Societal Sessions at Obertauern 2026
This page outlines the themes and discussion questions for the Societal Sessions at Obertauern 2026, part of the Machine Learning workshop of the German Academic Scholarship Foundation. These sessions explore the social and ethical dimensions of AI and machine learning — regulation, healthcare, misinformation, and the future of work. Each theme is designed as a participant-led discussion with guiding questions and interdisciplinary perspectives.
đź“š Topics Covered
- The EU AI Act – Comparing Regulation in the EU, China, and the US
- AI in Healthcare & Medicine – Ethical and Regulatory Concerns
- AI for Fake News Generation and Detection
- Impact of AI and Automation on the Future of Work
1. The EU AI Act – Comparing Regulation in the EU, China, and the US
The EU Artificial Intelligence Act is the world’s first comprehensive legal framework for AI. It entered into force in August 2024 and has been rolling out in phases: prohibitions on the highest-risk practices (social scoring, real-time biometric surveillance, emotion recognition in workplaces and schools) applied from February 2025; rules for general-purpose AI models from August 2025; and requirements for high-risk systems — in healthcare, employment, education, law enforcement, and critical infrastructure — come into full effect in August 2026. By the time of this school, the Act is no longer a policy proposal but an active regulatory reality that companies are adapting to right now.
How does this approach compare to China’s more state-directed framework or the United States’ sector-by-sector, largely voluntary model? This topic invites analysis of how different regions are shaping AI’s future through regulation. You might explore enforcement structures, cross-border compliance challenges, or how regulatory choices reflect different political and cultural values.
Potential Discussion Points:
- Risk-Based Classification: The AI Act categorises systems as unacceptable, high-risk, limited risk, or minimal risk. What falls into each category, and why? Where are the contested boundaries?
- Prohibited Practices: What is banned outright — and why were those specific uses deemed unacceptable risks rather than just tightly regulated?
- High-Risk Systems: What requirements apply — risk management, human oversight, transparency, data governance, cybersecurity — and how do organisations comply in practice?
- General-Purpose AI: How does the Act approach foundation models and LLMs, which don’t fit neatly into the risk categories designed for narrow applications?
- Business and Innovation Impact: How does the regulation affect companies inside and outside the EU (extraterritorial reach)? What is the cost of compliance, and what are the penalties for non-compliance?
- Global Influence: Is the EU AI Act shaping regulation elsewhere — a “Brussels Effect” for AI? How are the US and China responding?
- Fundamental Rights: How does the Act interact with existing rights (privacy, non-discrimination)? Where might it fall short?
You are strongly encouraged to choose specific angles or case studies you find particularly relevant!
2. AI in Healthcare & Medicine – Ethical and Regulatory Concerns
AI is rapidly transforming healthcare — from clinical diagnostics to drug discovery to hospital logistics. The potential is substantial: faster diagnoses, more personalised treatments, better prediction of patient deterioration. But as algorithms become embedded in medical decision-making, they raise critical questions: How do we ensure patient safety when the model is a black box? Who is responsible when an AI-driven diagnosis is wrong? How do we address bias baked into training data?
This topic invites exploration of how AI is reshaping medicine, and where ethics, regulation, and societal values come into tension with clinical innovation. You could focus on a specific application domain, a regulatory controversy, or a broader question about how healthcare AI should be governed.
Potential Discussion Points:
- Concrete Applications:
- Diagnostics: AI interpreting medical images (radiology, pathology, dermatology) — where is performance now, and where do errors cluster?
- Drug Discovery: AI accelerating molecular screening and clinical trial design.
- Predictive Analytics: Forecasting deterioration, readmissions, or disease outbreaks.
- Surgical Robotics: Precision-assisted procedures.
- Administrative Automation: Scheduling, coding, documentation — lower stakes but high volume.
- The Black Box Problem: How do we build trust in models whose reasoning isn’t transparent? What does “explainability” actually require in a clinical setting?
