The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Computer Vision Machine Learning and Future-Oriented Ethics

Computer Vision Machine Learning (CVML) in the application of facial recognition is currently being researched, developed, and deployed across the world. It is of interest to governments, technology companies, and consumers. However, fundamental issues remain related to human rights, error rates, and bias. These issues have the potential to create societal backlash towards the technology which could limit its benefits as well as harm people in the process. To develop facial recognition technology that will be beneficial to society in and beyond the next decade, society must put ethics at the forefront. Drawing on AI4People’s adaption of bioethics for AI, Luciano Floridi’s distributed morality framework, Kate Crawford’s definition of harms of representation, and Microsoft’s leadership in facial recognition ethics within the industry, this paper explores stakeholder responsibility within CVML to create the best integration of CVML for society. The paper attempts to connect ethics with praxis in making decisions related to CVML

Effective CCTV and the challenge of constructing legitimate suspicion using remote visual images

This paper compares the effectiveness of public CCTV systems according to meta-reviews, with what might be expected based upon theoretical predictions. The apparent gulf between practice and prediction is explored in the light of the challenges faced by CCTV operators in terms of effective target selection. In addition, counter-intuitive reactions by members of the public to situational symbols of crime deterrence may also undermine the efficacy of CCTV. Evidence is introduced and reviewed that suggests CCTV operators may employ implicit profiles to select targets. Essentially, young, scruffy males who appear to be loitering are disproportionately targeted compared with their base rate use of surveyed areas. However, the extent to which such a profile is diagnostic of criminal intent or behaviour is unclear. Such profiles may represent little more than ‘pattern matching’ within an impoverished visual medium. Finally, suggestions for future research and effective CCTV operator practice are offered in order to improve target selection.Peer reviewe

Physiognomic Artificial Intelligence

The reanimation of the pseudosciences of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. This Article—which contributes to critical data studies, consumer protection law, biometric privacy law, and antidiscrimination law—endeavors to conceptualize and problematize physiognomic artificial intelligence (“AI”) and offer policy recommendations for state and federal lawmakers to forestall its proliferation. Physiognomic AI, as this Article contends, is the practice of using computer software and related systems to infer or create hierarchies of an individual’s body composition, protected class status, perceived character, capabilities, and future social outcomes based on their physical or behavioral characteristics. Physiognomic and phrenological logics are intrinsic to the technical mechanism of computer vision applied to humans. This Article observes how computer vision is a central vector for physiognomic AI technologies and unpacks how computer vision reanimates physiognomy in conception, form, and practice and the dangers this trend presents for civil liberties. This Article thus argues for legislative action to forestall and roll back the proliferation of physiognomic AI. To that end, it considers a potential menu of safeguards and limitations to significantly limit the deployment of physiognomic AI systems, which hopefully can be used to strengthen local, state, and federal legislation. This Article foregrounds its policy discussion by proposing the abolition of physiognomic AI. From there, it posits regimes of U.S. consumer protection law, biometric privacy law, and civil rights law as vehicles for rejecting physiognomy’s digital renaissance in AI. Specifically, it contends that physiognomic AI should be categorically rejected as oppressive and unjust. Second, it argues that lawmakers should declare physiognomic AI unfair and deceptive per se. Third, it proposes that lawmakers should enact or expand biometric privacy laws to prohibit physiognomic AI. Fourth, it recommends that lawmakers should prohibit physiognomic AI in places of public accommodation. It also observes the paucity of procedural and managerial regimes of fairness, accountability, and transparency in ad- dressing physiognomic AI and attend to potential counterarguments in support of physiognomic AI