Difference between revisions of "La monetización del cotidiano Por Andreu Belsunces"

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English version below
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'''Monitoreo / capitalización del comportamiento / configuración hábitos'''
 
'''Monitoreo / capitalización del comportamiento / configuración hábitos'''
  
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Teniendo todo esto en cuenta, es cuando Fuller y Goffey dicen que es incierto hasta qué punto el comportamiento ha sido transformado en economía. La interfaz, en la era de la datificación, juega en su rol de intraestructura prescriptora de hábitos, una posición central en la cristalización y reproducción de un estado de cosas emergente, donde intereses corporativos toman forma en hardwares y softwares que permiten existir, pensar y relacionarse en un espectro de posibilidades y no en otras. Y es ahí dónde las interfaces no sólo configuran y normalizan determinadas formas de ser usuario, sino también de ser en un sentido ontológico.
 
Teniendo todo esto en cuenta, es cuando Fuller y Goffey dicen que es incierto hasta qué punto el comportamiento ha sido transformado en economía. La interfaz, en la era de la datificación, juega en su rol de intraestructura prescriptora de hábitos, una posición central en la cristalización y reproducción de un estado de cosas emergente, donde intereses corporativos toman forma en hardwares y softwares que permiten existir, pensar y relacionarse en un espectro de posibilidades y no en otras. Y es ahí dónde las interfaces no sólo configuran y normalizan determinadas formas de ser usuario, sino también de ser en un sentido ontológico.
  
==Referencias==
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----
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----
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== '''The Moneticization of Everyday Life - Andreu Belsunces''' ==
 +
 +
 
 +
'''Monitoring / capitalizing behaviour / configuring habits'''
 +
 
 +
''“Why not consider the irreducibly constitutive role of machines, techniques, or technology in the problematic axiology of power?”'' Matthew Fuller and Andrew Goffey
 +
 
 +
''“We have gone from being agents tending to the action of machines to becoming constituent parts of them.”'' Deleuze and Guattari
 +
 
 +
- Note: the numbers in brackets correspond to points in the Interface Manifesto
 +
 
 +
==Introduction==
 +
 
 +
The user is one of the key figures in today’s cultural ecosystem. More than cables, satellites, and protocols, the internet is, above all, its users. The power of disruptive companies, the inspiration for cutting-edge innovation, and the content that feeds all types of platforms depends on them (on us). We are a (porous) bottomless pit.
 +
When the new century was born it brought along Web 2.0, and like children born with a silver spoon, it augured a future full of happy omens. Users became the fuel for the new paradigm based on participation and horizontality, and the engine was the companies that created spaces where it could take place. The important thing was the information, the “content”.
 +
 
 +
As “viewers” became “users”, it became clear that the strategies that made money in traditional media could also make money in online environments. Online platforms and portals began selling brands to its users, just as television had sold brands to audiences. And just as television had segmented audiences based on market research and audiometers, web companies used cookies, the profiles created by users when they sign up to use their services, and their data footprints in general. The digital environment started to convince users through performativity, just as television tried to convince viewers through seduction.
 +
 
