Category: Recommended International Trainings

To boost innovation and productivity in the public sector, the Development Academy of the Philippines (DAP), through the Center of Excellence on Public-Sector Productivity (COE-PSP), conducted a series of Productivity Challenge workshops in Luzon and Mindanao this September.

The workshops were held in key cities, including Baguio, San Fernando, General Santos, Davao, and Cagayan de Oro.

Two hundred fifty-one government executives and staff from various agencies and institutions attended these workshops.

The Luzon leg featured DAP Associate Project Officer Alvin Bilog as the resource speaker and saw participation from several regional agencies and State Universities and Colleges (SUCs). Key attendees included the Department of Agriculture, Department of Information and Communications Technology, Department of the Interior and Local Government, and others.

In Mindanao, participants came from various government offices, including the Department of Education, the Department of Environment and Natural Resources, and the Philippine National Police.

The workshops aimed to spur and sustain creativity through individual and group exercises, prompting participants to redefine workplace productivity and address urgent productivity challenges. Attendees were encouraged to submit their ideas for the “2023 Productivity Spark: 1000 Ideas for Productivity.”

COE-PSP has already surpassed its target for idea submissions, and those who could not attend the workshops are invited to contribute their ideas via bit.ly/prodspark1000ideas.

The Productivity Challenge is a multi-year project spearheaded by the DAP’s Center of Excellence on Public Sector Productivity that intends to foster awareness and boost the productivity and innovativeness of public sector organizations (PSOs) in the Philippines. Its goal is to advance the productivity movement in the public sector by raising awareness and enabling a culture of innovation to improve productivity performance and quality service delivery. 2023 Productivity Spark: 1,000 Ideas for Productivity 2024 Fastbreak: 100k Transaction Hours Reduced 2025 Paper-less: 1 Million Sheets of Paper Saved 2026 Money Wise: 1 Billion Pesos Saved

Artificial Intelligence (AI) has an array of potential as an effective tool that can assist the development work. For one, AI can be a transformative force to accelerate sustainable development goal (SDG) targets such as climate change mitigation, healthcare, education, and economic growth. Through the lens of the public sector, AI can help enhance efficiency and productivity while improving decision and policy-making.

The public sector can learn from the experience of international organizations on how they have used AI to maximize efficiency and minimize risks, which, in the long run, can help boost productivity.

Marc Segone, Evaluation Officer Director of the United Nations Population Fund (UNFPA), in his presentation during the 2023 Asian Evaluation Week held in Thailand last 11-14 September, talked about the various stages in the evaluation process where AI can be applied, including data collection, analysis, interpretation, communication, synthesis, and report generation. The UNFPA also recognizes the importance of enhancing digital and AI literacy to bridge the digital divide, which can also be applied in the public sector.

Uyen Kim Huynh, Innovation Specialist at the Evaluation Office of the United Nations Children’s Fund (UNICEF), and Martin Prowse, Evaluation Specialist of Green Climate Fund (GCF), also shared their insights on how AI has helped them improve their efficiency.

At UNICEF, AI is tapped in the organization’s evaluation processes, particularly through the use of Natural Language Processing (NLP), for a better understanding of the holistic impact of UNICEF’s interventions, especially in multidimensional goals. These include analyzing priorities of interventions, scoping and synthesizing evaluations, generating baselines, and examining social data from sources like X (Twitter). AI tools also assist UNICEF in processing and cleaning large volumes of data efficiently, making it ready for analysis. AI further helps integrate diverse data sets from various sources, which is crucial for constructing baselines and counterfactual values. This can also be applied in public sector organizations that help decision-makers develop informed, evidence-based policies and recommendations.

While GCF has not been using generative AI yet, it explores the tool’s potential utilization in the future, especially in addressing the challenge of measuring adaptation in climate interventions. One significant challenge in climate evaluation is measuring adaptation, as it has no concrete metric similar to mitigation’s carbon footprint. AI may be helpful in synthesizing existing Climate Change Adaptation’s (CCA) monitoring and evaluation frameworks and other CCA activities, such as ensuring transparency, longitudinal data usage, and its alignment with the national contexts.

