Pith. sign in

REVIEW 2 cited by

Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1611.04135 v3 pith:53263QUJ submitted 2016-11-13 cs.CV

classification cs.CV
keywords arxivcriminalitydiscussionsmediaresearchsomeworkacademic
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In November 2016 we submitted to arXiv our paper "Automated Inference on Criminality Using Face Images". It generated a great deal of discussions in the Internet and some media outlets. Our work is only intended for pure academic discussions; how it has become a media consumption is a total surprise to us. Although in agreement with our critics on the need and importance of policing AI research for the general good of the society, we are deeply baffled by the ways some of them mispresented our work, in particular the motive and objective of our research.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    Reliability and faithfulness of post-hoc explanations do not suffice to support claims about how a scientific phenomenon is structured.

  2. The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?

    stat.ML 2024-11 conditional novelty 4.0 of 10

    Machine learning and deep learning applications can repeat the errors of physiognomy and Lombrosianism because they treat correlations as causal, and bias reduction alone does not fix this.

Pith tools