Over the past six months I’ve come to appreciate the reasons behind the wording – the range of questions and complexities that sit within the SHADE space are legion.
The intersection of tech, health, and the environment
I worked for many years in the tech industry, where I saw the “agile” methodology evolve. Agile’s holy grail is a real-time, comprehensive, accurate feedback loop, keeping the ‘digital product’ in a constant state of adaptation to whatever is required of it at any point in time. This methodology has given us any number of frictionless experiences.
In 2021 I left tech land and did a Masters in Social Policy. My dissertation involved an analysis of policy around the NHS. I tuned into the public discourse about how the NHS needed to innovate; how its tech was clunky and not fit for purpose; how there were pockets of excellence where tech solutions were showing great promise; and how these needed to go large across the organisation while leveraging NHS data. An innovative agile culture was called for; healthcare was being channelled down the path forged by tech.
Since 2021, the advent of AI has turbo charged this joint venture for healthcare services globally. Concerns have been raised around how this could harm patients, for example through glitches in robotic surgery, bias in diagnosis, or dangerous recommendations. But there is another problem. Digital technology, and particularly AI and the big data it feeds off, have stratospheric environmental costs. This is less talked about in the healthcare context. Doctors are supposed to, first and foremost, do no harm. So if they are bringing benefits to patients, shouldn’t they be given a free pass and let off the climate change guilt? But the bottom line is that the storage and processing of health-related data is set to become the fastest growing sector in the datasphere; healthcare is doing significant and increasing harm, and this harm contributes to man-made climate change which represents the biggest threat to human health globally.
Despite this, calls for a more frugal approach to digital research and practice can feel like a lone voice from beyond the walls of the medical industrial complex. How do you stop the juggernaut? Should we stop it?
Compounding the problem
Measuring the environmental impacts of digital technologies and AI is far from being an exact science, and inexactness is something tech really hates. So there is an ongoing push for better accuracy in measuring the carbon emissions associated with these activities. Many are highlighting how this pursuit of accuracy not only generates its own carbon footprint, but it is distracting us from acting with the sense of urgency the situation demands. They say we know enough about the environmental impacts and we need to stop their worst excesses now before we not only significantly exceed 1.5, we hit a climate tipping point. We can’t delay until we can demonstrate these impacts with no caveats: It will be too late.
Others are not too concerned about this because they argue that more AI will address these environmental impacts.
Genomics and the Rebound Effect
Genomics provides a neat illustration of SHADE dilemmas. New genome sequencing methods, combined with digital innovation and AI are fuelling revolutions in genomics that will soon make it possible - time and cost wise - for whole genome sequencing (WGS) to be available to everyone. Indeed, the NHS intends to be the first national healthcare system to offer WGS as part of routine care.
The environmental impact of bioinformatics like those involved in WGS is clearly significant, but some argue that WGS’s need for data and computing power “should perhaps be perceived in a broader context and regarded as an investment in precision medicine”. In addition, it’s not possible to quantify the impact accurately and, per genome sequenced, it is reducing as renewable energy comes online and as the process is further optimised, potentially helped by AI. This brings us to a classic rebound effect question: Will the rate at which the environmental impact of WGS reduces be dwarfed by the massive scale up in use of WGS?
Conclusion
Personally, I feel uneasy about tech’s answer to any problem being more tech, but I am also aware that what is often referred to as “techno solutionism” can work - if not always in the first way tried. What I am now sure of is that we need to fill the SHADE space.
SHADE promotes interdisciplinary enquiry to understand and make visible sustainable practices situated in specific geographical and societal contexts. Undertaking both normative and solutions based research, SHADE draws on empirical, epistemic and ethical perspectives from philosophy, law, sociology and ethics, as well as from more quantitative approaches such as life cycle sustainability assessment. See more about SHADE.
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