Privacy without persons: a Buddhist critique of surveillance capitalism
Bibliographic record
Abstract
Abstract Much has been written about artificial intelligence (AI) perpetuating social inequity and disenfranchising marginalized groups (Barocas in SSRN J, 2016; Goodman in Law and Ethics of AI, 2017; Buolamwini and Gebru in Conference on Fairness, Accountability and Transparency, 2018). It is a sad irony that virtually all of these critiques are exclusively couched in concepts and theories from the Western philosophical tradition (Algorithm Watch in AI ethics guidelines global inventory, 2021; Goffi in Sapiens, 2021). In particular, Buddhist philosophy is, with a few notable exceptions (Hongladarom in A Buddhist Theory of Privacy, Springer, Singapore, 2016; Hongladarom in The Ethics of AI and Robotics A Buddhist Viewpoint, Lexington Book, Maryland, 2020; Hongladarom in MIT Technology Review, 2021; Lin et al. in Robot Ethics: The Ethical and Social Implications fo Robotics, MIT, Cambridge, 2012; Promta and Einar Himma in J Inf Commun Ethics Soc 6(2):172–187, 2008), completely ignored. This inattention to non-Western philosophy perpetuates a pernicious form of intellectual imperialism (Alatas in Southeast Asian J Soc Sci 28(1):23–45, 2000), and deprives the field of vital intellectual resources. The aim of this article is twofold: to introduce Buddhist concepts and arguments to an unfamiliar audience and to demonstrate how those concepts can be fruitfully deployed within the field of AI ethics. In part one, I develop a Buddhist inspired critique of two propositions about privacy: that the scope of privacy is defined by an essential connection between certain types of information and personal identity (i.e., what makes a person who they are), and that privacy is intrinsically valuable as a part of human dignity (Council of the European Union in Position of the Council on General Data Protection Regulation, 2016). The Buddhist doctrine of not self (anattā) rejects the existence of a stable and essential self. According to this view, persons are fictions and questions of personal identity have no ultimate answer. From a Buddhist perspective, the scope and value of privacy are entirely determined by contextual norms—nothing is intrinsically private nor is privacy intrinsically valuable (Nissenbaum in Theor Inq Law 20(1):221–256, 2019). In part two, I show how this shift in perspective reveals a new critique of surveillance capitalism (Zuboff in J Inf Technol 30(1):75–89, 2015). While other ethical analyses of surveillance capitalism focus on its scale and scope of illegitimate data collection, I examine the relationship between targeted advertising and what Buddhism holds to be the three causes of suffering: ignorance, craving and aversion. From a Buddhist perspective, the foremost reason to be wary of surveillance capitalism is not that it depends on systematic violations of our privacy, but that it systematically distorts and perverts the true nature of reality, instilling a fundamentally misguided and corrupting conception of human flourishing. Privacy, it turns out, may be a red herring to the extent that critiques of surveillance capitalism frame surveillance, rather than capitalism, as the primary object of concern. A Buddhist critique, however, reveals that surveillance capitalism is merely the latest symptom of a deeper disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".