Still, AI also has its known harms and potential risks. These include human-AI conflict (difference in interpretation between the two), loss of control and technological dependency, unemployment fears and socio-economic disparities, lack of legal and regulatory frameworks, and ethical concerns.

Strong ethical guidelines and international regulations are crucial to reduce potential harm and mitigate AI-related risks. Implementing activities such as robust testing, data quality control, transparency, regular monitoring, and human oversight are also essential. Finally, automation must be balanced with human expertise and oversight in AI-powered evaluation.

Watch the session recording here.

Key Takeaways:

• AI holds promise in improving planning, decision intelligence, predictive analytics, and other systems that support evaluators in drawing more precise conclusions and making recommendations. As AI technology matures, its impact on evaluation practices is expected to grow. However, there is a need for regulation in using generative AI, ensuring data privacy, sensitivity, and responsible usage within organizations.
• The evaluation community can be pivotal in ensuring responsible and ethical AI power evaluation. The transdisciplinary nature of evaluation can contribute to assessing AI technologies’ merit and impact while identifying biases and unfair outcomes. Collaboration with internal stakeholders and external partners and developing a clear vision and leadership are essential for successful AI integration into the evaluation function.

As part of the 2023 International Conference on Artificial Intelligence in Work, Innovation, Productivity and Skills, the Organisation for Economic Co-operation and Development (OECD) held a session to discuss how policymakers should respond to the latest developments in Artificial Intelligence (AI). With the advent of new technologies such as deep learning and autonomous systems, the session aimed to explore the challenges and opportunities of these new forms of AI and identify ways stakeholders can use this policy to ensure that benefits are maximized, and risks are minimized.

Summary

The session held last 27 March was attended by representatives from the academe, government, and civil society, who discussed AI’s role in addressing critical social challenges. Participants also discussed the importance of ensuring that AI is developed in a way that is consistent with fundamental human rights, including privacy, non-discrimination, and fairness.

Speakers and panelists include Professor Ajay Agrawal (Economist and Professor at the University of Toronto’s Rotman School of Management), Emilija Stojmenova Duh (Minister of Digital Transformation, Republic of Slovenia), Zoë Baird (Senior Counsellor to Secretary Gina Raimondo, U.S. Department of Commerce), and Gabriel Mazzini (Team Leader, AI ACT, European Commission). Stephanie Ifayemi (Partnership on AI) moderated the panel discussion.

In his keynote speech, Professor Agrawal centered his discussion on the economic implications of AI systems – their costs and benefits. He also discussed the difference between the Point Solution and System Solution concerning AI technologies.

Professor Agrawal said most AI applications are being brought to market as a point solution to increase the productivity gains of stakeholders. For example, taxi companies provide navigational AIs to make professional taxi drivers better and to increase their productivity.

On the other hand, the system-level solution attempts to redesign the entire system with a new resolution to increase productivity. Uber, for example, was a complete redesign of the system.

During the panel discussion, Minister Duh provided several policy recommendations that Slovenia is making and shared how her country is balancing to address the emerging risks of AI while promoting innovation and adoption of trustworthy AI.

According to Minister Duh, some of their AI priority policies include quality data and open data maturity, setting up data stewards in the public sector, and establishing a national program for AI.

On the other hand, Baird discussed how the United States invests in education and training programs to ensure that individuals are equipped with the skills and knowledge necessary to work with new technologies.

“Productivity improvements can come from multiple directions. It is an area of enormous challenge to create an agile, rapidly-changing training system that enables millions of workers to learn the skills to participate in the economy and enables multiple new entrants and results in an economy that is not concentrated in a few winners,” Ms. Baird added.

Overall, the session provided insights into how policymakers should respond to the latest developments in AI by ensuring that policies promote human well-being and advance the public interest.

Key Takeaways

• As AI continues to evolve and become increasingly integrated into our daily lives, it is essential that policymakers take a proactive approach to ensure that benefits are maximized, and risks are minimized.
• It is vital for countries to develop ethical guidelines and standards for AI.
• Public participation and dialogue are essential in shaping AI policy to ensure that all stakeholders’ interests are taken into account.

Watch the recording of the session here.