 +
==The Datified User==
 +
 
 +
The characteristics users have changed in line with the changes in the corporate, technical, institutional, and social fields. We could say that two main trends have shaped what we know now generally refer to as the “user”. On one hand, in the age of Big Data everything around us has become a potential source of information, and many aspects that were left up to human judgement are now augmented or replaced by computerised systems. On the other hand, mobile communication offers myriad internet-connected devices that allow us to interact with each other and with the world. As Viktor Mayer-Scönberger and Kenneth Cukier argue, this has changed basic aspects of our lives and given them an unprecedented quantitative dimension. As a result a new layer of meaning was added to the idea of the participatory user that became widespread with Web 2.0.
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Against this backdrop, graphic user interfaces, which form part of virtually all our devices, become – to paraphrase Lev Manovich – cultural objects that permeate all areas of contemporary societies, creating frameworks for monitoring, representation, simulation, analysis, decision-making, memory, vision, writing, and interaction.
 +
These interface-mediated processes are part of the so-called “algorithm society”, in which the idea of machine learning encourages constant monitoring (through geolocation, the use of apps that require access to contact details, the use of online services and networks, city sensorization, etc.), which is described as a collateral effect and a necessary step for reaching the next level of comfort and efficiency. This approach takes for granted that the more machines know about us, the better they will serve us.
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==Optimization + Operability==
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This process is what has turned our omnipresent interfaces into spaces for potential datified interaction (2). Algorithms, which are supposed to compile, classify, predict and manage information, passively monitor our use of interfaces. In the context of hyper-accelerated capitalism, algorithms – which respond to the interests of the system they form part of – attempt to optimize a system that tends towards simplicity and operability for users, and profitability for the companies behind them (7).
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To this end, digital environments, always in Beta, begin operating in a way akin to what Fuller and Goffey have called a Pavlovian experiment on large-scale, constantly adjusting to the way they are used according to the collected data.
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An example is AppPulse Mobile, a web-native software for designers and developers that monitors the use of services in which it is implemented, monitoring their operation in real time – from the moment in which a user’s finger first glides on a screen to the moment he goes offline. This allows programmers to identify the problems that affect the users or functionality of an application, to discover what fails on a particular device, operating system, or app version, and to fix errors before they affect the “end user experience” (and before the company that has contracted their services finds out).
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Going back to Fuller and Goffey, these testing techniques support, amplify, and confirm the production of the user as a behavioural unit and provider of quantifiable test data, placing him in an unwitting, unconscious situation of agency in regard to the interface and the services it enables (14).
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==Surveillance==
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As well as monitoring geared towards the performative optimization of the interface, user datification is also used to produce consumer profiles. Through algorithms, data collection and ongoing processing can reveal overall patterns in regard to the types of users interested in a particular product, so that companies can adapt the content or try to attract other types of user profiles. Similarly, the general characteristics of groups of users can be sold to polling and marketing surveys.
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When data is aggregated, the features of individual users blur. This is why there are also other uses for interface surveillance at the individual level. Depending on privacy conditions, one of these other uses could be collecting information for mass surveillance by governments, as Edward Snowden’s leaks in June 2013 showed.
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Another more obvious use of individually-generated data is custom advertising to match user profiles. And another, perhaps more important, use is the burgeoning personal assistant market. This particularly interesting case shows how algorithms try to learn and predict user behaviour.
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For instance, Google Now, a personal assistant developed for mobile devices, collects information extracted from our use of different Google services (Gmail, Google+, Google Maps, Chrome, Google Calendar, etc.) and combines the different data to identify our patterns and preferences. Based on these patterns, it offers personalised recommendations of events, products, and routes, and even lets us know what time we should leave home to catch a flight or go to work, without us asking it to.
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This case illustrates the extent to which users feed data systems through the interfaces they use, providing detailed information about their everyday lives.
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==Interface as Infrastructure==
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Interfaces are data collection points that involve the interests of several actors. But the means by which this takes place are not clear. By mediating and simplifying technical operations, interfaces produce black boxes and their expedient opacity hides behind an illusion of transparency and user-friendly design (9)(10)(12).
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Today’s interfaces, which are becoming increasingly ubiquitous in our lives, are designed to reduce user friction. In a quest to achieve this, the field of user experience design prioritises interface design that seeks efficiency of gesture and thought. And this requires monitoring geared towards optimization. But paradoxically, this contradicts a phenomenon noted by Olia Liana: the gradual disappearance of the term “user” in the discourse of designers and developers. By calling users “persons” they are naturalized (and neutralized) and the computational component is eliminated. But at the same time, they are subjected to surveillance that is programmed into the algorithms. This is one factor that contributes to reducing the conscious agency of interface users. The irrefutable tendency to make all graphic interfaces as usable, engaging, simple, and social as possible is an expression of the values inherent to the discourse of Silicon Valley and global technoculture elites.
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Interfaces as infrastructure incorporate these narratives and exert enormous power in their apparent neutrality. Because of their necessary, ubiquitous mediation, interfaces go unnoticed, as if they were an ambient factor. But the truth is that as users we delegate many different tasks to them, and through this delegating we give them a great deal of control when it comes to making all kinds of decisions.
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As Pierre Lévy argues, intelligence technologies – including computing and its interfaces – affect the way we think and behave, and our habits. This is where interfaces – not just as systems of representation, but also as matter – exercise agency on users, in many different forms.
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==Metrification as (and of) Performativity (15)==
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In their graphic form, algorithms invite those who interact with them to adopt their own logic, and this often leads to standardization based on their own pre-defined objectives (11). This can be seen in the growing quantification of online life, and in how the importance of metrics as standards of measurements is introducing the world of business management into the private realm.
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Artist and composer Ben Grosser talks about how social networks, particularly Facebook, enable certain conditions of performativity that stimulate what he calls the “business ontology” and “audit culture”. In this way users, focusing particularly on the amount of “likes”, friends, or comments on their own pages and those of others create a “graphopticon”, an audit practiced on themselves, where many users monitor the activity metrics of many others.
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This quantifying dynamic is fuelled by and becomes integrated in the habits of users, at least partially, through gamification strategies. As Daphne Dragona shows, every move on social media is measured and every post awaits response, creating a particular field of action in which content constantly competes with other content for attention. These points systems, she says, depend on algorithms on one hand, and on promptness and virtuosity on the other.
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Expanding these dynamics to the world of interface design, we see how game dynamics configure performativities geared towards creating a situation of soft conflict in users, who must negotiate it with their prowess (13). Rewards based on quantification (or micro-satisfaction) fuel participation, opening up the opportunity for playful interaction, but also for more possibilities of exploitation and control. (6)
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This is how business ontology seeps into social life through interfaces, leading - as Grosser puts it – to the audit culture or an increase in the use of observational measurement – way beyond its original use in financial administration – into the previously self-managed public sector.
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As Dragona writes, it is no coincidence that gamification has reached users in the age of data-oriented culture and economy, when there is a desire to calculate and quantify everything not just by governments, companies and institutions, but also by users and citizens themselves.
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This dynamic should be considered within a scenario that is very well illustrated by Mayer-Scönberger and Cukier, in which data become an important corporate asset, a vital economic factor, and the basis for new economic models. Which is why the more measurable our living habits become, the easier it will be to monetize them.
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Taking all of this into account, Fuller and Goffey say that we are not aware of the extent to which behaviour has been transformed into economics. In the age of datification, the interface acts as an infrastructure that prescribes habits, a key position in the crystallisation and reproduction of an emergent state of affairs, where corporate interests materialize in hardware and software that make it possible to exist, think, and interact within a specific range of possibilities, but not others. This is where interfaces not only configure and normalise particular ways of being a user, but also of being itself, in the ontological sense.
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==References==
  
