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Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford. Until appropriate safeguards are in place, we need a moratorium on biometric technology that identifies individuals, says Kate Crawford. “These tools are harmful when they fail and dangerous when they work.”

The identifying or sensitive anatomical features in MR and CT images used in research raise patient privacy concerns when such data are shared. In order to protect human subject privacy, we developed a method of anatomical surface modification and investigated the effects of such modification on image statistics and common neuroimaging processing tools. Common approaches to obscuring facial features typically remove large portions of the voxels. The approach described here focuses on blurring the anatomical surface instead, to avoid impinging on areas of interest and hard edges that can confuse processing tools. The algorithm proceeds by extracting a thin boundary layer containing surface anatomy from a region of interest. This layer is then “stretched” and “flattened” to fit into a thin “box” volume. After smoothing along a plane roughly parallel to anatomy surface, this volume is transformed back onto the boundary layer of the original data. The above method, named normalized anterior filtering, was coded in MATLAB and applied on a number of high resolution MR and CT scans. To test its effect on automated tools, we compared the output of selected common skull stripping and MR gain field correction methods used on unmodified and obscured data. With this paper, we hope to improve the understanding of the effect of surface deformation approaches on the quality of de-identified data and to provide a useful de-identification tool for MR and CT acquisitions.

From Quito to Nairobi, Moscow to Detroit, hundreds of municipalities have installed cameras equipped with FRT, sometimes promising to feed data to central command centres as part of 'safe city' or 'smart city' solutions to crime. [...]Lee Tien, a senior staff attorney at the Electronic Frontier Foundation in San Francisco, California, says that one of the main reasons large technology firms - whether in China or elsewhere - get involved in supplying AI surveillance technology to governments is that they expect to collect a mass of data that could improve their algorithms. The Russian capital rolled out a city-wide video surveillance system in January, using software supplied by Moscow-based technology firm NtechLab. In May, the chief executive of London's Heathrow airport said it would trial thermal scanners with facial-recognition cameras to identify potential virus carriers.

Four leading researchers share their concerns and solutions for reducing societal risks from intelligent machines.

The fields of computer science and artificial intelligence are struggling with the ethical challenges of biometrics. Researchers, funders and institutions must respond. The fields of computer science and artificial intelligence are struggling with the ethical challenges of biometrics. Researchers, funders and institutions must respond. Surveillance camera at Kings Cross Central

The technology is improving — but the bigger issue is how it’s used. The technology is improving — but the bigger issue is how it’s used.

Facial recognition plays a vital role in several security and law enforcement workflows, such as passport control and criminal investigations. The identification process typically involves a facial recognition system comparing an image against a large database of faces to return a list of probable matches, called a candidate list, for review. A human then looks at the returned images to determine whether there is a match. Most evaluations of these systems tend to examine the performance of the algorithm or human in isolation, not accounting for the interaction that occurs in operational contexts. To ensure optimal whole system performance, it is important to understand how the output produced by an algorithm can impact human performance. Anecdotal claims have been made by users of facial recognition systems that the images being returned by new algorithms in these systems have become more similar in appearance compared to old algorithms, making their job of determining the presence of a match more difficult. This paper explores whether these claims are true and whether the latest facial recognition algorithms decrease human performance compared to an old algorithm from the same company. We examined the performance of 40 novice participants on 120 face matching trials. Each trial required the participant to compare a face image against a candidate list containing eight possible matches returned by either a new or old algorithm (60 trials of each). Overall, participants were more likely to make errors when presented with a candidate list from a new algorithm. Specifically, they were more likely to misidentify an incorrect identity as a match. Participants were more accurate, confident, and faster on candidate lists from the older algorithm. These findings suggest that new algorithms are generating more plausible matches, making the task of determining a match harder for humans. We propose strategies to potentially improve performance and recommendations for future research. •We tested claims that new facial recognition algorithms make manual comparison harder.•Results show that performance was lower using a new algorithm compared to an old one.•Participants were less accurate, confident and slower when using the new algorithm.

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as “open science”; “gated science”; and “closed science.” No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior‐data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta‐analytic‐predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta‐analytic‐combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta‐analytic‐predictive prior, which is not available analytically. We propose two‐ or three‐component mixtures of standard priors, which allow for good approximations and, for the one‐parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy‐tailed and therefore robust. Further robustness and a more rapid reaction to prior‐data conflicts can be achieved by adding an extra weakly‐informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior‐data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof‐of‐concept trial with historical controls from four studies. Robust meta‐analytic‐predictive priors alleviate prior‐data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare. However, reliably identifying individual macaques in group environments remains a significant challenge. This study introduces ACE-YOLOX, a lightweight facial recognition model tailored for captive macaques. ACE-YOLOX incorporates Efficient Channel Attention (ECA), Complete Intersection over Union loss (CIoU), and Adaptive Spatial Feature Fusion (ASFF) into the YOLOX framework, enhancing prediction accuracy while reducing computational complexity. These integrated approaches enable effective multiscale feature extraction. Using a dataset comprising 179 400 labeled facial images from 1 196 macaques, ACE-YOLOX surpassed the performance of classical object detection models, demonstrating superior accuracy and real-time processing capabilities. An Android application was also developed to deploy ACE-YOLOX on smartphones, enabling on-device, real-time macaque recognition. Our experimental results highlight the potential of ACE-YOLOX as a non-invasive identification tool, offering an important foundation for future studies in macaque facial expression recognition, cognitive psychology, and social behavior.