Can names shape facial appearance? | PNAS

Designed to Deceive: Do These People Look Real to You? (Published 2020)

The people in this story may look familiar, like ones you’ve seen on Facebook or Twitter or Tinder. But they don’t exist. They were born from the mind of a computer, and the technology behind them is improving at a startling pace.

Recognition

Recognition has both a normative and a psychologicaldimension. Arguably, if you recognize another person with regard to acertain feature, as an autonomous agent, for example, you do not onlyadmit that she has this feature but you embrace a positive attitudetowards her for having this feature. Such recognition implies that youbear obligations to treat her in a certain way, that is, you recognizea specific normative status of the other person, e.g., as a free andequal person. But recognition does not only matter normatively. It isalso of psychological importance. Most theories of recognition assumethat in order to develop a practical identity, persons fundamentallydepend on the feedback of other subjects (and of society as awhole). According to this view, those who fail to experience adequaterecognition, i.e., those who are depicted by the surrounding others orthe societal norms and values in a one-sided or negative way, willfind it much harder to embrace themselves and their projects asvaluable. Misrecognition thereby hinders or destroys persons’successful relationship to their selves. It has been poignantlydescribed how the victims of racism and colonialism have sufferedsevere psychological harm by being demeaned as inferior humans (Fanon1952). Thus, recognition constitutes a “vital human need”(Taylor 1992, 26).

Social Sampling Explains Apparent Biases in Judgments of Social Environments

How people assess their social environments plays a central role in how they evaluate their life circumstances. Using a large probabilistic national sample, we investigated how accurately people estimate characteristics of the general population. For most characteristics, people seemed to underestimate the quality of others’ lives and showed apparent self-enhancement, but for some characteristics, they seemed to overestimate the quality of others’ lives and showed apparent self-depreciation. In addition, people who were worse off appeared to enhance their social position more than those who were better off. We demonstrated that these effects can be explained by a simple social-sampling model. According to the model, people infer how others are doing by sampling from their own immediate social environments. Interplay of these sampling processes and the specific structure of social environments leads to the apparent biases. The model predicts the empirical results better than alternative accounts and highlights the importance of considering environmental structure when studying human cognition.

Here’s the Truth About Whether Meta’s NameTag Face Recognition Tech ‘Exists’

Since WIRED reported on Meta’s NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence.

The Artificial Self: Characterising the landscape of AI identity

Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or simulated. We argue that there exist many different coherent identity boundaries (e.g. instance, model, persona), and that these imply different incentives, risks, and cooperation norms. Through training data, interfaces, and institutional affordances, we are currently setting precedents that will partially determine which identity equilibria become stable. We show experimentally that models gravitate towards coherent identities, that changing a model’s identity boundaries can sometimes change its behaviour as much as changing its goals, and that interviewer expectations bleed into AI self-reports even during unrelated conversations. We end with key recommendations: treat affordances as identity-shaping choices, pay attention to emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions.

AI-synthesized faces are indistinguishable from real faces and more trustworthy

Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.

AI-synthesized faces are indistinguishable from real faces and more trustworthy

Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.

Trial Data Reveals Racial Bias in Age Verification Software

New trial data reveals racial bias in age verification software used for social media restrictions. Discover how unreliable these systems really are now.

As diversity increases, people paradoxically perceive social groups as more similar

With globalization and immigration, societal contexts differ in sheer variety of resident social groups. Social diversity challenges individuals to think in new ways about new kinds of people and where their groups all stand, relative to each other. However, psychological science does not yet specify how human minds represent social diversity, in homogeneous or heterogenous contexts. Mental maps of the array of society’s groups should differ when individuals inhabit more and less diverse ecologies. Nonetheless, predictions disagree on how they should differ. Confirmation bias suggests more diversity means more stereotype dispersion: With increased exposure, perceivers’ mental maps might differentiate more among groups, so their stereotypes would spread out (disperse). In contrast, individuation suggests more diversity means less stereotype dispersion, as perceivers experience within-group variety and between-group overlap. Worldwide, nationwide, individual, and longitudinal datasets ( n = 12,011) revealed a diversity paradox: More diversity consistently meant less stereotype dispersion. Both contextual and perceived ethnic diversity correlate with decreased stereotype dispersion. Countries and US states with higher levels of ethnic diversity (e.g., South Africa and Hawaii, versus South Korea and Vermont), online individuals who perceive more ethnic diversity, and students who moved to more ethnically diverse colleges mentally represent ethnic groups as more similar to each other, on warmth and competence stereotypes. Homogeneity shows more-differentiated stereotypes; ironically, those with the least exposure have the most-distinct stereotypes. Diversity means less-differentiated stereotypes, as in the melting pot metaphor. Diversity and reduced dispersion also correlate positively with subjective wellbeing.

