Poland - National AI Strategy (2018)

Overview

The document titled Założenia do strategii AI w Polsce. Plan działań Ministerstwa Cyfryzacji (November 2018) is a strategic policy paper prepared and published by the Polish Ministry of Digital Affairs. It presents a diagnosis of national strengths and gaps in AI readiness, proposes guiding principles and a governance approach, and sets out a two-year plan of activities (2018–2019) intended to catalyze research, skills, public data use, pilot deployments and legal-ethical analysis. The paper was publicly presented at the national conference 'Sztuczna Inteligencja — Polska 2118' on 9 November 2018 and subsequently made available via the ministry’s portal. While not legally binding, the assumptions aim to mobilize government ministries, research institutions and industry through concrete projects, coordination mechanisms and alignment with EU and OECD AI initiatives.

Definitions

Key definitions in the assumptions reflect contemporary policy usage: “artificial intelligence (AI)” is framed broadly as techniques and systems that enable automated decision-making, learning from data and pattern recognition across domains; "data economy" describes economic activity built on collection, processing and exchange of digital data; "pilot projects" are time-limited public‑sector or cross-sector deployments intended to test technical, governance and legal solutions; "trustworthy AI" denotes technology developed and used in ways that respect human rights, privacy (GDPR), fairness, explainability, safety and security. The paper also uses terms such as "open data," "public data repositories," and "model robustness," establishing a practical vocabulary for subsequent program design.

Governance and Institutional Framework

The assumptions propose a governance model anchored in the Ministry of Digital Affairs (Ministerstwo Cyfryzacji) acting as the primary coordinator and secretariat for national AI work, supported by temporary and ongoing inter-ministerial working groups, expert panels, and public–private partnerships. It recommends defining clear roles and responsibilities across ministries (science and higher education, health, labor, economy/industry, investment) and establishing mechanisms for stakeholder engagement with academia, civil society and industry. The proposal calls for institutional linkages with national research funding bodies and for the creation of a small standing coordination unit inside the ministry to track pilots, gather indicators, administer seed funding and liaise with EU-level structures such as the European Commission AI policy initiatives. The governance approach emphasizes transparency, regular reporting and the use of public procurement and government data sharing as levers to encourage responsible AI development.

Key Focus Areas

The assumptions identify four parallel planes for action: (1) programmatic — creating funding instruments and public procurement pathways that accelerate AI adoption in priority sectors; (2) educational — expanding tertiary curricula, vocational training and lifelong retraining to increase the pool of specialists and domain-experts who can use AI responsibly; (3) project-driven — launching demonstrator and pilot projects in areas where Poland has comparative advantage (industry/manufacturing, energy, healthcare, public services, finance) to generate reference implementations and reusable data sets; and (4) structural — legal and ethical preparedness, including analysis of GDPR implications, sectoral regulatory adjustments and ethics-by-design approaches. The document prioritizes enabling infrastructure: computational resources, secure data repositories, and APIs for re‑use of public sector datasets. It recommends direct support for R&D through co-funded grants, tax incentives or public procurement, and encourages creation of innovation hubs, cooperation platforms and partnerships between universities and industry. The assumptions also stress cross-cutting requirements: cybersecurity and model robustness, auditability and documentation standards, impact assessment for socio-economic effects, and active communication to build public trust.

Implementation Framework

Implementation is presented as a staged program: short-term (2018–2019) operational actions to launch pilots, set up working groups, publish reference datasets and design funding instruments; medium-term activities to scale successful pilots, integrate AI tools into public administration services and finalize legal/ethical recommendations; and long-term objectives to build a sustainable AI ecosystem with strong R&D and workforce capacity. The Plan of Activities assigns coordinating responsibility to the Ministry of Digital Affairs, recommends memoranda of cooperation with the Ministry of Science and Higher Education and the Ministry of Entrepreneurship/Industry, and outlines the need for monitoring indicators (e.g., number of pilots, datasets published, new curricula, private investment leveraged). The framework foresees the use of public procurement as a policy instrument, and proposes templates for pilot agreements, data-sharing terms and ethical review checklists to be trialed in demonstrators.

Monitoring and Evaluation

Monitoring is treated as essential: the assumptions call for a set of measurable indicators and regular public reporting on progress against the 2018–2019 plan. Metrics proposed include quantity and quality of released public datasets, number and sectoral distribution of pilots, levels of public and private R&D funding mobilized, numbers of trained specialists and retrained workers, and qualitative assessments of legal/ethical risks identified and mitigated. The document recommends periodic independent reviews and the use of pilot evaluations to refine procurement and governance instruments. It also proposes publishing lessons learned to facilitate replication and to inform the later, binding national AI strategy.

Penalties, Liability, and Appeals

As a strategic assumptions document, it does not create new administrative penalties or criminal sanctions. Instead, it positions enforcement and liability within existing legal frameworks: data protection obligations under the EU General Data Protection Regulation (GDPR), sectoral regulatory regimes (healthcare, finance, transport), consumer protection and general civil liability rules. The assumptions recommend that pilots and public procurements embed contractual liability clauses, audits and appeal mechanisms where appropriate, and that public bodies ensure GDPR-compliant processing. Any future binding regulatory measures would need to be drafted and enacted under the ordinary legislative process.

Relationship to Other Instruments

The assumptions explicitly link to the EU-level workstreams and recommendations: they were prepared in the context of the European Commission’s strategic communications and the 2018–2019 Coordinated Plan on Artificial Intelligence and reference OECD deliberations on AI principles. They are presented as a preparatory step — a foundation for a full national AI strategy and for inter-ministerial memoranda such as the 2019 cooperation instruments that followed. The paper therefore functions as an implementing/operational bridge between EU guidance and future national legislation and sectoral regulation.

International Alignment

International alignment is emphasized as a core objective. The assumptions call for Poland to synchronize national priorities with the EU Coordinated Plan and OECD principles, to participate in cross-border data initiatives and to pursue bilateral cooperation with EU and regional partners (including V4 member states). The document recommends Poland advocate for interoperable standards, participate in EU pilot schemes and seek funding under EU research and innovation programs. Alignment is framed not only as regulatory harmonization but also as a competitive strategy to attract investment and ensure Polish solutions can enter wider European markets.

Implementation Timeline

Sources and References