 
Andrejevic, M. (2013). Estranged Free Labor’ in Digital Labor, The Internet as a Playground and a Factory. Ed. Scholz. (New York: Routledge, 2013), 149 – 164
 
Andrejevic, M. (2013). Estranged Free Labor’ in Digital Labor, The Internet as a Playground and a Factory. Ed. Scholz. (New York: Routledge, 2013), 149 – 164

Revision as of 20:54, 16 August 2015

English version below

Monitoreo / capitalización del comportamiento / configuración hábitos

"¿Por qué no considerar el rol irreductiblemente constitutivo de las máquinas, técnicas o tecnologías en la problemática axiología del poder?" Fuller y Goffey

"Hemos pasado de ser los agentes al cuidado de la acción de la máquina a convertirnos en partes constituyentes intrínsecas de ellas" Deleuze y Guattari

- Nota: los números entre paréntesis indican relación con los puntos del Interface Manifesto

Intro

El usuario es una de las figuras centrales en el ecosistema cultural de hoy en día. Internet, más allá de sus cables, satélites y protocolos, es sobre todo usuarios. Es en ellos (nosotros) en quienes se sustenta el poder de las empresas disruptivas, donde se inspira lo cutting-edge, y de donde las plataformas de todo tipo se nutren de contenido. Somos un (poroso) pozo sin fondo.

El nuevo siglo llegó con la Web 2.0 bajo el brazo, y como el pan que traen los niños, prometía un futuro lleno de buenos augurios. El usuario se convirtió en la energía del nuevo paradigma de la participación y la horizontalidad, y su motor, las compañías que crearon espacios para que eso sucediera. Lo que importaba era la información, el “contenido”.

