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The Sociology of Artificial Intelligence: Explained

The Sociology of Artificial Intelligence (AI) is the application of machine intelligence techniques to social phenomena (Bainbridge et al, 1994). It includes methods of analysing social data that use Artificial Intelligence (AI) to further its work. With the advent of AI and consequent development, sociologists have engaged in the implementation of the same to understand social phenomena. Several researchers have adopted the issues of customary significance in Sociology, such as the combination of computational simulations through intellectual adaptive devices, the functions of organisations in influencing their managers and so on. Many scholars have also pointed out that artificial intelligence couldn’t be realised in its entirety without the help of social scientists. The first conference on Sociology and Artificial Intelligence was held in 1985 by Gilbert and Heath.

The Sociology of Artificial Intelligece

What is Artificial Intelligence?

Coppin defines artificial intelligence or AI as “the study —of systems that act in a way that to any observer would appear to be intelligent” (Coppin, 2004). It is used to solve both simple and complex issues within a shorter period. It effectively helps the computer system to improve with data and experience. The most fascinating aspect of AI is that it enables the electronic vehicle to learn from its mistakes and work on rectification of the same through the correct implication of efficient methods.

Read more about A.I

Society and Artificial Intelligence

The computational devices serve as agents in facilitating the solution to the problems in governmental and social change. AI has also strongly impacted the political scenario of the world. It has helped in the smooth restoration of the party-political science information and dissemination of the same. Theoretical assessments like text analysis, network examination, professional schemes and discovery of supplementary information have been possible through the use of AI. Its multi-agent calculation model makes it possible to bring together mental science and social accomplishment theory. Few examples of such models are the genomic systems, blocks, neural networks, and so on.

AI focuses on both macro as well as microstructures of the society. It effectively represents each component or individual of the society and works towards strengthening and improving their position by integrating them into the larger world and thereby impacting the overall organisational structures or social institutions. Therefore, artificial intelligence “intelligently intellectualises” the society into “synthetic vehicles” (Zhang, 2018). The non-human intelligent actors are increasingly integrated into our society with smart home systems, chatbots, and autonomous vehicles, and so on.

Moreover, AI-based social media platforms help in keeping social relations intact. It has been found that most of young people prefer to stay connected over social media rather than through face-to-face conversations. Bonds formed over the internet, supposedly, continue in real-life too. Hence, it keeps the individual socially connected and treats them as social beings. Apps driven by AI are also used by the crime bureau and investigation committees to monitor the activities of the masses and help in the prevention of cybercrime and so on. Hence, it effectively contributes to social control.

Emotional Impact

Traditional artificial intelligence which focused on isolated agents majorly contributed to introducing current mental psychology (Zhang, 2018). Initially, it was considered that AI would work just like a human but without any hint of emotions. Whereas jobs like that of engineers, builders, and so on would be replaced by it (which majorly has been), psychological work and social work that are embedded in human emotions would still be retained by humans. Is it so? Medical researchers claim that AI has efficiently acted as a catalyst in the field of healthcare and with proper research, it can revolutionize the world of psychotherapy.

Well-being applications like Replika and Wysa which offer cognitive behavioural therapy (driven by AI) for various mental illnesses have become increasingly popular in the past few years. People often resort to these apps to vent out their frustrations and anxieties, share their secrets and stories and open up without the fear of being judged. In other words, such apps act as perfect listeners. AI has also helped in the diagnosis of mental health disorders and made psychotherapy accessible and less challenging to both the seekers of therapy as well as the therapists. Due to several stigmas and taboos attached to mental health, people often refrain from visiting a therapist. Hence, such apps become their only solution and escape. The first AI for mental health care was invented in the 1960s, named as Eliza, to make people feel like they were speaking to another human therapist who would further respond with open-ended questions. It had also been observed that some AI apps can also spot suicide tendencies among their users and help prevent suicide.

