Why faces don't always tell the truth about feelings

Complex faces and naïve machines A commentary on facial perceptions of age, gender, and leader preferences in the age of AI

Tasks driven by artificial intelligence (AI), such as evaluating video job interviews, rely on facial recognition systems for decision-making. Therefore, it is extremely important that the science behind this technology is continually advancing. If not, visual stereotypes, such as those associated with facial age and gender, will lead to dangerous misapplications of AI.

Emotional AI and the future of wellbeing in the post-pandemic workplace

This paper interrogates the growing pervasiveness of affect recognition tools as an emerging layer human-centric automated management in the global workplace. While vendors tout the neoliberal incentives of emotion-recognition technology as a pre-eminent tool of workplace wellness, we argue that emotional AI recalibrates the horizons of capital not by expanding outward into the consumer realm (like surveillance capitalism). Rather, as a new genus of digital Taylorism, it turns inward, passing through the corporeal exterior to extract greater surplus value and managerial control from the affective states of workers. Thus, empathic surveillance signals a profound shift in the ontology of human labor relations. In the emotionally quantified workplace, employees are no longer simply seen as physical capital, but conduits of actuarial and statistical intelligence gleaned from their most intimate subjective states. As a result, affect-driven automated management means that priority is often given to actuarial rather than human-centered managerial decisions.

What is missing in the study of emotion expression?

While approaching celebrations for the 150 years of "The Expression of the Emotions in Man and Animals", scientists' conclusions on emotion expression are still debated. Emotion expression has been traditionally anchored to prototypical and mutually exclusive facial expressions (e.g., anger, disgust, fear, happiness, sadness, and surprise). However, people express emotions in nuanced patterns and - crucially - not everything is in the face. In recent decades considerable work has critiqued this classical view, calling for a more fluid and flexible approach that considers how humans dynamically perform genuine expressions with their bodies in context. A growing body of evidence suggests that each emotional display is a complex, multi-component, motoric event. The human face is never static, but continuously acts and reacts to internal and environmental stimuli, with the coordinated action of muscles throughout the body. Moreover, two anatomically and functionally different neural pathways sub-serve voluntary and involuntary expressions. An interesting implication is that we have distinct and independent pathways for genuine and posed facial expressions, and different combinations may occur across the vertical facial axis. Investigating the time course of these facial blends, which can be controlled consciously only in part, is recently providing a useful operational test for comparing the different predictions of various models on the lateralization of emotions. This concise review will identify shortcomings and new challenges regarding the study of emotion expressions at face, body, and contextual levels, eventually resulting in a theoretical and methodological shift in the study of emotions. We contend that the most feasible solution to address the complex world of emotion expression is defining a completely new and more complete approach to emotional investigation. This approach can potentially lead us to the roots of emotional display, and to the individual mechanisms underlying their expression (i.e., individual emotional signatures).

Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace

Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology stems from advancements in affective computing. But whereas previous generations of biometric monitoring targeted the exterior physical body of the worker, concurrent with the writings of Foucault and Hardt, we argue that emotion-recognition tools signal a far more invasive disciplinary gaze that exposes and makes vulnerable the inner regions of the worker-self. Our paper explores attitudes towards empathic surveillance by analyzing a survey of 1015 responses of future job-seekers from 48 countries with Bayesian statistics. Our findings reveal affect tools, left unregulated in the workplace, may lead to heightened stress and anxiety among disadvantaged ethnicities, gender and income class. We also discuss a stark cross-cultural discrepancy whereby East Asians, compared to Western subjects, are more likely to profess a trusting attitude toward EAI-enabled automated management. While this emerging technology is driven by neoliberal incentives to optimize the worksite and increase productivity, ultimately, empathic surveillance may create more problems in terms of algorithmic bias, opaque decisionism, and the erosion of employment relations. Thus, this paper nuances and extends emerging literature on emotion-sensing technologies in the workplace, particularly through its highly original cross-cultural study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00146-021-01290-1.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

Questioning the EU proposal for an Artificial Intelligence Act: The need for prohibitions and a stricter approach to biometric surveillance

Artificial Intelligence (AI)-based surveillance technologies such as facial recognition, emotion recognition and other biometric technologies have been rapidly introduced by both public and private entities all around the world, raising major concerns about their impact on fundamental rights, the rule of law and democracy. This article questions the efficiency of the European Commission’s Proposal for Regulation of Artificial Intelligence, known as the AI Act, in addressing the threats and risks to fundamental rights posed by AI biometric surveillance systems. It argues that in order to meaningfully address risks to fundamental rights the proposed classification of these systems should be reconsidered. Although the draft AI Act acknowledges that some AI practices should be prohibited, the multiple exceptions and loopholes should be closed, and in addition new prohibitions, in particular to emotional recognition and biometric categorisation systems, should be added to counter AI surveillance practices violating fundamental rights. The AI Act should also introduce stronger legal requirements, such as third-party conformity assessment, fundamental rights impact assessment, transparency obligations as well as enhance existing EU data protection law and the rights and remedies available to individuals, thus not missing the unique opportunity to adopt the first legal framework that truly promotes trustworthy AI.

Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry

PURPOSE OF REVIEW

Emotion artificial intelligence (AI) is technology for emotion detection and recognition. Emotion AI is expanding rapidly in commercial and government settings outside of medicine, and will increasingly become a routine part of daily life. The goal of this narrative review is to increase awareness both of the widespread use of emotion AI, and of the concerns with commercial use of emotion AI in relation to people with mental illness.

RECENT FINDINGS

This paper discusses emotion AI fundamentals, a general overview of commercial emotion AI outside of medicine, and examples of the use of emotion AI in employee hiring and workplace monitoring. The successful re-integration of patients with mental illness into society must recognize the increasing commercial use of emotion AI. There are concerns that commercial use of emotion AI will increase stigma and discrimination, and have negative consequences in daily life for people with mental illness. Commercial emotion AI algorithm predictions about mental illness should not be treated as medical fact.

Macro- and Micro-Expressions Facial Datasets: A Survey

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro- and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application.

Diagnosis of Depressive Disorder Model on Facial Expression Based on Fast R-CNN

This study examines related literature to propose a model based on artificial intelligence (AI), that can assist in the diagnosis of depressive disorder. Depressive disorder can be diagnosed through a self-report questionnaire, but it is necessary to check the mood and confirm the consistency of subjective and objective descriptions. Smartphone-based assistance in diagnosing depressive disorders can quickly lead to their identification and provide data for intervention provision. Through fast region-based convolutional neural networks (R-CNN), a deep learning method that recognizes vector-based information, a model to assist in the diagnosis of depressive disorder can be devised by checking the position change of the eyes and lips, and guessing emotions based on accumulated photos of the participants who will repeatedly participate in the diagnosis of depressive disorder.