A medida que los “espectadores” fueron convirtiéndose en “usuarios”, empezó a quedar claro que lo que daba dinero en medios de comunicación tradicionales podía hacerlo también en los entornos online. Si la televisión vendía a sus audiencias a las marcas, las plataformas y portales empezaron a hacerlo con los usuarios. Si la televisión segmentaba a sus públicos de acuerdo a un sistema de estudios de mercado y audiómetros, la web lo hacía con mucha más precisión aprovechando las cookies, los perfiles que se hacían los usuarios al registrarse en sus servicios y sus rastros de datos en general. Donde la televisión trataba de convencer a los espectadores a través de mensajes seductores, el entorno digital empezó a hacerlo a través de la performatividad.

El usuario datificado

Las características de lo que se entiende por usuario han ido mutando a medida que lo han hecho los ámbitos corporativos, técnicos, institucionales o sociales. Podría decirse que lo que hoy se entiende por usuario está marcado por dos tendencias. Por un lado, en la era del Big Data todo lo que nos rodea se convierte en potencial fuente de información, y muchos aspectos que eran competencia exclusiva del juicio humano se ven aumentados o sustituidos por sistemas computerizados. Por otro, la comunicación mobile, pone a nuestra disposición una miríada de dispositivos conectados a Internet, a través de los cuales nos relacionamos con nosotros mismos y con el mundo. Esto -como afirman Mayer-Scönberger y Cukier- ha modificado aspectos fundamentales de la vida, otorgándole una dimensión cuantitativa que nunca antes había tenido. Es así como se añade una nueva capa de significado a la idea de usuario participativo generalizada con la web 2.0.

En este contexto, las interfaces gráficas de usuario, presentes en prácticamente todos nuestros dispositivos, se constituyen -parafraseando a Lev Manovich-, en objetos culturales que impregnan todas las áreas de las sociedades contemporáneas, creando marcos para el control, la comunicación, la representación, la simulación, el análisis, la toma de decisiones, la memoria, la visión, la escritura o la interacción.

Estos procesos mediados por interfaces se inscriben en la llamada sociedad del algoritmo, donde la idea del machine learning impulsa un monitoreo constante (a través de la geolocalización, el uso de apps que requieren acceso a datos de contacto, uso de redes y servicios online, o la sensorización de las ciudades, etc.), que se presenta como un efecto colateral y un paso necesario para alcanzar el siguiente nivel de confort y eficiencia. Se da por sentado que las máquinas podrán servirnos mejor cuanto más sepan de nosotros.

Optimización + operatibilidad

Es así como las omnipresentes interfaces se han convertido en espacios de potencial interacción datificada (2). Sus algoritmos, cuya función es recopilar, clasificar, predecir y gestionar información, ejercen la atenta vigilancia pasiva del uso que hacemos de las interfaces. Respondiendo a los intereses del sistema en el que se inscriben, los algoritmos, entendidos en el contexto del capitalismo hiperacelerado, buscan optimizar un servicio que tiende a ser cada vez más sencillo y operativo para los usuarios, y más provechoso para las empresas que los impulsan (7).

Para ello los entornos digitales, en una versión Beta permanente, adoptan el funcionamiento de lo que Fuller y Goffey han llamado un experimento pavloviano a gran escala y altamente distribuido, adaptándose constantemente al uso que se les da en función de lo que los datos recogidos indican.

Ejemplo de ello es AppPulse Mobile, un software nativo online orientado a diseñadores y desarrolladores. Su función es monitorear el uso que se hace de los servicios donde se implementa, controlando en tiempo real -desde el primer desliz del dedo hasta el momento que se abandona la interfaz- su correcto funcionamiento. De este modo, es posible identificar los problemas que afectan a los usuarios o a la funcionalidad de la aplicación, y averiguar qué falla dependiendo del dispositivo, el sistema operativo o la versión de la app, arreglando los errores antes de que afecten la “experiencia del usuario final” (y antes de que se entere la empresa que ha encargado estos servicios).

