A Socio-Technical Reaction to Allo
Today Google announced that they had developed Allo, a new messaging application for iOS and Android. Ingrained within Allo are AI and machine learning capabilities, as well as functionalities that compete directly with Amazon’s Alexa and iOS messenger. I personally think this application is extremely interesting and I’m looking forward to trying it out. Techcrunch has done a great preliminary analysis, here. As messaging applications, such as Allo, become more powerful allowing unique interactions between individuals, bots, other useful integrations there are interesting sociological and technical implications. I’d like to describe some of these socio-technical implications here so as to better analyze the underlying reason for the potential success of Allo.
Socio-technical components of an application are the distinct characteristics of both the software of an application and those of the people who are interacting with the technology. These components are useful when studying the outcomes of technology use and the potential for improvements. One particularly applicable example of socio-technical studies is in product management. As any product manager will tell you good applications are built with the users in mind. User interactions are interesting because they are sometimes unpredictable, and have extremely large effects on whether or not a product is successful. Many theories of product development such as the learn startup methodology and design thinking all posit that understanding human-technological interaction is key to long term company success. At the heart of these theories are many socio-technical assumptions about the nature of technology development and human behavior. These assumptions are often subtly woven into the product development without a designer even knowing. Therefore, having a greater understanding of socio-technical components of a product, such as Allo, can help a marketer or product manager understand the potential user experience with that application before its created.
What are these behaviors and assumptions, though? Well they encompass everything that relates to anyone’s use of a technology. For example, how often you use your phone to pay a credit card bill. The idea of using a thumbprint to securely make bank transactions is rooted in the idea that we value convenience and utility in technological use. Mobile applications were created to enable us to use technology in order to help satisfy our shifting behaviors and attitudes. Furthermore these shifting attitudes, as well as the increasing capabilities of technology represent the socio-technical elements of technology use. Our behaviors and decisions, as well as technological capabilities, are continuously affecting each other and producing certain outcomes.
The degree to which a company builds products within this framework varies; however, a messenger app like Allo will be especially sensitive to human behavior. Google has built this application with the belief that users are looking for a platform that allows them to do more than just communicate with one another. This includes search, payments, and other complex functionalities. All of these functions are enabled through communication, whether social or technical; therefore, these communications formulate the social experience of the users. Take an example of a user of Allo, who wants to create a group chat using google’s AI based chat bot. The ensuing communicative actions, which take place over a number of steps, formulate paradigms that are unique now, but will become increasingly common in the future. From the standpoint of a person these include communicating with the bot to notify other members of an intention to chat, actual person to person communication, or a group members use of one of the other functions of Allo to accomplish some non-communicative task. From the perspective of Allo, its built-in functionality not only provides the platform for these interactions to take place, but it also learns and adapts to the users behavior. These learning behaviors include language, but also search patterns, favorite contacts, and other patterns.
Some aspects of this interaction are not unique and have been studied with other bots including Alexa; however, the scale of what Google, and similarly Facebook Messenger bots, are trying to accomplish are very new. At the heart of these technological advancements are socio-technical behaviors between humans and machines that are forming new social behaviors. As machine learning becomes more prominent we will most likely become more inclined to focus on those specific behaviors that have been enshrined within the technology we use. For example, we may become inclined to check our email through Allo instead of a native email application. This will affect our the software developments and designs of Allo and similar applications in the future as much as it will change our email checking behavior. Sociological Technologists study these behaviors because they create paradigms that describe our daily experiences and interactions with other human beings. It is increasingly likely that communication with machine learning applications such as Allo will become one of these norms. For many years technologies have tried to encapsulate complete social experiences. It is possible that Google, with the announcement of Allo, has created an application that will define our social experiences for many years to come.