Read: Mental Health and Disorders: Overview

Social Theories Inspired by AI

Fararo and Skvoretz (1984) claimed that AI concept of the production system can form the basis of a general theory about action structures and social institutions. Since production comprises a set of conditions demanding a particular action, social norms can be referred to as productions, and institutions and roles can be treated as production systems. The interrelationships of these played roles can be referred to as “rolegram”.

Carley (1989, 1991) built a constructuralist theory of social behaviour inspired by the symbolic processing’s mechanical cognitive model. This theory can make specific predictions about human behaviour and can be simulated on a computer. For instance, the development of AI-based apps for psychotherapy which has been discussed earlier in this article. Kontopoulos (1993) says that neural networks serve as perfect metaphors for understanding social structure.

Drawbacks

Research on AI is scarce compared to computational studies. Sociology has also placed less emphasis on cognition and focused on fields where the use of AI remains undeveloped (structural components). Similarly, computer scientists working on AI have blatantly ignored the social roots of human intelligence. Collins (1992) claimed that artificial intelligence cannot be achieved without help from sociologists.

Cultural aspects greatly impact and mimic human behaviour, but literature on AI doesn’t systematically represent culture. AI also fails to provide much attention to social simulation (Chai, 2004). Although AI emphasises on both macro as well as micro-level components, social theories on microstructures are largely underrepresented.

As machines become workhorses of humankind, more and more people become vulnerable to losing their livelihoods. Hence, although AI might help in economically advancing a country to a great level, it will widen the gaps between social classes and result in downward social mobility. This particular characteristic marks the era of capitalism where products (human-made technology) overshadow producers (humans) who produce them. Moreover, even with emotional integration, it still poses a question against the necessity of physicality or touches, because, at the end of the day, we are not just social but also biological beings.

References

  1. Bansraj, R. (2018). The Sociology of A.I. Sociology, Researchgate. Retrieved from https://www.researchgate.net/publication/328570876_The_Sociology_of_AI
  2. Carley, K. 1989. The value of cognitive foundations for dynamic social theory. Journal of Mathematical Sociology 14:171-208.
  3. Carley, K. 1991. A theory of group stability. Am. Sociol. Rev. 56:331-354.
  4. Chai, S. (2004). Artificial Intelligence and Social Theory: A One-Way Street? Perspectives, University of Hawaii, volume 27(4).
  5. Collins, R. 1992. Can sociology create an artificial intelligence? In Sociological Insight, pp. 155-184. New York: Oxford University Press.
  6. Coppin, B. (2004). Artificial Intelligence Illuminated. Mississauga. Canada: Jones and Bartlett Publishers.
  7. Fararo, T. J., Skvoretz, J. 1984. Institutions as production systems, Journal of Mathematical Sociology 10:117- 182.
  8. Kontopoulos, K. M. 1993. Neural networks as a model of structure. In The Logics of Social Structure, pp. 243-267. New York: Cambridge University Press.
  9. Mlynor, J., Verma, H. & Alavi, H. (2018). Towards a Sociological Conception of Artificial Intelligence. Researchgate. Retrieved from https://www.researchgate.net/publication/326525611_Towards_a_Sociological_Conception_of_A.I
  10. William Bainbridge, Edward Brent, Kathleen Carley, David Heise, Michael Macy, Barry Markovsky, John Skvoretz, 1994, “Artificial Social Intelligence.” Annual Review of Sociology, 20: 407-436.
  11. Zhang, Z. (2018). Artificial Intelligence Within Sociology at the Taft School. Advances in Social Science, Education and Humanities Research (ASSEHR), volume 300. 2018 International Workshop on Education Reform and Social Sciences (ERSS 2018). Retrieved from http://creativecommons.org/licenses/by-nc/4.0/
  12. Bol Magazine. (October, 2020). “Psychotherapy through Artificial Intelligence” by Spandana Datta. Retrieved from https://bolmagazine.com/2020/10/19/psychotherapy-through-artificial-intelligence/
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