AI Strategy is Now a Nation-Defining Capability

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I would love to learn more about your work as a government policy adviser on AI, infrastructure, education, and smart governance. Could you share about your role in crafting the National AI Roadmap under the Philippine Department of Trade and Industry? In what ways is this project important strategically for the Philippines?

Many enterprises and organizations already consider data science and artificial intelligence (DSAI) as strategic capabilities. They are no longer optional, “nice-to-have” capabilities, but necessary, “must-have” — a matter of organizational survival. Leaders across the world have realized that if they want their organizations to stay relevant, or even to survive, they have to be competitive and innovative. As a result, the embrace of technology has become a necessity, particularly analytics, because of its predictive power.

In recent years, governments have also started to recognize the same thing, and the Philippines is no exception. Like military capabilities, AI is now widely considered a nation-defining capability, especially since AI cuts across industries and sectors.

Ultimately, the Philippine government aims to harness the potential of our people and resources through innovation and entrepreneurship. For example, the local trade ministry, the Department of Trade and Industry (DTI), is implementing an Inclusive Innovation Industrial Strategy (i3S) specifically to grow innovative and globally competitive industries across the manufacturing, agriculture, and services sectors. DSAI, among other technologies, is front and center in making this possible, driving innovation, digital transformation, and improvements in industry competitiveness.

The DTI had contracted me and my colleague Dr. Chris Monetrola to design and craft the Philippines’ roadmap on AI. Its formulation was our first step towards developing and implementing AI strategies, programs, and projects to especially help local firms become more productive and competitive. Working on the roadmap, I am bringing in my expertise in data-driven science and a wealth of experience working with various stakeholders, particularly on triple helix projects. Another part of my role is to review existing AI policies and strategies published by other countries, to identify best policies and practices that the Philippines could adopt, and to customize and tailor-fit said principles and practices to our culture. Finally, the DTI also tasked us to analyze the country’s policy landscape and ecosystem for AI. Given our past R&D engagements, we also identified DSAI applications with tangible socioeconomic benefits, both short and long term.

What is your favorite project or a project you’re particularly proud of?

Besides designing the National AI Roadmap, I am particularly proud of a recent contact-tracing project with a multinational corporation (the Aboitiz Group), our academic institution (the Asian Institute of Management), various government agencies led by the National Economic Development Authority, and some local government units. The project was a data-for-good volunteer project to boost the country’s contact tracing capabilities, which had previously been identified as the “weakest link” in our government’s COVID-19 response.

Image courtesy of Erika Legara

The project consisted of two main parts — the first one involved the integration and harmonization of various data sources, particularly the database of cases from COVID-19 test results and the databases of contact tracing apps. In the second, we designed algorithms to efficiently build contact tracing networks and subsequently identify “vulnerable” individuals in communities.

An excellent contender to that project is the smart city project with Cauayan City, a small and developing city in the Philippines, which I have been involved in. It was the first smart city project of its kind to receive funding from our science ministry, the Department of Science and Technology (DOST). Academically, the science of cities is exciting, of course; but more than the science, the experience of working with various individuals, particularly domain experts (in policy and engineering, for example), is equally exhilarating.

I have been studying urban complexity for quite some time now, and the works I came across had, for the most part, only involved the study of advanced cities and economies with abundant data sources and an already mature data-driven culture. However, different challenges face data-driven urban researchers when studying the dynamics of cities and towns from developing economies like the Philippines.

Do you have any insight into how the government works to mitigate risks from deploying AI solutions and interventions with the vulnerabilities associated with marginalized communities?

I am a huge advocate for ethical and responsible AI. Thus, the Philippine National AI Roadmap design also highlights the importance of building an AI ecosystem “conscience,” which has its own strategic dimension in the plan. The dimension covers data privacy and the more contentious ethical issues in DSAI, pushing for reliable, safe and secure, trustworthy, inclusive, accountable, and transparent predictive platforms. Therefore, our recommendation to the Department of Trade and Industry is to establish a Commission or advisory board that focuses on responsible data and AI technologies. Ideally, the Commission would be composed of policymakers, legal experts, ethicists, academics, and DSAI practitioners. Further, efforts to this end would be convergent with other responsible and ethical guidelines for data and AI fashioned by other countries.

