When digital economy and Big Data empower our lives and increase inequalities of the UK’s education system, student protests turn out to be powerful very quickly. The article of James Meadway we publish explains why this method of algorithmic working was bound to fail and our hypothesis is that “Fuck the algorithm” could become a worldwide slogan!
Blog editorial team
Protesting students and a national outcry have forced a U-turn over A-level marking in Scotland, and subsequently Wales, England and Northern Ireland in quick succession, despite the UK government’s public belligerence at the prospect of revising students’ algorithmically-moderated grades. The modelling that had been applied will now be scrapped in favour of teachers’ assessments for both A-level and GSCE students. It’s a major victory for the thousands of students who have protested.
The anger has been completely justified. The model that had been applied appears to have been designed to produce results that, on the headline measures, looked ‘fair’ (in that the averages could look about right) whilst forgetting that the distribution of overall marks was itself the product of many thousands of individual results. Throw in the fact that searching for an average based on historic performance inevitably tends to favour those who have historically done well – which, in the British case, has privileged the private sector – and the stage was set for a spectacular government failure. It’s now clear that education secretary Gavin Williamson, “promoted beyond his competence”, needs to go.
Beyond the specific government failures here, however, such as the lack of oversight and review – including attempting to silence competent professionals – the fiasco and the protests indicate a trend for the future.
Since the Covid-19 outbreak erupted, fundamentally (and permanently) disrupting how we live and work, the presence of data and statistical modelling in all our lives has accelerated markedly. From working-from-home on one side to increased biosecurity surveillance on the other (itself ranging from contact-tracing apps to temperature monitoring to “pandemic drones”) – the weight of data in our everyday lives has dramatically increased.
We’ve all become very familiar with this ghostly digital presence in the decade since the Great Financial Crisis, as (mainly US) Big Data companies have exercised their extraordinary capacities to gather, store and analyse our data, resulting in immense gains for them and an increasingly data-saturated world for the rest of us.
One way or another, the fact of algorithmic prediction has become an accepted part of how we live, most obviously online in the form of recommendation algorithms. One set of consequences – surveillance capitalism’s insatiable greed for the data we produce – is becoming better-known. The hunger is driven by the raw economics of the digital economy: each dataset that can be obtained is worth more if it can be compared with another dataset, so the value of a data company is always maximised by grabbing as much data as it can.
Usually, we don’t notice the algorithms that are used to do this. The entire purpose of those used for behavioural analysis is to forecast, as far as possible, the actions of individuals on the basis of past data. Increasingly, they are also intended to shape the behaviour of individuals in particular ways – to guide us to specific YouTube videos or Facebook advertisers or whatever. They might also be used to shape our political beliefs and preferences, as we have seen.
The critical issue with exam results is that this method of algorithmic working was bound to fail. On average, the algorithm may be more or less correct, but the ‘average student’ does not really exist – it’s a statistical fiction, generated from data which describes many thousands of individuals, none of whom are the ‘average’. Specifically for exams, moreover, there are individual people who we expect to be judged against the criteria of both their own performance and some objective standard – not against the performance of the average, either today or historically. In situations like marking exams we have specific expectations of individual autonomy and the recognition of individual merit which statistical techniques tend to override.
So what we can tolerate for, say, targeted advertising, we find intolerable for exams, which have precisely the worst possible combination – from the point of view of algorithmic processing – of four factors: being applied en masse, in public, where specific individualised results are required, and where the results for an individual are supposed to say something about their merit or worth in a particular dimension with meaningful consequences.
(Obviously, exam results aren’t the only dimension we might judge people on, and mercifully, we tend not to filter any of the others through any sort of marking scheme – although, inevitably, one UK government advisor is at least interested in China-style social credit systems.)
Environmental instability
The combination of the first three factors – mass processing, public display, individualised results – makes the case for protests clear, and the last – that the results should bear some relationship to true merit – makes it desirable. Most algorithmic processing will have only one or two of these four factors: the fact that Facebook is choosing to display some adverts to you on the basis of the statistical assumptions it makes about you is certainly individualised, but it is also not something intended for wider social comparison. Most algorithmic processing will not result in protests; as it becomes ubiquitous, we may be largely unaware it is even happening, and we may not care too much.
But one thing we have learned from this pandemic is that environmental instability – of which Covid-19 is a profound example – brings with it a deeper and deeper dependency on data. The semblance of accounting and control that Big Data provides, built-in to the business models of our data economy and increasingly factored into the functioning of government, becomes more – not less – appealing in unstable conditions.
