Disclosive ethics and information technology: disclosing facial recognition systems

This paper is an attempt to present disclosive ethics as a framework for computer and information ethics � in line with the suggestions by Brey, but also in quite a different manner. The potential of such an approach is demonstrated through a disclosive analysis of facial recognition systems. The paper argues that the politics of information technology is a particularly powerful politics since information technology is an opaque technology � i.e. relatively closed to scrutiny. It presents the design of technology as a process of closure in which design and use decisions become black-boxed and progressively enclosed in increasingly complex sociotechnical networks. It further argues for a disclosive ethics that aims to disclose the nondisclosure of politics by claiming a place for ethics in every actual operation of power � as manifested in actual design and use decisions and practices. It also proposes that disclosive ethics would aim to trace and disclose the intentional and emerging enclosure of politics from the very minute technical detail through to social practices and complex social-technical networks. The paper then proceeds to do a disclosive analysis of facial recognition systems. This analysis discloses that seemingly trivial biases in recognition rates of FRSs can emerge as very significant political acts when these systems become used in practice

The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1