En este sentido, y volviendo de nuevo a Fuller y Goffey, estas técnicas de testeo apoyan, amplifican y confirman la producción del usuario como una unidad que provee datos cuantificables provenientes de los tests, poniéndolo en una involuntaria e inconsciente situación de agencia respecto a la interfaz y los servicios que habilita (14).

Vigilancia

Paralelamente al monitoreo orientado a la optimización performativa de la interfaz, la datificación del usuario se aplica también a construir perfiles de consumidores. Mediante los algoritmos, la recogida y procesamiento constante de información agregada puede, por un lado, servir para conocer patrones generales sobre el tipo de usuarios de un producto y así adaptar los contenidos o tratar de atraer otro tipo de perfil. Asimismo, los rasgos generales de los grupos de usuarios pueden venderse a estudios de mercado u opinión.

Cuando los datos de los usuarios están agregados, los rasgos individuales se desdibujan. Es por ello que la vigilancia que ejercen las interfaces a nivel individual tiene otras aplicaciones. Una de ellas, y dependendiendo de las condiciones de privacidad, podría ser, como mostraron las filtraciones de Edward Snowden en junio de 2013, ejercer de punto de recogida de información para servicios de vigilancia masiva llevada a cabo por Estados.

Otra aplicación más evidente de los datos producidos individualmente es la de la publicidad personalizada según perfil, y otra, quizás más significativa, es la que se utiliza para el incipiente mercado de los asistentes personales. Este es un caso especialmente interesante, que da cuenta de cómo los algoritmos tratan de aprender y predecir el comportamiento de los usuarios.

Google Now, un asistente personal desarrollado para dispositivos móviles, registra la información extraída del uso que hacemos de los distintos servicios de Google (Gmail, Google+, Google Maps, Chrome, Google Calendar, etc.), para, combinando los distintos datos, reconocer nuestros patrones y preferencias. A partir de ellos, trata de hacernos recomendaciones de eventos, productos, rutas, o incluso avisarnos, sin previa solicitud, de a qué hora debemos salir de casa para tomar un vuelo o ir a trabajar.

Este hecho concreto da cuenta de en qué medida el usuario alimenta sistemas de datos a través de las interfaces, proveyendo información detallada sobre su vida cotidiana.

La interfaz como infraestructura

Las interfaces son puntos de recogida de datos donde varios actores toman partido, y sin embargo, los mecanismos mediante los cuales esto sucede no se hacen evidentes. En su mediación y simplificación de operaciones técnicas, estos dispositivos construyen cajas negras, cuya conveniente opacidad se escuda en una ilusión de transparencia y amabilidad desplegada en diseños user friendly (9)(10)(12).

Con creciente presencia en nuestras vidas, las interfaces contemporáneas buscan un diseño que disminuya la fricción con el usuario. En su afán por esto, el User Experience Design prioriza una interacción guiada por la eficiencia de la gestualidad y el pensamiento Para lograr este objetivo, el monitoreo orientado a la optimización es básico. Esto, sin embargo, se contradice paradójicamente con lo que Olia Liliana ha notado respecto a la paulatina desaparición de la idea de usuario en el discurso de diseñadores y desarrolladores. A la vez que se lo naturaliza (y neutraliza) llamándolo “persona” y eliminando el componente computacional, se lo somete a una vigilancia programada en los algoritmos. Este es uno de los factores que contribuyen a disminuir la capacidad de agencia consicente de los usuarios sobre las interfaces. La tendencia incuestionable de que cualquier interfaz gráfica debe ser lo más usable, engaging, sencilla y social posible, es una materialización de los valores inherentes al discurso de la élite de la tecnocultura global de Sillicon Valley (8).

Como infraestructura, las interfaces incorporan estas narrativas, y despliegan en su aparente neutralidad un enorme poder. En su necesaria y ubicua mediación, las interfaces pasan desapercibidas como factor ambiental, cuando en realidad los usuarios delegamos en ellas una gran variedad de tareas. En esta delegación, les transferimos una gran capacidad de control a la hora de tomar todo tipo de decisiones.

Tal como nos enseña Pierre Lévy, las tecnologías intelectuales, entre las cuales se encuentra la computación y por lo tanto sus interfaces, afectan a nuestro pensamiento, hábitos y comportamiento. Es en este punto donde las interfaces no sólo como sistemas de representación sino también como materia, ejercen su capacidad de agencia sobre los usuarios, la cual se expresa de muchas maneras.


