Who controls the controllers? That is a question that has become increasingly relevant as we progress into the Information Age and further into the Digital Era. The Social Technological Landscape, or STS for short, consists of those who control the digital processes – information as well as technical operations behind it. In terms of power dynamics, this landscape consists of three groups: people (ideally), machines, and ultimately algorithms. Algorithms are self-learning mathematical procedures that enable machines to autonomously make decisions based on incoming data. Technology will change society in a number of different ways, especially under the guidance of those who control it.
What determines your technology fate?
Artificial Intelligence will one day be capable of making much more complicated decisions than ever before. This will inevitably lead to a reduction in the role of human beings as decision-makers. Algorithms are already taking over many facets of our lives, such as online advertising and search engine rankings. However, artificial intelligence will eventually be able to control the infrastructure behind those algorithms as well as those algorithms themselves.
The Social Technological Landscape is a complex one with multiple actors involved who also influence each other’s roles and actions to a large extent – e.g., people running technology companies might prioritize profit maximization which may put their customers’ interests second, whereas an algorithm would not. The Social Technological Landscape is changing rapidly as technology advances, but there are worries that technological progress is not accompanied by social progress. The landscape must be carefully mapped out – an incorrect turn now could lead to serious societal and political problems in the future.
One of the most important things we can do to ensure a healthy STS environment is to scrutinize those who control it and hold them accountable for their actions. Machine learning already enables algorithms to learn from past behaviour; this should also extend itself to those who make algorithms and other decisions behind the scenes (e.g., Facebook’s algorithm). We must obtain a full representation of how decisions are made across all levels of society. One way to obtain such data would be through public records laws, which should be revised to include many new forms of decision-making.
Public records laws need revision in the age of social technologies. Public records laws are enacted at state or federal levels and give citizens access to information about their government, some of which must be provided upon request. These laws were developed in an era when governments made decisions with little external influence; today, they instead face complex situations where there are multiple actors who all seek to exert their will onto others by influencing some kind of outcome – e.g., elections, online behaviour, consumer markets.
Moreover, public records laws are also ineffective because they only apply within certain jurisdictions that are not required to provide all relevant information even if it exists elsewhere – e.g., citizens can’t ask for everything from California if their request is only for federal records.
When public records laws were initially developed, they didn’t anticipate a large role for social technological landscape actors in decision-making. Thus, a revision to include greater transparency of inner workings and decisions would be beneficial. Public records laws have been vital in holding government officials accountable because citizens have the power to influence them through voting or protesting.
In modern times, we should also hold private corporations accountable by demanding information about their operations through public records laws that enable outside scrutiny under certain circumstances – e.g., when it could harm society as a whole if such information was kept private or not made available upon request.
It remains unclear how this landscape will look like in the future but it is possible that AI systems that can write new algorithms without any human input could be on the horizon.