

Term: summer 2026

Dates: July 6 to July 17, 2026

Total duration: 40 in-person hours + 16 hours of study visits

Instructor: Lourdes Daza Aramayo

Instructor contact:  lourdes.aramayo@aauni.edu

Office hours: schedule an appointment via email

 

US/ECTS credits	3/6	Level	Bachelor
Duration	2 weeks	Language	Spanish
Contact hours	40 hours	Course type	Elective

1.    Course description

This introductory course explores the relationship between technological change and social development, examining how innovations have transformed human production, organization, communication, and cooperation from the Cognitive Revolution to contemporary Artificial Intelligence.

Through case studies, data analysis, collaborative activities, digital tools, and emerging technologies, students will investigate how technology shapes prosperity, inequality, innovation, and social transformation. The course integrates historical, economic, social, and technological perspectives to understand both the opportunities and the challenges associated with technological change.

Throughout the program, students will use artificial intelligence tools, analytical platforms, and prototyping environments to develop applied learning evidence, critical reflections, and a final project focused on addressing a relevant social or technological challenge. The course emphasizes critical thinking, evidence-based argumentation, creativity, collaboration, and the ability to assess the social impact of emerging technologies.

By the end of the course, students will have developed a deeper understanding of how technologies evolve, how they diffuse across different contexts, and how they can be used responsibly to contribute to more inclusive, sustainable, and innovative futures.

No prior technical, programming, or engineering background is required. The course is designed for students from business, social science, humanities, and related disciplines. All technological tools and analytical methods used during the course will be introduced at an accessible, introductory level.

2.    Student Learning Outcomes

Upon successful completion of the course, students will be able to:

 

1. Analyze the major technological transformations in human history and their influence on economic, political, and social development.
2. Examine the relationship between technological innovation, prosperity, inequality, and development using historical evidence, case studies, and comparative data.
3. Apply digital tools, data analysis platforms, and artificial intelligence applications to explore contemporary challenges related to technology and society.
4. Critically evaluate the opportunities, risks, biases, and ethical implications associated with automation, algorithmic systems, and emerging technologies.
5. Design innovative solutions or proposals aimed at addressing social, organizational, or economic challenges through the responsible use of technology.
6. Develop research, argumentation, and communication skills to analyze complex technological phenomena from an interdisciplinary perspective.
7. Collaborate effectively in multicultural environments to develop projects, analyze problems, and build evidence-based solutions.
8. Reflect on the potential future impacts of technology and formulate informed perspectives on innovation, sustainability, and social transformation.

3.    Reading Materials

Access to digital texts and multimedia resources provided throughout the course is intended exclusively for academic and personal study purposes.

Required Materials

▪ Assigned readings, articles, case studies, and digital resources for each course session.

▪ Access to institutional learning and academic communication platforms.

▪ A laptop computer with Internet access for in-class activities.

▪ Basic software and tools for research, collaboration, and information analysis (Google Workspace, Microsoft Office, Notion, or equivalent applications).

▪ Access to artificial intelligence tools and digital technologies used throughout the course, including platforms for content generation, analysis, data visualization, and prototyping.

▪ Access to collaborative and digital learning platforms used during the course (such as Miro, Mentimeter, NotebookLM, Google Colab, Lovable, Kumu, or equivalent tools).

Recommended Materials

▪ Supplementary books and readings on technology, innovation, economic development, and social transformation.

▪ Audiovisual resources related to artificial intelligence, automation, technological development, the digital economy, and future studies.

▪ Data analysis and visualization platforms such as Our World in Data, World Bank Open Data, Flourish, or equivalent tools.

▪ Specialized resources on innovation, technology entrepreneurship, AI ethics, and emerging technologies.

▪ Optional design, multimedia creation, and prototyping tools to support course activities and project development.

4.    Teaching methodology and learning activities

The course employs an active and participatory learning approach that combines historical analysis, critical discussion, project-based learning, problem-solving activities, collaborative work, and the use of digital technologies and artificial intelligence.

