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

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.

References in corpus (3)

• Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
• Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
• Quality Adaptive Low-Rank Based JPEG Decoding with Applications

Cited by in corpus (6)

• The Ethics of AI in Education
• Pitfalls in Machine Learning Research: Reexamining the Development Cycle
• The AgeGuess database: an open online resource on chronological and perceived ages of people aged 3-100
• Facial Recognition: A cross-national Survey on Public Acceptance, Privacy, and Discrimination
• Automated Inference on Sociopsychological Impressions of Attractive Female Faces
• FusiformNet: Extracting Discriminative Facial Features on Different Levels