The social structures we currently use to manage our glut of digital information – principally the giant tech companies – have every incentive to maximise their reach across social life, given the blunt economics of data: more data means more value, so grab more data. And governments, confronted by conditions over which they have increasingly little control or sway, facing populations whose cynicism about government itself remains at historically high levels, have every incentive to try to utilise mass data techniques themselves. The (relatively unsophisticated) A-level results modelling was introduced, for instance, precisely because the pandemic had resulted in the cancellation of the actual exams: it was an attempt to cope with contingency on the basis of a forecasting model.
Put these two elements together – the raw economics of Big Data that drive its expansion, and the desire of governments to try to assert some control in situations of instability – and the likelihood is that algorithmic management becomes more common as part of how government operates, not less. And of course to the extent that government data is immensely valuable, like the treasure trove of NHS data, Big Data will be more than happy to assist governments in making use of it. Michael Gove’s recent speech on the future of government, for example, explicitly highlighted the need to “open up” government data in this fashion. The direction of travel is clear, and – under current circumstances – instability will accelerate us along it.
Politicisation
But as the techniques of modelling and forecasting become a more significant part of government, they become politicised. Alongside our belief in the autonomy of individuals and the belief that they ought to be assessed according to their own merits is a belief that government should be fair and transparent.
We have built entire systems of governance and rule around roughly those ideas: the legal system depends on this principle – that the assessments it produces are fair because they are delivered on the basis of evidence that is seen, and made on the basis of the individual standing trial or settling a dispute. We elect governments in a process that hides our personal choice, but which is intended to provide scrutiny and transparency of whatever government then emerges. Both systems may fail, but they fail relative to that approximate (and widely-held) ideal.
Statistical modelling, particularly as it becomes more sophisticated, does not work like this.
It is hard for us to understand even a relatively simple model, such as that used for the A-level results. (The Royal Statistical Society’s letter to the Office for Statistics Regulation is a good guide to the problems, however.) By the time very large datasets are being used, particularly in machine learning, the results that are produced may become literally indecipherable – they are, in the jargon, not ‘interpretable’. It is not possible to see why a large statistical model produces a result, and nor is it possible for the computer – unlike a judge or a politician, say – to explain why it reached a particular conclusion. Increasingly sophisticated machine learning means that algorithms are getting better at accounting for individual nuance. But if what they are going on is past behaviour, they still start to hem in future choices and can produce radically unfair outcomes.
Again, we might tolerate this in much of our online life. The fact a particular shop is being advertised to you in particular probably doesn’t matter too much. But if the decision-making process starts to intrude on questions of underlying value, or where the outcome has profound consequences on your life, it matters a great deal. And if it is the government making those decisions, the clash between our expectations of fairness and the actual results produced by government may become profound. This is the moment of politicisation: once a procedure is moved from the realm of the mundane, or from where a market can be blamed for an outcome, and into the realm of what we think of as government, it is open to political protest.
(I’m reminded somewhat of the politicisation of the labour market that took place in the West during the post-war boom. Once governments broke with liberal capitalism and accepted some responsibility for the management of labour, it politicised the question of how labour was managed. The early years of neoliberalism in the West were, in part, an attempt to break out of this problem by having states refuse to accept this responsibility. For the regimes in the East, the problem was chronic: the attempt by government to set the conditions in every market, including labour, meant everything was always the government’s problem. Every strike suddenly took on a political character.)
We have already seen multiplying protests during the pandemic, from Black Lives Matter to an uptick in strikes. We have also seen legal objections being raised, successfully, to the use of automated facial recognition, and we should expect further legal challenges to the encroachment of algorithmic methods in future. But what the A-level protests point towards are the opening rounds of a new form of protest, against a new style of government: one that appeals directly to our faith in fair, transparent, and human-centred processes, on one side, and against the opacity and unfairness of statistically-determined outcomes on the other.
« Nous, Désert’heureuses, refusons de continuer à robotiser, mécaniser, optimiser, informatiser, accélérer, déshumaniser le monde... ».
Nous publions ci-dessous le manifeste de ce tout nouveau collectif de jeunes diplômé.es des plus grandes écoles d'ingénieur.es françaises. Il s’agit non seulement de réfléchir à de nouveaux modes de production pour sortir du capitalisme technocratique et productiviste mais aussi d’agir et de faire exploser les remparts que le système de domination des grandes écoles reproduit depuis plus de deux siècles. Une approche militante radicale dont l’IDST ne peut que se réjouir et qui, espérons-le, sera largement suivie.