Learning activities are designed to promote the practical application of concepts, critical thinking, evidence-based argumentation, and reflection on the social impact of technology. Throughout the program, students will develop both individual and team-based learning evidence that connects the theoretical frameworks of the course with contemporary challenges related to innovation, development, and social transformation.

Class sessions include:

▪ Short instructor-led presentations introducing key concepts and theoretical frameworks.

▪ Structured debates and guided discussions on contemporary technological issues.

▪ Analysis of historical and contemporary case studies.

▪ Collaborative research and problem-solving activities.

▪ Data analysis and visualization exercises.

▪ Prototype development and solution design using digital tools and artificial intelligence platforms.

▪ Simulations, technology audits, and critical evaluation exercises.

▪ Individual reflections contributing to the Technology Thinking Portfolio.

▪ Team presentations and peer feedback activities.

Independent work includes completing assigned readings and resources, developing assessed learning activities, preparing the Technology Thinking Portfolio, advancing the final project, and preparing presentations and project deliverables.

The course methodology emphasizes active participation, intercultural collaboration, creativity, responsible innovation, and the ability to apply knowledge to real-world contexts.

5.     Course content

Week 1 – Technology, Development, and Social Transformation

Unit 1. Critical Thinking and Historical Transformations

1.     Critical Thinking and Argumentation.

2.     The Cognitive Revolution.

3.     The Agricultural Revolution.

4.     Social Unification and Large-Scale Cooperation.

5.     Technology as a Driver of Social Transformation.

Learning Evidence:

·       Infographic: The Superpower That Transformed Humanity.

·       Agritech Prototype for Contemporary Agricultural Challenges.

Unit 2. Technology, Prosperity, and Development

1.     Regional Differences and Innovation Ecosystems.

2.     Technological Diffusion and Economic Development.

3.     Prosperity, Poverty, and Technological Inequality.

4.     Institutions, Innovation, and Growth.

5.     Comparative Analysis of Technological Trajectories.

Learning Evidence:

·       Innovation Ecosystems Analysis.

·       Evidence-Based Mini Data Story.

Unit 3. Automation, Artificial Intelligence, and Algorithms

1.     Intelligent Automation and Organizational Transformation.

2.     Business Process Management and Responsible Automation.

3.     Classification Algorithms and Bias.

4.     Machine Learning and Algorithm Auditing.

5.     Neural Networks and Autonomous Systems.

Learning Evidence:

·       Responsible Automation Proposal.

·       Algorithmic Bias Audit.

·       Autonomous Systems Analysis.

Week 2 – Technology, Futures, and Social Transformation

Unit 4. Emerging Technologies and Foresight Thinking

1.     Emerging Technologies and the Gartner Hype Cycle.

2.     Innovation, Expectations, and Technology Adoption.

3.     Generative Artificial Intelligence.

4.     Digital Infrastructure and Technologies for Development.

5.     Technological Foresight and Future Scenarios.

Learning Evidence:

·       Hype Cycle Analysis.

·       Emerging Technologies Presentation.

Unit 5. The Nature of Technology and Possible Futures

1.     Formal Definitions of Technology.

2.     Technological Evolution and Social Coevolution.

3.     Technology, Ethics, and Responsibility.

4.     Technological Futures and Social Transformation.

5.     The World in 2075.

Learning Evidence:

·       Technology Thinking Portfolio.

·       “The World in 2075” Foresight Exercise.

·       Final Project.

 

Pedagogical Logic of the Course

The course content is structured to develop a progressive and integrated understanding of the relationship between technology and social development. The learning journey begins with the study of the major technological transformations in human history and their impact on the organization of societies. It then examines innovation, prosperity, inequality, and technological diffusion before moving toward contemporary challenges associated with artificial intelligence, automation, and intelligent systems. The course subsequently introduces conceptual and philosophical perspectives on the nature of technology, its mechanisms of evolution, and its social, political, and ethical implications. Finally, students explore real-world applications and contemporary case studies, culminating in an integrative final project that allows them to apply course concepts to the analysis of current challenges and the design of technology-based solutions with social impact.