La metrificación como (y de la) performatividad (15)

Los algoritmos, en su traducción gráfica, invitan a quienes se relacionan con ellos a funcionar con su misma lógica, llevando a menudo a una normalización basada en objetivos predefinidos en ellos (11). Esto se hace evidente en la creciente cuantificación de la vida online. El valor que se da a las métricas como estándares de medición se está trasladando del mundo de la gestión empresarial al de la vida privada.

Al respecto, Ben Grosser habla de cómo las redes sociales y especialmente Facebook habilitan ciertas condiciones de performatividad que estimulan lo que él llama “cultura del negocio y la auditoría”. Así, los usuarios, poniendo especial atención a la cantidad de likes, amigos o comentarios en sus perfiles y los ajenos, crean un “grafóptico”, una auditoría practicada a sí mismos donde muchos usuarios vigilan la medición de la actividad de muchos otros.

Esta dinámica cuantificadora se alimenta e inserta en los hábitos de los usuarios, por lo menos en parte, a través de las estrategias de gamificación. Como muestra Daphne Dragona, en las redes sociales cada movimiento es medido y cada post espera respuesta, de modo que se forma un particular campo de acción donde los contenidos compiten constantemente los unos por los otros por atención. Estas puntuaciones, explica, dependen de los algoritmos por un lado y de su prontitud y virtuosismo por el otro.

Extendiendo estas dinámicas al mundo del diseño de interfaces, vemos cómo las dinámicas de juego configuran performatividades orientadas a crear una situación de conflicto suave en el usuario, que a través de su pericia debe ir sorteando (13). Los premios (o microsafisfacciones) basados en la cuantificación alimentan la participación, abriendo la oportunidad no sólo a una interacción lúdica, sino también a mayores posibilidades de explotación y control (6).

Es así como la ontología del negocio se filtra en la vida social a través de las interfaces, llevando -usando las palabras de Grosser-, la cultura de la auditoría o el aumento de la aplicación de la medición observacional -mucho más allá de su uso original en la administración financiera-, hacia el sector público previamente auto-gobernado.

Tal como escribe Dragona, no es coincidencia que la gamificación haya llegado a los usuarios en la era de la cultura y la economía guiada por los datos, cuando todo quiere ser calculado y cuantificado no solo por gobiernos, compañías o instituciones, sino también por los mismos usuarios o ciudadanos.

Esta dinámica debe entenderse en un escenario muy bien ilustrado por Mayer-Scönberger y Cukier, donde los datos se convierten en un activo corporativo importante, un factor económico vital y en el fundamento de nuevos modelos económicos. Es así como, cuanto más medibles se vuelven nuestros hábitos de vida, más facil será monetizarlos.

Teniendo todo esto en cuenta, es cuando Fuller y Goffey dicen que es incierto hasta qué punto el comportamiento ha sido transformado en economía. La interfaz, en la era de la datificación, juega en su rol de intraestructura prescriptora de hábitos, una posición central en la cristalización y reproducción de un estado de cosas emergente, donde intereses corporativos toman forma en hardwares y softwares que permiten existir, pensar y relacionarse en un espectro de posibilidades y no en otras. Y es ahí dónde las interfaces no sólo configuran y normalizan determinadas formas de ser usuario, sino también de ser en un sentido ontológico.






The Moneticization of Everyday Life - Andreu Belsunces

Monitoring / capitalizing behaviour / configuring habits

“Why not consider the irreducibly constitutive role of machines, techniques, or technology in the problematic axiology of power?” Matthew Fuller and Andrew Goffey

“We have gone from being agents tending to the action of machines to becoming constituent parts of them.” Deleuze and Guattari

- Note: the numbers in brackets correspond to points in the Interface Manifesto

Introduction

The user is one of the key figures in today’s cultural ecosystem. More than cables, satellites, and protocols, the internet is, above all, its users. The power of disruptive companies, the inspiration for cutting-edge innovation, and the content that feeds all types of platforms depends on them (on us). We are a (porous) bottomless pit. When the new century was born it brought along Web 2.0, and like children born with a silver spoon, it augured a future full of happy omens. Users became the fuel for the new paradigm based on participation and horizontality, and the engine was the companies that created spaces where it could take place. The important thing was the information, the “content”.

