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 programmes – 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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