6.    Learning activities led by an instructor

 

Under the guidance of the instructor, students will participate in activities designed to integrate historical, technological, economic, and social perspectives while promoting the practical application of course concepts and the development of analytical, communication, and collaborative skills.

 

Learning activities include:

 

▪ Instructor-led presentations and guided discussions on major processes of technological change and social development.

▪ Structured debates and argumentation exercises focused on innovation, artificial intelligence, automation, and technological futures.

▪ Analysis of historical and contemporary case studies related to prosperity, inequality, organizational transformation, and technology adoption.

▪ Collaborative research activities using academic sources, data repositories, and digital tools.

▪ Data analysis and visualization exercises exploring the relationship between technology, development, and social well-being.

▪ Design and evaluation of prototypes, technological solutions, and innovation proposals using digital tools and artificial intelligence platforms.

▪ Critical audits of algorithms, automated systems, and emerging technologies to identify risks, biases, and social consequences.

▪ Foresight activities and future-scenario exercises focused on the societal impact of emerging technologies.

▪ Individual reflective activities contributing to the Technology Thinking Portfolio.

▪ Progressive development of the final project through planning, research, prototyping, feedback, and presentation activities.

These activities are intended to strengthen critical thinking, effective communication, intercultural collaboration, and the responsible application of knowledge in real-world contexts.

 

7.    Study Visits

Study visits provide students with the opportunity to observe how technological change, innovation, and digital transformation influence economic development, public policy, organizational performance, and social well-being in a country with a strong scientific and technological ecosystem such as the Czech Republic.

These visits complement the theoretical and practical components of the course by exposing students to real-world examples of technological innovation, research, entrepreneurship, and applied problem-solving. Students will be encouraged to connect insights gained during the visits with course concepts, learning activities, portfolio reflections, and the final project.

Depending on availability, students will participate in two to three academic visits selected from the following:

1. Innovation and Research Center (Prague Innovation Institute, CIIRC CTU, or equivalent)

Students will explore projects related to artificial intelligence, robotics, automation, digital transformation, and applied innovation.

Learning objectives:

▪ Understand how technological innovation is developed and transferred into practice.

▪ Analyze the relationship between research, entrepreneurship, and social development.

▪ Examine the role of innovation ecosystems in economic competitiveness and technological advancement.

 

2. National Technical Museum Prague

Students will examine the historical evolution of technology and its impact on industrial, economic, and social development.

Learning objectives:

▪ Analyze major technological transformations throughout history.

▪ Identify connections between historical innovations and contemporary technological challenges.

▪ Reflect on the relationship between technological progress and social change.

 

3. Technology Company, Startup, Innovation Hub, or University Laboratory

Students will observe innovation processes and engage with professionals working in technology-intensive environments.

Learning objectives:

▪ Explore contemporary applications of emerging technologies.

▪ Understand organizational challenges related to digital transformation and innovation management.

▪ Evaluate the social, economic, and ethical implications of technological development.

 

Reflection and Integration

Students may be asked to integrate insights from study visits into class discussions, learning activities, the Technology Thinking Portfolio, and the Final Project. The objective is to encourage students to connect theoretical concepts with practical examples and critically evaluate the role of technology in addressing contemporary societal challenges.

 

8.    Course Requirements and Assessment (with estimated workload)

 

Note: The estimated student workload reflects the total time commitment expected throughout the course and includes contact hours, study visits, instructor-led activities, collaborative work, preparation of assignments, development of the Technology Thinking Portfolio, completion of the Final Project, and independent study.