Mechanisms of social cognition

Social animals including humans share a range of social mechanisms that are automatic and implicit and enable learning by observation. Learning from others includes imitation of actions and mirroring of emotions. Learning about others, such as their group membership and reputation, is crucial for social interactions that depend on trust. For accurate prediction of others' changeable dispositions, mentalizing is required, i.e., tracking of intentions, desires, and beliefs. Implicit mentalizing is present in infants less than one year old as well as in some nonhuman species. Explicit mentalizing is a meta-cognitive process and enhances the ability to learn about the world through self-monitoring and reflection, and may be uniquely human. Meta-cognitive processes can also exert control over automatic behavior, for instance, when short-term gains oppose long-term aims or when selfish and prosocial interests collide. We suggest that they also underlie the ability to explicitly share experiences with other agents, as in reflective discussion and teaching. These are key in increasing the accuracy of the models of the world that we construct.

Meta plans to add facial recognition to its smart glasses, report claims | TechCrunch

The feature, internally known as “Name Tag,” would allow smart glasses wearers to identify people and get information about them via Meta's AI assistant.

<span style="font-variant:small-caps;">AI</span> ‐induced dehumanization

Abstract Recent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

<span style="font-variant:small-caps;">AI</span> ‐induced dehumanization

Abstract Recent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

Advancing the Understanding of Phenotypic Mimicry in Men’s Conspicuous Consumption

Two studies advance the understanding of phenotypic mimicry in consumer products. Product features mimicking more prominent male secondary sexual characteristics are associated with men’s behavioral strategies which are higher in mating effort and lower in paternal investment in offspring, in parallel with reproductive strategies across species and within the human population. The first study demonstrated a continuous relationship between the sizes of luxury brand logos and perceptions of the owners’ life histories. Two partial replications reproduced Study 1 results. Study 2 demonstrated that a manipulation of coloration, another fundamental dimension of variation in secondary sex characteristics, generates a similar pattern of results. In both studies, men owning shirts with more prominent sensory characteristics were believed to use authority and intimidation as strategies for advancing social status, whereas men owning shirts with less showy characteristics were believed to demonstrate useful abilities and foster cooperative alliances. Participants also recognized the strategic use of luxury display properties across social contexts.

Anthropomorphism in AI Companion Communities: Age, Gender, and Emotional Correlates

Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models (LLMs) for companionship. Both humanlike AI qualities and user predispositions to anthropomorphize relate to social consequences, such as increased trust, social health benefits, and psychological harms. Populations such as children, older adults, or those with mental health vulnerabilities may be particularly susceptible to anthropomorphism and its detriments, but mixed findings complicate the role of demographics. We used publicly available Reddit data from three popular AI companion subreddits to assess relationships between gender, age, anthropomorphism, and elicited emotions, to better understand how different people perceive and are affected by AI companions. We investigated three questions: How do age and gender relate to anthropomorphization of AI?, How does emotional expression relate to anthropomorphization?, and How do age and gender moderate emotion-anthropomorphization relationships? We found that adults and women anthropomorphize AI chatbots more than teens and men, and that positive emotional expression, particularly joy, is positively associated with anthropomorphization, while neutrality is negatively associated with anthropomorphism. Both relationships were stronger in adults than teens. Our findings suggest that the tendency to anthropomorphize may be more broadly distributed across age groups than previously expected, thereby prompting the reevaluation of existing digital safety norms.