- Bias and Fairness: Models trained on unrepresentative data amplify existing health disparities. What does equitable AI in healthcare actually require?
- Data Privacy: Patient data is sensitive and regulated (GDPR, national health data laws). How do we train effective models while protecting privacy?
- Accountability and Liability: When an AI recommendation contributes to patient harm, who is responsible — the developer, the hospital, the clinician who followed it?
- Human-AI Collaboration: Will AI replace clinicians or augment them? What happens to diagnostic skills if clinicians increasingly defer to models?
- Regulatory Approval: How do health regulators evaluate AI medical devices? What standards apply, and are they adequate for continuously updated models?
You are strongly encouraged to choose specific angles or case studies you find particularly relevant!
3. AI for Fake News Generation and Detection
Generative AI has dramatically lowered the barrier to producing convincing fake content — fabricated articles, synthetic images, deepfake videos, voice clones. This makes it harder to distinguish authentic from fabricated information, and easier to manufacture persuasive disinformation at scale. Recent election cycles have provided concrete evidence of how these tools are used — and of the limits of both human and automated detection.
This raises profound questions: How do we protect public discourse from automated manipulation? What are the responsibilities of platforms, governments, and individuals? And is the technical arms race between generation and detection winnable?
Potential Discussion Points:
- The Changed Landscape: How has generative AI shifted the economics of disinformation? What does it now cost to produce convincing fake content at scale, compared to five years ago?
- Deepfakes and Synthetic Media: What specific threats do deepfake videos and voice clones pose — to individuals, public figures, elections, and institutional trust?
- The Detection Challenge: What approaches exist for detecting AI-generated content — technical signatures, watermarking, content provenance (e.g., C2PA standards)? How reliable are they, and what is the arms-race dynamic?
- Societal and Psychological Impact: How does widespread synthetic media affect trust in authentic evidence? What are the downstream effects on public discourse and democratic participation?
- Platform and Regulatory Responses: What obligations do platforms have? What does the EU’s Digital Services Act require? Are voluntary commitments sufficient?
- Media Literacy: What can individuals do? How should education systems respond?
- Ethical Tensions: How do we balance free expression with the need to suppress harmful disinformation? Who decides what counts as harmful?
You are strongly encouraged to choose specific angles or case studies you find particularly relevant!
4. Impact of AI and Automation on the Future of Work
AI and automation are restructuring work in ways that are increasingly concrete — no longer hypothetical. Large language models have made routine cognitive tasks (drafting, summarising, translating, coding) significantly faster or cheaper. Robotic automation continues to transform logistics and manufacturing. The central questions are shifting from “will this happen?” to “how do we respond?”
But the picture is not simply one of displacement. Some roles are being augmented rather than replaced; new kinds of work are emerging; and the distributional effects — who benefits and who loses — are uneven across sectors, geographies, and skill levels.
Potential Discussion Points:
- Displacement vs. Creation vs. Transformation: Which types of work are most exposed to automation right now? Which are being augmented? What genuinely new roles are emerging, and who can access them?
- The Cognitive Task Shift: LLMs have extended automation into white-collar and knowledge work in ways earlier waves of automation did not. What does this mean for professions like law, medicine, journalism, and academia?
- Skills and Education: What capabilities become more valuable as AI handles routine tasks — and are educational systems adapting fast enough? What does lifelong learning actually look like in practice?
- Inequality: Are the gains from AI productivity broadly shared, or do they accrue primarily to capital and high-skilled workers? How might automation exacerbate or reshape existing inequalities?
- Policy Responses: What are the arguments for and against policy interventions — retraining programmes, universal basic income, shorter working weeks, stronger labour protections, taxes on automation?
- Workplace Surveillance and Autonomy: How is AI being used to monitor workers — productivity tracking, performance evaluation, algorithmic management? What are the implications for autonomy and dignity at work?
You are strongly encouraged to choose specific angles or case studies you find particularly relevant!