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.
Centre for International Programmes – International Development – 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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