Germany - National Multi-Stakeholder Platform

Overview

Plattform Lernende Systeme (PLS) is Germany’s national, multi‑stakeholder forum for learning systems and artificial intelligence. Initiated by the Federal Ministry of Education and Research (BMBF) and coordinated by acatech, the platform was announced in 2017 and began activities thereafter to bring together experts from academia, business and civil society. PLS aims to shape research agendas, foster transfer between science and industry, and inform public policy through working groups, publications and public events. The platform’s public presence and outputs are maintained via its official site and publications; see the platform’s information pages and acatech project pages for details and downloadable deliverables (e.g. Platform About page, acatech project page).

Definitions

For the purposes of the platform and its outputs, "learning systems" (lernende Systeme) are computational systems that use data and algorithms to adapt behaviour over time; the term covers a wide range of machine learning and AI techniques. "Platform" denotes a convening, advisory and coordinating body rather than a regulatory agency—PLS develops guidance, scenarios, whitepapers and recommendations but has no formal rule‑making authority. "Members" are appointed experts representing diverse sectors; the "Lenkungskreis" is the steering committee that sets strategic priorities. The secretariat/ managing office is the operational unit responsible for coordination, publications and events.

Governance and Institutional Framework

PLS is institutionally anchored through BMBF funding and strategic direction while being coordinated by acatech. The governance structure comprises a Lenkungskreis (steering committee) chaired by a senior BMBF representative together with an acatech co‑chair, thematic working groups that produce the substantive outputs, and a managing office (Geschäftsstelle) located at acatech which handles administration, outreach and publication management. Membership is multi‑stakeholder and appointed to ensure disciplinary breadth; participants include representatives from research institutions (e.g. DFKI, Fraunhofer), industry actors, standardisation bodies, trade unions and civil society. The steering committee defines priorities and approves major deliverables, while working groups operate under defined mandates to research specific domains (e.g. data science, occupational transformation, mobility, health, IT security, ethics). The governance model emphasises openness in publishing reports and organising public conferences; see the platform’s organisational description for current membership and steering committee composition (Platform About page) and acatech’s project pages for institutional context (acatech project page).

Key Focus Areas

PLS focuses on a set of sectoral and cross‑cutting themes: technological enablers and data science, the future of work and human‑machine interaction, IT security/privacy/legal/ethical frameworks, innovation and business models, mobility and intelligent transport systems, health/medical/ care applications, and learning robotics. Cross‑cutting priorities include safety and robustness of learning systems, data protection and privacy compliance, transparency and explainability, societal dialogue and skills development, standardisation and conformity considerations, and alignment with EU regulatory initiatives. The platform develops application scenarios (use cases) to illustrate benefits and risks, issues whitepapers with policy recommendations (for example on competency development, criticality of AI in contexts, and sectoral guidance), and organises conferences and roundtables to disseminate findings. Outputs are targeted at policymakers, industry leaders, researchers and the public and aim to influence national strategies and inform legislative processes such as AI governance at the EU level.

Implementation Framework

Implementation of platform outputs follows a non‑binding advisory model: working groups research and draft position papers, the steering committee endorses strategic recommendations, and the managing office publishes deliverables and organises stakeholder events. There is no statutory enforcement mechanism; instead the platform seeks to influence through evidence‑based recommendations, partnerships with ministries (notably BMBF) and coordination with standardisation bodies (e.g. DIN) and research infrastructures. The platform encourages the adoption of voluntary best practices by industry, recommends governance measures for public procurement and research funding, and supports competency and skills initiatives. Implementation therefore relies on voluntary uptake, government integration of recommendations into policy and funded programmes, and alignment with EU regulatory developments (notably the EU AI Act) and data protection law (GDPR).

Monitoring and Evaluation

Monitoring of platform impact is conducted through publication tracking (number of whitepapers, events and citations), engagement metrics (member participation, public events, media coverage) and direct feedback from ministries and stakeholders who incorporate recommendations into policies or funded projects. The platform periodically reports activities and outputs; acatech and the managing office document event proceedings and maintain a publications repository for transparency. While PLS itself does not perform formal regulatory compliance audits, it contributes technical assessments and risk analyses that can inform regulatory bodies and conformity frameworks.

Penalties, Liability, and Appeals

As an advisory and convening framework, PLS does not establish statutory penalties or enforcement mechanisms. Membership and participation are voluntary and subject to selection or invitation; funding or formal endorsement by government entities (e.g. BMBF) can be altered or withdrawn according to funding rules. Liability for platform outputs lies with contributing authors and institutional sponsors in accordance with standard academic and organisational practices; stakeholders seeking redress in relation to PLS outputs would use normal administrative, contractual or civil remedies against participating entities, not the platform itself.

Relationship to Other Instruments

PLS operates alongside and in support of other national and European instruments: it feeds into the German national AI strategy and works in coordination with ministries, research funding instruments and standardisation initiatives. It explicitly aligns its work with GDPR obligations on data protection and with European regulatory developments such as the EU AI Act. The platform cooperates with research institutes (e.g. DFKI, Fraunhofer), academic bodies, industry associations and standardisation entities to ensure complementarity rather than duplication; its thematic working groups often address issues that are later taken up in legislative, regulatory or standards processes.

International Alignment

PLS emphasises international exchange and alignment: it monitors and engages with EU‑level rulemaking (including the AI Act and EU research programmes), contributes to standardisation dialogues and seeks to position German approaches within international debates on trustworthy AI. The platform’s outputs reference international best practices and standards and encourage German stakeholders to align with global technical and governance norms to ensure interoperability, market access and responsible innovation. For more detail on the platform’s international engagement see acatech project information and recent steering‑committee reports (acatech project page).

Implementation Timeline

Event	Date
Announcement / founding initiated by BMBF	2017-05-16
Platform begins operational activities and working groups convene	2017-09-11
Steering committee and managing office established (acatech)	2017–2018
Ongoing publications, events and annual conferences	2018–present
Recent steering committee meetings and engagement on EU AI Act	2024-10-31; 2025-05-26

Compliance Checklist

Action	Guidance
Membership & participation	Ensure appointment or invitation and active contribution to working groups.
Transparency	Publish and disseminate study methodology, authorship and funding for outputs.
Data protection	Comply with GDPR in all research and case studies; anonymise datasets where required.
Safety & risk assessment	Follow recommended risk classification approaches and scenario analyses published by PLS.
Alignment with standards	Reference relevant DIN/ISO standards and EU guidance in technical recommendations.

Sources and References

Source	Type
Plattform Lernende Systeme — About the Platform	Primary Source
Plattform Lernende Systeme — Founding announcement (news)	Primary Source
acatech — Lernende Systeme / Learning Systems project page (English)	Primary Source
acatech — BMBF founded Plattform Lernende Systeme (press)	Primary Source