mlsanityData to 7 Oct 2026

Digital Voodoo Dolls

arXiv 2105.02738 ↗First posted 6 May 2021cs.CY cs.AI cs.HCcited by 4

Not on the map. Why?

Abstract (arXiv)

An institution, be it a body of government, commercial enterprise, or a service, cannot interact directly with a person. Instead, a model is created to represent us. We argue the existence of a new high-fidelity type of person model which we call a digital voodoo doll. We conceptualize it and compare its features with existing models of persons. Digital voodoo dolls are distinguished by existing completely beyond the influence and control of the person they represent. We discuss the ethical issues that such a lack of accountability creates and argue how these concerns can be mitigated.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 75 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

5 of 75 references.

References · 5

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2020Multi-Objective Counterfactual Explanationscited by 157
  4. 2017The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferencescited by 9
  5. 2016How smart does your profile image look? Estimating intelligence from social network profile imagescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2022Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other waycited by 3
  2. 2022Explainability for identification of vulnerable groups in machine learning modelscited by 1
  3. 2023Against Algorithmic Exploitation of Human Vulnerabilitiescited by 0
  4. 2026Understanding Privacy by Formalizing Itcited by 0