Facultad de ciencias sociales de la universidad de Valencia (España)
La investigación científica nos permite comprender la relación entre las personas y su medio natural, las dinámicas estructurales de igualdad o desigualdad de las sociedades en las que viven y los procesos históricos que les han originado. Nos proporciona saberes teóricos e instrumentos tecnológicos para intervenir sobre los problemas que asolan nuestro medio ambiente y nuestra realidad social. En las universidades públicas la investigación científica complementa a la actividad docente y ambas deben contribuir a la libertad y el bienestar colectivo de las sociedades que las sostienen. Desde dicho compromiso y ante la gravedad de la crisis civilizatoria que la pandemia del coronavirus está acelerando los promotores de este manifiesto miembros de la Universidad de Valencia, nos sentimos en la obligación de trasladar a la sociedad las siguientes consideraciones:
La evidencia científica es una condición necesaria pero no suficiente para transformar, en un sentido progresista, la realidad social. La excelencia de la investigación científica en las universidades públicas es inseparable de su conciencia ciudadana y de la capacidad de promover la participación ciuadana en la decisión sobre los fines y los usos sociales de la ciencia. En este sentido es necesario recorder, como ha señalado entre otros Richard Horton, editor de la prestigiosa revista científica The Lancet, que en los últimos años, las principales conclusiones de las investigaciones en el ámbito de la salud pública y la epidemiología advirtieron reiteradamente del riesgo de la pandemia actual; constataron que nuestras sociedades no estaban preparadas para afrontar dicho riego y, en consecuencia, instaron a los gobiernos a tomar medidas urgentes para paliar sus futuras consecuencias. Sin embargo, tal y como está ocurriendo desde hace décadas con las recomendaciones de las investigaciones sobre la crisis ecológica y el cambio climático, tales advertencias no sólo se ignoraron sino que incluso en determinados países como el nuestro dieron lugar a políticas sociales que, mediante los recortes del gasto público y las privatizaciones, han deteriorado todavía más los servicios y las infraestructuras públicas. La ciencia sin conciencia ciudadana es sólo otro tipo de negocio mercantil.
En la última década, dichos recortes también han deteriorado gravemente las universidades públicas españolas. Han precarizado las condiciones laborales de sus trabajadoras y trabajadores más vulnerables; han descuidado su formación y han asumido, con muy pocas críticas, el modelo neoliberal de la rentabilidad mercantil en la investigación en la gestión administrativa. El COVID-19 ha llegado a una universidad publica cada vez menos pública, cada vez más sometida a criterios de mera rentabilidad, y cada vez menos democrática. Nuestro confinamiento de hoy acentúa y hace más visibles las carencias de este modelo de universidad mercantil que ya había confinado en sus despachos a amplios sectores del PDI, que los había enclaustrado, desorientado y alejado de cualquier sentido de comunidad universitaria y de compromiso colectivo. Frente al ‘sentido común neoliberal’ que nos ha invadido es necesario repetir lo obvio: las universidades públicas no son empresas económicas. Es necesario abandonar este modelo. Es necesario regresar a una universidad pública comprometida con la transformación progresista de la sociedad que la financia.
Desde las consideraciones anteriores y ante la gravedad de la crisis civilizatoria que está precipitando la pandemia del Coronavirus es imprescindible poner nuestros recursos docentes e investigadores al servicio de las demandas de la ciudadanía. Nuestra investigación científica conocimientos y nuestros saberes aplicados han de estar a disposición de las necesidades concretas que está situación extraordinaria está provocando en la sociedad valenciana. Para ello, es imprescindible que las universidades públicas habiliten recursos telemáticos y procedimientos de información y comunicación propios con la sociedad valenciana que les permitan, en primer lugar, escuchar y recoger dichas demandas, sanitarias, sociales, psicológicas y humanas y, en función de sus propios recursos, darles respuesta.
Por último, entre la atmósfera de incertidumbres, temores y esperanzas que nos envuelve estos días se abre paso una certeza: cuando termine nuestro confinamiento no vamos a regresar al mismo mundo. De nosotras y nosotros depende, desde hoy mismo, que la conmoción que atravesamos abra el camino hacia una universidad pública que mejore en capacidad docente e investigadora y, también, en conciencia ciudadana y en compromiso con la transformación social.
Valencia, Abril 2020
Promueven este manifiesto: firman este manifiesto enviando un mail a jose.m.rodriguez@uv.es
La création d’un salaire étudiant a déjà été évoquée à plusieurs reprises sur notre blog et fait désormais régulièrement partie des revendications portées par nombre de mouvements étudiants, en France et dans le monde. Ce que l’on sait moins, et que révèle l’article ci-dessous d’Aurélien Casta, publié en janvier 2020 dans Le Monde diplomatique, c’est que ce projet d’un salaire pour les « jeunes travailleurs intellectuels » date de la Résistance et qu’il faillit même être adopté par l’Assemblée nationale à l’orée des années 50. Des ministres socialistes – éternité de la gauche molle ! –, s’y opposèrent alors. On lira aussi l’ouvrage du même auteur : Un salaire étudiant. Financement et démocratisation des études, La Dispute, Paris, 2017.
