On bringing data science and philosophy to the hardest questions in elder care
When people learn I have a double major in Data Science and Philosophy, the response is usually a polite pause. The two feel, at first glance, like they belong to different worlds — one rigorous and quantitative, the other speculative and slow. But the longer I work at Sao Po Centre on Ageing researching caregiver burden, the more I believe that this combination is precisely what care for older adults needs most.
My philosophical education was shaped largely by the classical Chinese and Buddhist traditions. One idea that stayed with me comes from a very old distinction in Chinese thought: the difference between 道 (dào) — the way, the underlying principle — and 術 (shù) — technique, strategy, method. A practitioner who accumulates shù without dào becomes adept, perhaps impressive, but ultimately unmoored. They optimise without knowing what they are optimising for.
But beyond this structural distinction, what these traditions gave me was something more foundational: a sense of what a life well spent actually looks like. Confucian thought, at its core, holds that the highest expression of being human is 仁 (rén) — benevolence, a genuine orientation toward others. Buddhism deepens this into 慈悲 (cíbēi), compassion as active practice. Both traditions push against the idea that personal flourishing is the end goal. They ask instead: who else is better off because of your being here? This is the question that draws me to ageing research. Not because it is prestigious or financially rewarding — it is neither — but because this field puts me in proximity to some of the most fundamental human struggles, and asks whether we can do something about them.
I thought about this distinction a great deal in late 2024, when I lost a close friend. In the months that followed, I spent time at a Buddhist monastery, reading sutras and philosophical texts on death and impermanence. Among the texts I encountered was the final book by a philosopher at Renmin University of China — written in the months before his own death from illness. What struck me most was a distinction he drew carefully: the difference between death (死亡, sǐwáng) and dying (死, sǐ) as a lived process.
Death itself, he argued, is not what we should fear. It is the natural conclusion of a human life — not painful, not shameful, not something that demands our dread. What is genuinely hard, what deserves our full moral and practical attention, is the extended process before death arrives: the slow erosion of capacity, the loss of independence, the mounting burden on those who love us, the quiet suffering that accumulates across months and years. This is the territory of ageing. This is where the real work is. His framing gave me language for something I had already felt but could not articulate: that the goal of elder care is not to delay the endpoint, but to dignify the journey—not only for the person who is dying, but also for those who accompany them.
Early in my research, I encountered accounts of elder care tragedies in Japan, where elderly caregivers, exhausted and isolated after years of burden, reached a breaking point. Some took the life of the person they loved most, unable to bear either the weight or the alternative. These are not stories of monsters, but of human beings ground down by systemic pressure. They reminded me of something crucial: if a care system sees only the person being cared for, and remains blind to the caregiver’s dignity and breaking point, its kindness will eventually curdle into tragedy.
What those weeks made clear was something that rarely appears in research dashboards: dying is not just a clinical event. It is one of the most profoundly human passages a person can make. Nor is the distinction between quantitative and qualitative understanding as simple as it might seem. Numbers capture what narrative cannot systematize: population trends, caregiver burden across populations, evidence for policy. Stories and careful listening reach what data cannot: the specific textures of fear, the desire to be remembered, the weight of an unseen struggle. These are not opponents—they complement each other. And this is where AI offers something genuinely different: it can translate the previously uncapturable detail—a caregiver’s particular exhaustion, an elder’s wish not to be forgotten—into analysable information; and then translate that information back into warmer, more personalized care. Yet in most discourse around AI and ageing, this potential remains underdeveloped. The extended process of dying before death—the long journey requiring accompaniment—is still too often treated as merely a variable to be managed.
This is the gap that troubles me — and it is where I think AI has the most to offer, if pointed in the right direction.
We are living through a transformation comparable in scale to the Industrial Revolution. The long-term consequences of AI on society are genuinely unforeseeable. In the near term, economists warn of massive disruption to labour markets. But every major technological shift raises the same question that history has never resolved on its own: who does the transformation actually benefit? China’s Reform and Opening Up generated extraordinary wealth — yet its earliest and largest rewards flowed to those who already had capital and proximity to power. The AI wave risks following the same current, unless people actively choose to redirect it.
This is how I choose to engage. Yes, riding the wave—that much is inevitable. But riding it toward a different shore. Deliberately positioning myself, and our collective efforts at CoA, in the space between this technology and the people most vulnerable to being left behind: the elderly, their caregivers, those navigating the long, quiet difficulty of dying. I have no illusions about the scale of individual effort—it is small. But I am fortunate to work with colleagues who understand this work as a shared responsibility. Each of us, through concrete projects, tries to push in the same direction. Small steps matter less than whether the direction itself is sound. And on that much, I can find peace.
AI is already entering elder care, and in many respects it brings genuine promise. Tools that reduce information overload for exhausted caregivers, systems that detect early signs of cognitive decline, applications that help older adults record and share their life narratives before memory fades: these are not trivial contributions. Used well, AI creates more space for the human moments that matter.
But “used well” is the critical phrase. The risk is not that AI will fail to be efficient — it will almost certainly be efficient. The risk is that we mistake efficiency for care. That we measure what is measurable, optimise what is optimisable, and quietly set aside everything that resists quantification: loneliness, meaning, the need to be witnessed.
What motivates me in this work is the belief that the real measure of any technology in ageing care is not how much it streamlines, but how much it dignifies. If an AI tool can help a caregiver feel less alone in their role, or help an older adult feel seen and understood in their final years, then it is doing something worth doing.
Data science brought me the tools. Philosophy, and a little grief, taught me what they are for.
By: Wenbo (文博). Research Assistant
Email: wennbo@hku.hk