*1 = Critical Thinking; 2 = Effective Communication; 3 = Effective and Responsible Action

 

8.1.Evaluation System

Component	%	What Is Assessed	Assessment Tool
Academic Participation and Class Contribution	10%	Active participation, quality of contributions, ability to formulate questions, contribution to debates and discussions	Participation Rubric
Learning Activities (Classes 1–11)	44%	Application of concepts, critical thinking, data analysis, creativity, use of digital tools, and completion of deliverables	Activity-Specific Rubrics
Technology Thinking Portfolio (4 Reflections)	16%	Development of critical thinking, analytical ability, argumentation, personal reflection, and integration of course themes	Critical Reflection and Academic Writing Rubric
Final Project – Proposal (Brief)	5%	Effective communication, synthesis, and critical analysis	Critical Reflection and Academic Writing Rubric
Final Project – Presentation	10%	Effective communication, synthesis, critical analysis, use of evidence, and response to questions	Oral Presentation Rubric
Final Project – Digital Product	10%	Digital literacy, creativity, conceptual rigor, technical quality, and applicability of the proposal	Digital Product Rubric
Peer Evaluation	5%	Collaboration, individual responsibility, contribution to group work, and quality of feedback	Structured Peer Evaluation Form
TOTAL	100%

 

 

8.2.Assessment Rubric

All assignments are evaluated using the following rubric. The rubric is available from the first day of the course; there are no hidden criteria.

 

8.3.Distribution of Weights by Deliverable

 

 

8.4.Description of the Assessment System

The assessment system is designed to evaluate continuous learning, active participation, critical thinking, and the practical application of course concepts. Rather than relying on a traditional final examination, the course uses a distributed assessment model that recognizes contributions made throughout the program.

 

1. Academic Participation and Class Contribution (10%)

 

Students will be assessed on the quality of their participation throughout the course, including:

• Contributions to debates and discussions.
• Ability to formulate relevant questions.
• Participation in collaborative activities.
• Evidence-based interventions grounded in course concepts.
• Respectful and constructive interaction with classmates and guest speakers.

 

2. Applied Learning Activities (44%)

 

Applied activities form the core of the course and provide opportunities to apply concepts to real-world situations and contemporary technologies.

 

 

3. Technology Thinking Portfolio (16%)

Throughout the course, each student will develop a personal reflection portfolio documenting the evolution of their thinking about technology, social development, inequality, automation, and artificial intelligence.

 

The portfolio will consist of four individual critical reflections (maximum one page each):

 

 

Reflection	Associated Session	Course Themes Inspiring the Reflection	Critical Question
Reflection 1	Class 12 – Formal Definitions of Technology	Cognitive Revolution, Agricultural Revolution, language, cooperation, shared narratives, and technology as a social phenomenon.	Are human beings fundamentally toolmakers, or are there other technologies that better explain the development of civilization?
Reflection 2	Class 13 – Mechanisms of Technological Evolution	Innovation ecosystems, prosperity and technological diffusion, institutions, infrastructure, and relationships among people, organizations, and technological artifacts.	If our actions constantly depend on tools, infrastructures, and technical systems, what does it mean to separate the human from the technological?
Reflection 3	Class 14 – Engineering, Solutions, and Technological Coevolution	Intelligent automation, BPM, algorithms, bias, artificial intelligence, autonomous systems, and technological governance.	Although technologies circulate globally, to what extent do they reflect the political, economic, and cultural interests of the countries that develop them?
Reflection 4	Class 15 – Society and Technology: Future Projections	Hype Cycle, emerging technologies, technological foresight, the future of work, technological risks, and the social impact of innovation.	Between technological optimism and technological pessimism, is it possible to adopt a genuinely critical and balanced position?

 

The four reflections are developed throughout Classes 12–15 and are submitted according to the deadlines specified in the study guide. Each reflection corresponds to a critical question connected to the content of the respective session and has a maximum length of one page. Together, the reflections document the evolution of the student’s thinking about the relationship between technology, innovation, and social development.

 

4. Final Project – Technology in Context (30%)

The final project integrates the knowledge acquired throughout the course through the analysis of an emerging technology and its social, economic, and cultural implications.

The Final Project is not an informational presentation. It is an exercise in critical intervention. Teams must identify a real-world problem and propose a technological solution that simultaneously addresses three dimensions:

• Technical effectiveness
• Social and ethical viability
• Sustainability and inclusion

All projects must explicitly analyze how the selected technology affects a Global South community differently from a Global North community. Projects that fail to include this analysis will receive a one-level penalty in the Critical Thinking rubric.