What projects do you feel are best addressed by governmental institutions rather than for-profit efforts in AI and DS?

As most of us in the field know, DSAI cuts across sectors and industries. In fact, it is quite a challenge to identify a sector or industry where DSAI will fail to have an impact, or at least where the technology cannot be applied.

Now, on government-driven DSAI projects, these would be those with a direct impact on the lives and livelihoods of people; i.e., initiatives that improve the well-being of the population.

Government institutions should also champion DSAI projects that specifically improve public service and other government processes. Off the top of my head, example projects include land-use design and development, traffic management, disaster risk reduction, public health service administration improvement, and energy and water management.

Finally, these institutions, constitutionally bound to ensure fair and proper treatment of Filipinos and offer the latter services without fear or favor, may spur initiatives for Fair AI, particularly the legal, policy, and jurisprudential frameworks needed to ground AI fairness locally.

What role(s) do you see the government (in the Philippines or Singapore) playing as a leader in data science for social good?

I see the government as the prime enabler of various data science for social good projects. First, I expect them to provide the strategic direction in DSAI to maximize the socio-economic benefits it brings. Second, I also trust policymakers to provide data scientists and DSAI teams with practical use cases, identifying critical socio-economic issues that must be prioritized and solved with data-driven approaches. For example, a government agency pushed for the contact tracing project I was involved in. Indeed, back when I was in Singapore, most of the R&D projects I was engaged in were championed by government agencies — from transport management to land-use design to improving human mobility.

Aside from the use cases, I also see the government championing data management and pushing for open data initiatives, primarily because of the gargantuan amount of information governments collect on their citizens and the environment.

I’ve already mentioned that government agencies can spur the development of legal, policy, and jurisprudential frameworks to solidly ground local Fair AI. But, more generally, it is upon the government to provide the necessary legal and policy frameworks to create a healthy local ecosystem for AI initiatives while preventing or minimizing disparate treatment of, or impact upon, Filipinos resulting from these initiatives.

Finally, I recognize the government as a major partner of enterprises, society, and other sectors in their digital transformation and DSAI initiatives. I also believe that a government’s role is to provide the right environment, infrastructure, and services to maximize other organizations’ benefits from big data, AI, and other emerging technologies in the Fourth Industrial Revolution.

What kind of writing in DS/ML do you enjoy, and what would you like to see more of?

I usually go to Towards Data Science for more technical pieces and then to other sources like Harvard Business Review and MIT Sloan Management Review for topics on data and AI strategy, data science leadership, building and managing data science teams, and innovating with technology. I hope to read more DSAI use cases and project implementations in TDS from the perspective of both DSAI teams and their non-DSAI collaborators. From experience, many small and medium enterprises and traditional large corporations struggle in their digital transformation and DSAI journeys. Thus, it would be great to read more about the challenges stakeholders the world over face in embracing DSAI and, if possible, the strategies implemented to overcome these hardships.

What are your hopes for the DS/ML community in the next few months/couple of years?

Indeed, I have a lot of hopes for the DSAI community. One is for us to continue engaging on data-driven projects for social good; aside from actually helping society, this is also one way for the field to be recognized by those who are not as exposed as we are to data-driven approaches.

I also hope that we, especially those working in the intersection of fields, learn how those outside our DSAI chamber speak. For context, executives (non-DSAI) tend to become disillusioned with what DSAI can do for their organizations because their technical team, the data scientists, simply cannot translate AI results into something they can understand — into insights valuable to the business. As a result, enterprises and other more traditional organizations are often left asking DSAI practitioners, “So what?”, a situation I dread finding myself in. My sincere hope is that more and more organizations embrace a more data-driven culture with the help of DSAI practitioners in the near future.

Finally, I hope that we as DSAI practitioners become more conscientious of what we build concerning algorithmic innovations — that we will all be responsible and ethical AI champions.


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