Decisions, Decisions: Big Data and the Future of Autonomy

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Fiona J. McEvoy


Members of the general public may think that terms like ‘Big Data’ are only of relevance to technology geeks and Silicon Valley executives. The reality is that so-called “datafication” marks the beginning of a new human epoch that will have huge implications for all of us – especially generations being born right now. Understanding the ethics of tech has never been more critical than it is today, and any comprehensive analysis should have one of the most apparent challenges right at its core: what Big Data means for our personal autonomy. Some commentators have already expressed nervousness. They are concerned that data-driven technology could lead to the erosion some of our human capacities as we relinquish more and more of our decision-making to computers.This paper attempts to frame this emerging concern, before articulating three ways in which an increasing emphasis on Big Data seems to threaten our basic liberty. I identify these as: i) data overload and automation, ii) feedback loops and manipulation, and iii) types and prejudice. I will then argue that, although these factors undoubtedly present a challenge to aspects of our decision-making (and so ethical concerns aren’t entirely misplaced), human autonomy itself is not in danger of being significantly destabilized. Rather, the rapid shift in perspective that characterizes this new era of data, intelligence and mass connectivity simply demands that we reimagine the objects, but not the conditions, of agent autonomy. I will suggest ways in which we might mitigate some of the more pernicious aspects of these developments, before ultimately concluding that new attitudes and new opportunities for decision-making are actually counteractively extending the domain of the autonomous human agent in positive ways.

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MCEVOY, Fiona J.. Decisions, Decisions: Big Data and the Future of Autonomy. Annals of the University of Bucharest - Philosophy Series, [S.l.], v. 66, n. 2, p. 43-66, feb. 2018. ISSN 0068-3175. Available at: <>. Date accessed: 11 aug. 2022.