 

      I.         Problem Brief (5%)

Definition of the problem, context, and justification for the selected topic.

Maximum length: one page.

    II.         Digital Product (10%)

Development of a digital resource, prototype, visualization, or tool that communicates and applies course concepts.

Examples include:

• Functional prototype using Lovable
• Trained model using Teachable Machine
• Interactive dashboard in Flourish or a similar platform
• 3–5 minute mini-documentary
• Website presenting a public policy proposal

  III.         Final Presentation (10%)

Oral presentation of project results, including critical analysis, evidence, and discussion of impacts.

Format:

• 5-minute presentation
• 5-minute Q&A session

The presentation must include:

• Problem and community context
• Technology functionality
• Economic and social impact
• Risks and challenges
• Perspective from at least one Global South country
• Future scenarios

  IV.         Peer Evaluation (5%)

Individual assessment of each team member’s contribution. Students evaluate the actual contribution of their peers using a structured evaluation form. Evaluations are anonymous but supervised by the instructor.

Project Milestones

July 10: Problem Brief (1 page). Must describe the problem, affected community, proposed solution, and the three project dimensions. Instructor approval is required to proceed.

July 15: Prototype or Minimum Viable Product with design justification. Written feedback will be provided.

July 17: Final class presentation (5 minutes + 5 minutes Q&A).

10 Innovative Project Ideas for Inspiration

 

 

9.     General requirements and school policies

General Requirements

All courses are governed by AAU’s academic regulations. Students are expected to be familiar with the academic regulations outlined in the Academic Code and the Student Handbook and to uphold the highest standards of honesty and academic integrity in their work.

 

Electronic communication and assignment submission

The university and instructors will only use students’ university email addresses to communicate, and additional communication will take place via NEO LMS or Microsoft Teams. Students who email an instructor must clearly indicate the course code and subject in the subject line: for example, “COM101-1 Midterm Exam. Question.”

All electronic submissions must be made through NEO LMS. Substantial written work (especially exams and take-home essays) may not be submitted outside of NEO LMS.

 

Attendance

To pass the course, in-person attendance at classes and study visits is mandatory. The maximum number of absences is 3 classes, and one class is equivalent to a 90-minute session during the scheduled time. Failure to participate in a study visit counts as 2 absences.

 

Attendance may be checked at any time.

 

Students may also be marked absent if they miss a significant portion of a class (by arriving late or leaving early).

 

Justified absences and make-up classes

If a student must miss classes for valid reasons (illness, serious family matters) and wishes to request that the absence be excused, they must submit an Absence Excuse Request Form, accompanied by supporting documents indicating the reasons for the absence, to the program director via the coordinator within one week of the absence. The medical certificate must correspond to the day of the absence; certificates dated later will not be accepted. It is recommended to inform the instructor and the coordinator of the absence in advance, if possible.

 

Students whose absence has been excused by the program director are entitled to make up assignments and exams, provided the nature of the work permits it. Missed assignments due to unexcused absences may result in a lower grade or a failing grade as specified in the curriculum.

 

Students are responsible for contacting their instructor within one week of the date their absence was excused to arrange for make-up options.

 

Late assignments

Late submissions will generally not be accepted after the established deadlines. However, students with officially excused absences or documented exceptional circumstances may request an extension. Supporting documentation may be required in accordance with AAU policies. Students are responsible for contacting the instructor as soon as possible to arrange alternative deadlines when applicable.

 

Electronic devices

The use of laptops, tablets, and mobile devices will be an integral part of the course’s learning activities. Students are expected to use these devices solely for academic purposes and for participation in scheduled class activities.

 

Food and drinks during classes: Eating during class time is strictly prohibited. Students are only allowed to drink water.

 

Cheating and disruptive behavior

If a student exhibits disruptive behavior or other conduct inappropriate for a classroom setting at an educational institution, the instructor must require the student to leave the room for the duration of the activity or for the remainder of the day. Any instances of academic dishonesty will be reported to the student’s home university.

