Elena Esposito (Milan 1960) is an Italian sociologist who works in the field of social systems theory. She teaches general sociology at Bielefeld University (Germany) and prediction and the future of public policy[1] at the University of Bologna (Italy). Her research is embedded in Luhmannian social systems theory.
Elena Esposito studied sociology at the University of Bologna (1983). At the same university she earned a Laurea in Philosophy under the supervision of Umberto Eco (1987). She earned a PhD in Sociology at the Bielefeld University with a thesis on the operation of observation in constructivism. Her PhD supervisor was Niklas Luhmann.[2]
Elena Esposito's research focuses on five main topics: General systems theory, Social memory, Fashion, Finance, and Algorithms and web.
Elena Esposito is Full Professor at the Fakultät für Soziologie[3] of the University Bielefeld and Full Professor at the Department of Political and Social Sciences of the University of Bologna. She was visiting scholar at the Max Planck Institute for the History of Science, Berlin (2017) and Niklas Luhmann Distinguished Visiting Chair in Social Theory, University Bielefeld (2015–16).[4] Elena Esposito is member of the editorial board of Sociologica (since 2014). She was member of the board of Soziale Systeme (2000–2006). She is also evaluator of the Deutscher Akademischer Austausch Dienst (DAAD) (since 2015) and advisor of the Zentrum für interdisziplinären Forschung (ZiF) of the University Bielefeld (since 2014). In 2019 Elena Esposito received an advanced grant by the European Research Council for the research project "The Future of Prediction: Social Consequences of Algorithmic Forecast in Insurance, Medicine and Policing"[5] (PREDICT - ERC-2018-ADG, n. 833749).
Source:[6]
In the introduction to her 2022 book Artificial Communication: How Algorithms Produce Social Intelligence, Elena Esposito characterizes the work as follows:
The book opens with a discussion on the adequacy of the classic metaphor of artificial intelligence, as well as derivatives such as neural networks, to analyze recent developments in digital technologies and the web. The latest generation of algorithms, which in various forms have given rise to the use of big data and related projects, does not try to artificially reproduce the processes of human intelligence. This, I argue, is neither a renunciation nor a weakness, but the basis of their incomparable efficiency in information processing and in their ability to interact with users. For the first time, machines are able to produce information never before considered by a human mind and act as interesting and competent communication partners—not because they have become intelligent; instead, it is because they no longer try to do so.[9]
