NE_PH_SSU Artificial Intelligence, Society, and Sustainability

NEWTON University
summer 2026
Extent and Intensity
2/0. 4 credit(s). Type of Completion: graded credit.
Teacher(s)
Pablo Maldonado, Ph.D. (lecturer)
Guaranteed by
Pablo Maldonado, Ph.D.
Language Centre – Academic Department – NEWTON University
Timetable
Wed 13:30–15:00 Zoom.Praha5
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives

By the end of the course students will be able to:


  • Analyze the societal, political, and economic implications of AI across global contexts.


  • Evaluate governance frameworks, regulatory models, and ethical principles shaping AI deployment.


  • Identify and critique sources of algorithmic bias, proposing mitigation strategies grounded in fairness and inclusion.


  • Assess the impact of AI on labor markets, organizational strategy, and the future of work.


  • Critically assess the environmental footprint of AI systems and explore pathways toward Green AI.


  • Develop strategic, ethical, and sustainable approaches to AI entrepreneurship and social impact innovation.


  • Synthesize interdisciplinary insights to design governance, policy, or business solutions for real‑world AI challenges.

Learning outcomes

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


  • Compare major global AI governance frameworks and articulate their underlying political and cultural values.


  • Distinguish between algorithmic bias, data bias, and systemic bias in AI systems.


  • Describe how AI technologies influence labor dynamics, automation, and human–machine collaboration.


  • Assess the sustainability impacts—both positive and negative—of AI systems and infrastructures.


  • Collaborate effectively in teams to analyze cases, debate policy options, and deliver applied projects.

Syllabus

Week 1: Introduction to AI and Society


 Overview of AI technologies


 Historical context and societal impact


 Key ethical frameworks


Week 2: AI Governance and Regulation


 Global regulatory efforts (EU AI Act, U.S. frameworks)


 Corporate compliance and risk management


Week 3: Bias, Fairness, and Ethics in AI


 Algorithmic bias and discrimination


 Inclusive design and mitigation strategies


Week 4: AI and the Future of Work


 Automation, job displacement, and reskilling


 Human-AI collaboration models


Week 5: AI and Surveillance


 Privacy concerns and surveillance capitalism


 Case studies: facial recognition, predictive policing


Week 6: AI in Political Influence and Disinformation


 Deepfakes, microtargeting, and lobbying


 Media manipulation and electoral risks


Week 7: AI for Climate Modeling and Disaster Response




 Forecasting, resource allocation, and emergency planning


 Case studies in environmental AI


Week 8: Sustainable Supply Chains Powered by AI


 Logistics optimization and waste reduction


 Transparency and traceability tools


Week 9: Smart Cities and Urban Sustainability


 AI in traffic, energy, and waste management


 Ethical concerns in urban data collection


Week 10: Green AI and Environmental Footprint


 Energy demands of AI models


 Sustainable computing strategies


Week 11: AI in Circular Economy Models


 Lifecycle tracking, recycling, and reuse


 Business models for sustainability


Week 12: AI Entrepreneurship for Social Impact


 Startups solving global challenges with AI


 Funding, scaling, and impact metrics

Assessment methods

Participation (10%), Group Presentation (30%), Individual Presentation


(30%), Final Project (30%)

Language of instruction
English
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