 

Students who engage in behavior suggesting cheating (such as whispering or passing notes) will receive a warning. If the student continues the misconduct, they will be removed from the exam, and the exam will receive a grade of 0 points and a report of Academic Dishonesty.

 

Plagiarism and the Academic Tutoring Center

Plagiarism is “the unauthorized use or close imitation of another author’s language and thoughts and the presentation of them as one’s own original work.” (Random House Comprehensive Dictionary, Second Edition, Random House, New York, 1993).

The Turnitin white paper, The Plagiarism Spectrum (available at http://go.turnitin.com/paper/plagiarism-spectrum), identifies 10 types of plagiarism ranked from most to least severe:

 

1. CLONE: The act of presenting someone else’s work, word for word, as one’s own.

2. CTRL-C: A written piece that contains significant portions of text from a single source without modification.

3. FIND-REPLACE: The act of changing keywords and phrases while preserving the essential content of the source in an article.

4. REMIX: The act of paraphrasing from other sources and making the content fit seamlessly.

5. RECYCLE: The act of borrowing generously from one’s own previous work without citing it; self-plagiarism.

6. HYBRID: The act of combining perfectly cited sources with copied passages (without citation) in a single article.

7. MASHUP: An article that is a mix of material copied from several different sources without proper citation.

8. ERROR 404: An article that includes citations to nonexistent or inaccurate information about the sources.

9. AGGREGATOR: Proper citations are included, but the article contains almost no original work.

10. RETWEET: This article includes proper citations but relies too heavily on the original wording and/or structure of the text.

 

At a minimum, plagiarism of types 1 through 8 will result in a failing grade for the assignment and will be reported to the program director and the student’s home university. The director may initiate disciplinary proceedings in accordance with the Academic Code. Allegations of purchased papers and intentional or repeated plagiarism always result in a disciplinary hearing and may lead to the student’s expulsion.

Artificial intelligence use

The course encourages the critical, ethical, and responsible use of artificial intelligence tools to support learning, research, creativity, analysis, and project development.

 

Students may use AI tools to generate ideas, analyze information, create prototypes, visualize data, support research activities, and improve communication of their work. However, all submitted assignments must demonstrate the student’s own understanding, critical judgment, and intellectual contribution.

 

Students are responsible for verifying the accuracy, quality, and relevance of any content generated through artificial intelligence. The use of inaccurate, misleading, or unverified AI-generated information does not exempt students from academic responsibility.

 

When appropriate, students may be required to disclose the use of AI tools as part of their work process, following the instructor’s guidelines.

 

Misuse of artificial intelligence tools, including the submission of AI-generated content as original work without critical review, proper acknowledgment, or substantial intellectual contribution, may negatively affect the grade of the corresponding assignment and may constitute a violation of the university’s academic integrity policies.

 

10.  Grading scale

Letter grade	Percentage*	Description
A	95–100	Excellent performance. The student has demonstrated originality and an exceptional grasp of the material, as well as a deep analytical understanding of the subject.
A–	90–94
B+	87–89	Good performance. The student has mastered the material, understands the subject well, and has demonstrated some originality of thought and considerable effort.
B	83–86
B–	80–82
C+	77–79	Fair performance. The student has acquired an acceptable understanding of the material and the essential content of the course but has not managed to translate this understanding into consistently creative or original work.
C	73–76
C–	70–72
D+	65–69	Insufficient. The student has demonstrated some understanding of the material and subject matter covered during the course. However, the student’s work has not demonstrated sufficient effort or understanding to warrant a passing grade in the school’s required courses. It qualifies as a passing grade for general and elective college courses.
D	60–64
F	0–59	Fail. The student has not mastered the subject matter covered in the course.

* Decimals should be rounded to the nearest whole number.

