The COVID-19 pandemic has brought out some ugly truths about modern-day ageism. A combination of the virus’s properties, an overwhelmed health care system and systematic neglect have taken a brutal toll on the elderly.

Unfortunately, it could get worse — if countries effectively automate ageism by allowing what has happened so far to dictate future decisions about care.

This crisis has highlighted a shocking lack of concern about older people. As one journalist at the British newspaper Daily Telegraph opined: “COVID-19 might even prove mildly beneficial in the long term by disproportionately culling elderly dependents.” In the United States, nursing homes are particularly vulnerable because the people who work there are so poorly paid that they must hold down multiple jobs, increasing the risk that they will spread the virus. The death count at such facilities is at least 7,000, but more are certainly coming from states such as Florida that have been slow to respond and report.

Older people also lose out as medical personnel, inundated by coronavirus patients, must make difficult decisions on rationing care. In Italy, for example, hospitals had to refuse care to older patients — a practice that undoubtedly increased the mortality rate in that age group.

In short, it’s fair to say that the death rate among the elderly is probably higher than it would be if only physiology were at play. Now consider what will happen if data scientists try to take this experience, bake it into predictive algorithms and apply them in places where the pandemic is still on the rise, or where it flares up as countries attempt to reopen.

It’s possible they’ll recognize that they lack the information needed to build reliable algorithms. The data available are too deeply flawed to calculate overall mortality rates, let alone rates by age.

But I wouldn’t count on humility. Researchers have become far too accustomed to imagining that if they collect enough data — even if it’s incomplete or biased — the sheer volume will provide a more or less comprehensive view. The flawed data we have will be seen as better than nothing.

The resulting models will lack critical context and nuance. They won’t account for the likelihood of the patient surviving if they’d been given better treatment. Causation will be lost, creating a denuded description of the past — which, in turn, will skew against treating old people in the future.

Suppose such a model were used to decide where to send ventilators. The scarce life-saving equipment would go to places where it saw high percentages of people likely to benefit. This might improperly tip the balance away from hospitals that serve large elderly populations on the grounds that they’ll just die anyway.

In the scramble to model COVID-19 fatalities, data scientists — and he officials who use their models — would do better to recognize and admit what they cannot do, rather than jury-rig something that could end up doing more harm than good.

Cathy O’Neil is a mathematician.

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