The climate and the A.I.

A few days ago, the New York Times and other major international publications sounded the alarm over a new study that claimed various coastal cities around the world would be underwater to different degrees by 2050. However, something seemed off; it couldn’t have been straightforward for the authors of the study to plot how much the sea-level rise would affect India’s coastal settlements. Specifically, the numbers required to calculate how many people in a city would be underwater aren’t readily available in India, if at all they do exist. Without this bit of information, it’s easy to disproportionately over- or underestimate certain outcomes for India on the basis of simulations and models. And earlier this evening, as if on cue, this thread appeared:

This post isn’t a declaration of smugness (although it is tempting) but to turn your attention to one of Palanichamy’s tweets in the thread:

One of the biggest differences between the developed and the developing worlds is clean, reliable, accessible data. There’s a reason USAfacts.org exists whereas in India, data discovery is as painstaking a part of the journalistic process as is reporting on it and getting the report published. Government records are fairly recent. They’re not always available at the same location on the web (data.gov.in has been remedying this to some extent). They’re often incomplete or not machine-readable. Every so often, the government doesn’t even publish the data – or changes how it’s obtained, rendering the latest dataset incompatible with previous versions.

This is why attempts to model Indian situations and similar situations in significantly different parts of the world (i.e. developed and developing, not India and, say, Mexico) in the same study are likely to deviate from reality: the authors might have extrapolated the data for the Indian situation using methods derived from non-native datasets. According to Palanichamy, the sea-level rise study took AI’s help for this – and herein lies the rub. With this study itself as an example, there are only going to be more – and potentially more sensational – efforts to determine the effects of continued global heating on coastal assets, whether cities or factories, paralleling greater investments to deal with the consequences.

In this scenario, AI, and algorithms in general, will only play a more prominent part in determining how, when and where our attention and money should be spent, and controlling the extent to which people think scientists’ predictions and reality are in agreement. Obviously the deeper problem here lies with the entities responsible for collecting and publishing the data – and aren’t doing so – but given how the climate crisis is forcing the world’s governments to rapidly globalise their action plans, the developing world needs to inculcate the courage and clarity to slow down, and scrutinise the AI and other tools scientists use to offer their recommendations.

It’s not a straightforward road from having the data to knowing what it implies for a city in India, a city in Australia and a city in Canada.