 

11. Recommended Resources by Week

Week 1

Readings

•        Harari, Y.N. — Sapiens (selected excerpts)  https://www.ynharari.com/book/sapiens-2/

•        Mazzucato, M. — The Entrepreneurial State, Chapter 1  https://marianamazzucato.com/books/the-entrepreneurial-state/

•        Winner, L. — 'Do Artifacts Have Politics?' (1980, 12 pp.) https://www.jstor.org/stable/20024652

•        Acemoglu & Robinson — Why Nations Fail, Chapter 1 https://whynationsfail.com

TED Talks and Videos

•        Hans Rosling — 'The best stats you've ever seen' (20 min) — Mandatory Class 6 https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen

•        Yuval Harari — 'What explains the rise of humans' (17 min) https://www.ted.com/talks/yuval_noah_harari_what_explains_the_rise_of_humans

•        Chimamanda Ngozi Adichie — 'The danger of a single story' (19 min) https://www.ted.com/talks/chimamanda_ngozi_adichie_the_danger_of_a_single_story

Podcasts

•        'Throughline' — NPR (historical episodes paired with contemporary analysis) https://www.npr.org/podcasts/510333/throughline

•        'Cautionary Tales' — Tim Harford (Technological Failures and Systemic Lessons) https://timharford.com/articles/cautionarytales/

•        '99% Invisible' — Episodes on How Design and Technology Shape Everyday Life  https://99percentinvisible.org

 

 

 

Documentaries

·       “General Magic” (2018) — The Failure That Predicted the Smartphone 15 Years Ahead of Its Time   https://www.generalmagicthemovie.com

•        'AlphaGo' (2017) —AlphaGo: The AI That Defeated the World Go Champion  https://www.alphagomovie.com

11.2 Week 2

Readings

•        Frey & Osborne — 'The Future of Employment' (Oxford, 2013) https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

•        Crawford, K. — Atlas of AI (Introduction) https://www.katecrawford.net

•        Russell, S. — Human Compatible, Chapter 1  https://people.eecs.berkeley.edu/~russell/hc.html

•        Gray & Suri — 'Ghost Work' (Introduction) https://ghostwork.info

TED Talks and Videos

•        Joy Buolamwini — 'How I'm fighting bias in algorithms' (9 min) — Mandatory Class 8  https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms

•        David Autor — 'How automation is changing work' (12 min) https://www.ted.com/talks/david_autor_why_are_there_still_so_many_jobs

•        Kai-Fu Lee — 'How AI can save our humanity' (15 min) https://www.ted.com/talks/kai_fu_lee_how_ai_can_save_our_humanity

•        Patrick Lin — 'The moral dilemmas of driverless cars' (15 min) https://www.ted.com/talks/patrick_lin_the_ethical_dilemma_of_self_driving_cars

Podcasts

•        'Hard Fork' — New York Times (Contemporary Technology with Insight and Humor) https://www.nytimes.com/column/hard-fork

•        'The AI Podcast' — NVIDIA (real applications, without hype) https://blogs.nvidia.com/ai-podcast/

•        'Lex Fridman Podcast' — Episodes with Machine Learning Researchers https://lexfridman.com/podcast/

Documentaries

•        'Coded Bias' (Netflix, 2020) — A Rigorous and Emotionally Compelling Look at Algorithmic Bias  https://www.codedbias.com

•        'The Social Dilemma' (Netflix, 2020) — How Persuasive Design Shapes Democracy  https://www.thesocialdilemma.com

•        'iHuman' (2019) — European Perspectives on AI and Society  https://www.nfi.no/en/films/ihuman

Data Resources and Tools

•        Our World in Data — Global Data with Ready-Made Visualizations  https://ourworldindata.org

•        World Bank Open Data https://data.worldbank.org

•        AI Index Report — Stanford HAI  https://hai.stanford.edu/research/ai-index

•        Global Innovation Index — WIPO  https://www.globalinnovationindex.org

•        Kaggle — public datasets for ML https://www.kaggle.com/datasets

•        Flourish — No-Code Data Visualization https://flourish.studio

•        MIT Moral Machine — ethical dilemmas of autonomous vehicles  https://www.moralmachine.net

•        AI Fairness 360 — IBM https://aif360.mybluemix.net

 

 

Document prepared by: Lourdes Daza Aramayo

Prague, May 5, 2026