By Veronica Cretu

Data Governance and AI Capacity Building in the context of securing meaningful commitments on transparency, participation and redress – taking stock off the discussions which took place during the Design Lab on Data and AI Governance, September 4 th 2023, in Tallinn, organized by Connected by Data team, lead by Tim Davies.

Training and capacity building play a pivotal role in equipping government bodies and civil society with the necessary knowledge and skills to effectively govern data and artificial intelligence (AI). Governments, particularly those which are members of the Open Government Partnership (OGP) initiative, might explore ways to address the capacity building component as part of their national commitments on Open Government. In an increasingly data-driven world, public servants need the knowledge and skills to harness data and AI tools effectively. Capacity building equips them to make data-informed decisions, leading to more efficient and evidence-based policy formulation and implementation.

Why capacity building on the emerging issues of data and AI governance is crucial for public servants?

- First and foremost, the implementation of such programs plays a pivotal role in enhancing the delivery of public services. Training initiatives provide invaluable support to government reformers by equipping them with the knowledge needed to comprehend and optimize resource allocation, customize services, and pinpoint areas for enhancement. This, in turn, leads to improved service outcomes.

Furthermore, the power of data-driven decision-making becomes evident as it uncovers opportunities for cost savings by detecting inefficiencies, fraud, and errors, thereby reducing wasteful expenditure. Beyond this, training in data governance and AI serves to champion ethical and transparent data utilization, upholding the integrity of data and nurturing trust among citizens and stakeholders.

- Secondly, with the integration of AI capabilities, public servants can spearhead the development of innovative policies and programs tailored to address the intricate challenges spanning various sectors, including healthcare, education, agriculture, social protection, and more. The adoption of technological solutions has witnessed varying degrees of enthusiasm across sectors, and it is essential to recognize how data fosters progress in each of these domains. Moreover, these programs prioritize cybersecurity and data protection, thereby mitigating the risks associated with data breaches and privacy infringements.

- Thirdly, capacity building on these emerging issues empowers public servants to align national objectives related to data-driven governance and AI adoption with evolving agendas such as the Sustainable Development Goals (SDGs) and legislation governing AI. This alignment encourages collaboration not only among different government departments but also between the government and civil society, government and the private sector, government and academia, and thus, supports in creating an environment conducive to holistic problem-solving.

Nations that invest in such capacity building initiatives gain a competitive edge, attracting talent, fostering innovation, and positioning themselves as global leaders in emerging technologies. This proactive engagement in global discussions on AI not only advances their own agendas but also contributes to shaping global AI governance and ethical standards, reinforcing their leadership role in the international community. The availability of capacity building programs on these issues equips public servants and diplomats with the knowledge and skills needed to negotiate on equal footing in the realm of AI governance.

In order to ensure the successful implementation of data governance and AI capacity building initiatives Governments can implement the Capacity Building component by embedding commitments as part of their National Action Plans on Open Government, and accordingly, can adopt a multifaceted approach that encompasses both in-service and pre- service training programs.

Pre-Service Training Component: Pre-service training refers to education and skill development programs designed to prepare individuals for careers in the publi sector, specifically focusing on data and AI governance.

Below are a few examples of the type of commitments that might be considered:

Reviewing the existent curriculum and adjusting it to the emerging developments in the field: Collaborating with educational institutions to integrate data governance and AI courses into existing academic programs. Bringing together experts from various fields, including computer science, ethics, law, policy, business, and social sciences, to review and develop the new curriculums, ensuring were possible, the interdisciplinary approach and a holistic understanding of data governance and AI. More than that, the programs should provide both foundational knowledge on aspects such as data governance, data ethics, data privacy, data management, and AI fundamentals to build a strong knowledge base. At the same time, advanced courses for in-depth exploration should be available, including on topics like advanced data analytics, deep learning, AI project management, language technologies or computational linguistics, data protection laws (e.g., GDPR, CCPA) and regulations relevant to data governance, AI, and privacy. In addition to these, incorporation of soft skills development, including teamwork, communication, and project management into data and AI programs, as they are crucial for effective implementation of data governance and AI initiatives.

Monitoring, Evaluation and Learning (MEL): is a critical component of government efforts to enhance capacity building in the areas of data governance and AI. Establishing a robust Monitoring, Evaluation, and Learning framework to assess the impact and effectiveness of capacity building programs is another example of a data and AI Governance. This framework should include key performance indicators (KPIs) to measure the success of educational initiatives, such as the number of students enrolled, completion rates, and post-graduation employment in relevant fields. Regular evaluations and feedback mechanisms should be in place to identify areas for improvement and adaptation.

Another important element of the Monitoring, Evaluation, and Learning (MEL) framework lies in the context of government-sponsored scholarships for studying data and AI abroad, particularly as part of bilateral agreements, and the need to track and analyze specific outcomes related to students; educational journeys and subsequent contributions to their home country.

The main reasons for doing so are (particularly across countries with high brain drain and migration phenomena):

Scholarships are often designed to align with these objectives by sending students to acquire specialized knowledge and skills. MEL helps ensure that the objectives are being met and that the investments in education are contributing to the nations' goals. By tracking how many students have completed studies in data and AI, the government gains insights into the pool of talent available in these critical areas. This data can inform policy decisions, such as funding allocations, curriculum development, and workforce planning.

Governments invest considerable resources in sending students abroad. Tracking whether these students return home after completing their studies and, if so, how many of them choose to work for the government, academia, or industry helps calculate the ROI of these investments. Tracking whether graduates return home to work is essential for talent attraction and retention. If many choose to work abroad, it may signal a need to improve domestic opportunities and working conditions in the field of data and AI.

For bilateral agreements, tracking the academic and career progress of scholarship recipients can be a diplomatic tool. It can showcase the positive impact of these agreements by highlighting the achievements and contributions of scholarship recipients.