As “viewers” became “users”, it became clear that the strategies that made money in traditional media could also make money in online environments. Online platforms and portals began selling brands to its users, just as television had sold brands to audiences. And just as television had segmented audiences based on market research and audiometers, web companies used cookies, the profiles created by users when they sign up to use their services, and their data footprints in general. The digital environment started to convince users through performativity, just as television tried to convince viewers through seduction.

The Datified User

The characteristics users have changed in line with the changes in the corporate, technical, institutional, and social fields. We could say that two main trends have shaped what we know now generally refer to as the “user”. On one hand, in the age of Big Data everything around us has become a potential source of information, and many aspects that were left up to human judgement are now augmented or replaced by computerised systems. On the other hand, mobile communication offers myriad internet-connected devices that allow us to interact with each other and with the world. As Viktor Mayer-Scönberger and Kenneth Cukier argue, this has changed basic aspects of our lives and given them an unprecedented quantitative dimension. As a result a new layer of meaning was added to the idea of the participatory user that became widespread with Web 2.0.

Against this backdrop, graphic user interfaces, which form part of virtually all our devices, become – to paraphrase Lev Manovich – cultural objects that permeate all areas of contemporary societies, creating frameworks for monitoring, representation, simulation, analysis, decision-making, memory, vision, writing, and interaction. These interface-mediated processes are part of the so-called “algorithm society”, in which the idea of machine learning encourages constant monitoring (through geolocation, the use of apps that require access to contact details, the use of online services and networks, city sensorization, etc.), which is described as a collateral effect and a necessary step for reaching the next level of comfort and efficiency. This approach takes for granted that the more machines know about us, the better they will serve us.

Optimization + Operability

This process is what has turned our omnipresent interfaces into spaces for potential datified interaction (2). Algorithms, which are supposed to compile, classify, predict and manage information, passively monitor our use of interfaces. In the context of hyper-accelerated capitalism, algorithms – which respond to the interests of the system they form part of – attempt to optimize a system that tends towards simplicity and operability for users, and profitability for the companies behind them (7). To this end, digital environments, always in Beta, begin operating in a way akin to what Fuller and Goffey have called a Pavlovian experiment on large-scale, constantly adjusting to the way they are used according to the collected data.

An example is AppPulse Mobile, a web-native software for designers and developers that monitors the use of services in which it is implemented, monitoring their operation in real time – from the moment in which a user’s finger first glides on a screen to the moment he goes offline. This allows programmers to identify the problems that affect the users or functionality of an application, to discover what fails on a particular device, operating system, or app version, and to fix errors before they affect the “end user experience” (and before the company that has contracted their services finds out).

Going back to Fuller and Goffey, these testing techniques support, amplify, and confirm the production of the user as a behavioural unit and provider of quantifiable test data, placing him in an unwitting, unconscious situation of agency in regard to the interface and the services it enables (14).

Surveillance

As well as monitoring geared towards the performative optimization of the interface, user datification is also used to produce consumer profiles. Through algorithms, data collection and ongoing processing can reveal overall patterns in regard to the types of users interested in a particular product, so that companies can adapt the content or try to attract other types of user profiles. Similarly, the general characteristics of groups of users can be sold to polling and marketing surveys.

When data is aggregated, the features of individual users blur. This is why there are also other uses for interface surveillance at the individual level. Depending on privacy conditions, one of these other uses could be collecting information for mass surveillance by governments, as Edward Snowden’s leaks in June 2013 showed.

Another more obvious use of individually-generated data is custom advertising to match user profiles. And another, perhaps more important, use is the burgeoning personal assistant market. This particularly interesting case shows how algorithms try to learn and predict user behaviour.

For instance, Google Now, a personal assistant developed for mobile devices, collects information extracted from our use of different Google services (Gmail, Google+, Google Maps, Chrome, Google Calendar, etc.) and combines the different data to identify our patterns and preferences. Based on these patterns, it offers personalised recommendations of events, products, and routes, and even lets us know what time we should leave home to catch a flight or go to work, without us asking it to.

