algorithmic governance
Algorithmic governance refers to the increasing use of computational systems, machine learning, and artificial intelligence to manage, regulate, and make decisions about various aspects of society. From a media studies perspective, algorithmic governance is a significant area of inquiry, as it raises important questions about the relationship between technology, power, and control.
One of the key concerns in media studies is the way in which algorithms shape and influence the flow of information. Algorithms are used to personalize and curate content, determine what information is relevant and what is not, and prioritize certain types of content over others. This can lead to the creation of "filter bubbles" or "echo chambers," where individuals are only exposed to information that reinforces their existing views and are shielded from opposing perspectives. This can have significant implications for democracy, civic engagement, and the public sphere.
Another area of concern is the lack of transparency and accountability in algorithmic governance. Algorithms are often opaque and difficult to understand, making it challenging to identify bias, error, or manipulation. This can lead to a lack of trust in institutions and a sense of powerlessness among individuals, who may feel that they are being controlled or manipulated by unseen forces.
Media studies scholars also examine the role of algorithms in shaping cultural production and consumption. Algorithms are used to recommend music, movies, and other forms of media, influencing what we watch, listen to, and engage with. This can lead to a homogenization of cultural production, as algorithms prioritize content that is likely to be popular or commercially successful over more niche or experimental work.
The use of algorithms in governance also raises important questions about the relationship between humans and machines. As machines become increasingly involved in decision-making processes, there is a risk that human judgment and agency will be eroded. This can lead to a loss of autonomy and a sense of disempowerment among individuals, who may feel that they are no longer in control of their own lives.
Furthermore, algorithmic governance is often tied to the logics of neoliberalism, which prioritize efficiency, productivity, and profit. This can lead to the exploitation of individuals and communities, as they are reduced to data points and optimized for maximum efficiency. Media studies scholars argue that this reflects a broader trend towards the "datafication" of society, where individuals are reduced to their data profiles and treated as commodities rather than human beings.
To address these concerns, media studies scholars are developing new methods and approaches for studying algorithmic governance. These include critical code studies, which involve analyzing the code and algorithms used in governance systems; data journalism, which involves using data analysis and visualization to uncover patterns and biases in algorithmic decision-making; and critical algorithmic literacy, which involves teaching individuals to read and understand algorithms in order to challenge and subvert them.
In conclusion, algorithmic governance is a significant area of inquiry in media studies, as it raises important questions about the relationship between technology, power, and control. Media studies scholars are concerned about the ways in which algorithms shape and influence the flow of information, the lack of transparency and accountability in algorithmic governance, and the exploitation of individuals and communities through datafication. By developing new methods and approaches for studying algorithmic governance, media studies scholars aim to challenge and subvert the dominant logics of algorithmic governance and promote a more just and equitable society.
References:
- Gillespie, T. (2014). The Relevance of Algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media Technologies: Essays on Communication, Materiality, and Society (pp. 167-194). MIT Press.
- boyd, d. (2017). The Ideology of Speed: How Technology Shapes Our Relationships with Time. Journal of Communication, 67(1), 1-13.
- Striphas, T. (2015). Algorithmic Culture. Journal of Visual Culture, 14(1), 3-14.
- Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Sage.
- Benkler, Y. (2011). Networks of Power, Degrees of Freedom