This case illustrates the extent to which users feed data systems through the interfaces they use, providing detailed information about their everyday lives.

Interface as Infrastructure

Interfaces are data collection points that involve the interests of several actors. But the means by which this takes place are not clear. By mediating and simplifying technical operations, interfaces produce black boxes and their expedient opacity hides behind an illusion of transparency and user-friendly design (9)(10)(12).

Today’s interfaces, which are becoming increasingly ubiquitous in our lives, are designed to reduce user friction. In a quest to achieve this, the field of user experience design prioritises interface design that seeks efficiency of gesture and thought. And this requires monitoring geared towards optimization. But paradoxically, this contradicts a phenomenon noted by Olia Liana: the gradual disappearance of the term “user” in the discourse of designers and developers. By calling users “persons” they are naturalized (and neutralized) and the computational component is eliminated. But at the same time, they are subjected to surveillance that is programmed into the algorithms. This is one factor that contributes to reducing the conscious agency of interface users. The irrefutable tendency to make all graphic interfaces as usable, engaging, simple, and social as possible is an expression of the values inherent to the discourse of Silicon Valley and global technoculture elites.

Interfaces as infrastructure incorporate these narratives and exert enormous power in their apparent neutrality. Because of their necessary, ubiquitous mediation, interfaces go unnoticed, as if they were an ambient factor. But the truth is that as users we delegate many different tasks to them, and through this delegating we give them a great deal of control when it comes to making all kinds of decisions.

As Pierre Lévy argues, intelligence technologies – including computing and its interfaces – affect the way we think and behave, and our habits. This is where interfaces – not just as systems of representation, but also as matter – exercise agency on users, in many different forms.

Metrification as (and of) Performativity (15)

In their graphic form, algorithms invite those who interact with them to adopt their own logic, and this often leads to standardization based on their own pre-defined objectives (11). This can be seen in the growing quantification of online life, and in how the importance of metrics as standards of measurements is introducing the world of business management into the private realm.

Artist and composer Ben Grosser talks about how social networks, particularly Facebook, enable certain conditions of performativity that stimulate what he calls the “business ontology” and “audit culture”. In this way users, focusing particularly on the amount of “likes”, friends, or comments on their own pages and those of others create a “graphopticon”, an audit practiced on themselves, where many users monitor the activity metrics of many others.

This quantifying dynamic is fuelled by and becomes integrated in the habits of users, at least partially, through gamification strategies. As Daphne Dragona shows, every move on social media is measured and every post awaits response, creating a particular field of action in which content constantly competes with other content for attention. These points systems, she says, depend on algorithms on one hand, and on promptness and virtuosity on the other.

Expanding these dynamics to the world of interface design, we see how game dynamics configure performativities geared towards creating a situation of soft conflict in users, who must negotiate it with their prowess (13). Rewards based on quantification (or micro-satisfaction) fuel participation, opening up the opportunity for playful interaction, but also for more possibilities of exploitation and control. (6)

This is how business ontology seeps into social life through interfaces, leading - as Grosser puts it – to the audit culture or an increase in the use of observational measurement – way beyond its original use in financial administration – into the previously self-managed public sector.

As Dragona writes, it is no coincidence that gamification has reached users in the age of data-oriented culture and economy, when there is a desire to calculate and quantify everything not just by governments, companies and institutions, but also by users and citizens themselves.

This dynamic should be considered within a scenario that is very well illustrated by Mayer-Scönberger and Cukier, in which data become an important corporate asset, a vital economic factor, and the basis for new economic models. Which is why the more measurable our living habits become, the easier it will be to monetize them.

Taking all of this into account, Fuller and Goffey say that we are not aware of the extent to which behaviour has been transformed into economics. In the age of datification, the interface acts as an infrastructure that prescribes habits, a key position in the crystallisation and reproduction of an emergent state of affairs, where corporate interests materialize in hardware and software that make it possible to exist, think, and interact within a specific range of possibilities, but not others. This is where interfaces not only configure and normalise particular ways of being a user, but also of being itself, in the ontological sense.


References

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