Silicon is becoming sacred. Chatbots interpret scripture and channel the divine, and people treat AI like a religious advisor, seeking out counsel and moral advice. This brief guide explores how people make sense of the intelligence and sacredness of AI and God—and predicts what might happen when technology and God touch within our minds. We briefly explore concepts of mind perception and sacredness before empirically mapping various entities along the dimensions of Mind (intelligence) and Sacredness: God, AI, average human, priest, philosopher, crow, carrot, and robot. While God is seen as maximally intelligent and sacred, AI is seen as moderately intelligent and minimally sacred. However, current events show that AI is becoming more intertwined with the sacred, and we outline four futures for AI’s relationship with God: Messenger, Killer, Rival, and Spirit. We close with future directions and what sacred silicon might mean for how we perceive ourselves.

Human kindness, especially in relationships, revolves around exchanging empathy. Empathic AI—large language models (LLMs) that provide empathic support—will undermine human kindness by stopping people from 1) seeking empathy and 2) giving empathy. Asking humans for empathy is often an imposition and can be expensive (e.g., therapy). Also, the empathy people receive from humans is often unwelcome advice (rather than pure sympathy). Because LLMs are always available to provide consistent and unconditional compassion, “seekers” will rely on LLMs instead of their relationship partners. And when seekers rely on AI, “givers” will happily offload their empathic responsibilities to save themselves from this uncomfortable and emotionally costly work. With seekers and givers both relying on LLMs, the kindness created by exchanging empathy will disappear—unless humanity uses empathic AI only to reinforce human empathy, not replace it. The future of human kindness hinges on how we choose to use LLMs.

People differ in their belief that speech can cause lasting psychological harm. We present the ten-item Words Can Harm Scale (WCHS) as a valid and reliable measure of this belief. Items assess attitudes about harmful speech (e.g., "Vulnerable people should not be exposed to certain kinds of speech, as this might harm them") and written words (e.g., "I could be left emotionally scarred by something I read"). In a representative sample of U.S. adults (N = 956), the WCHS demonstrated strong internal consistency (α = .92) and robust two-week test-retest reliability (r = .80). People higher in the belief that words can harm tended to be younger, female, non-White, and politically liberal. People with higher WCHS scores rated themselves as higher in intellectual humility, empathy, moral grandstanding, and the belief in the importance of silencing others. They were also more likely to support political correctness and endorse trigger warnings and safe spaces. People who believed that words can harm had worse mental health: they reported being more anxious and depressed, less resilient, and having more difficulties in emotion regulation. The WCHS is a reliable tool for measuring beliefs about the harmfulness of words—a divisive issue within modern cultural discourse.

Moral and political conflicts revolve around victimhood —who is harmed, how severely, and even whether harm has occurred at all. Although humans have always cared about concrete interpersonal victimhood, the complexity of modern society creates many varieties of victimhood, which we organize along two dimensions: Scale (the number of victims) and Tangibility (the concreteness of victimization). These dimensions exist in both the mind and the world and are grounded in domain-general cognitive processes: epistemic trust and numerical cognition (scale), and ontological assumptions and linguistic scaffolding (tangibility). These dimensions help explain elements of political dynamics from polarization to misinformation, help map moral cognition to cognitive science, and suggest multiple directions for future research.

Interpersonal forgiveness significantly impacts well-being and relationships, but divine forgiveness in Christianity-how individuals perceive forgiveness from God-remains underexplored, despite its cultural and psychological importance. This research examined whether perceptions of God as unconditionally loving or morally concerned explain the causes and consequences of perceptions of divine and interpersonal forgiveness. Across four preregistered studies (N approximate to 2,000 U.S. Christians), participants evaluated scenarios of moral offenses or recalled personal transgressions. Participants rated divine and interpersonal forgiveness expectations (Studies 1-2) or introspection and growth intentions (Studies 3-4). We manipulated offender motives (good vs. bad), motive disclosure, and forgiver type (God vs. person). Analyses included multilevel models, regressions, and path analyses. In Studies 1-2, participants expected greater divine than interpersonal forgiveness. For others' offenses, God was seen as both unconditionally loving and morally concerned, sensitive to motives and disclosure. For their own offenses, participants perceived God as primarily unconditionally loving. In Study 3, participants expected others forgiven by God to introspect less but show more behavior change when motives were immoral. In Study 4, participants reported less motivation to improve after divine than interpersonal forgiveness when recalling their own immoral offenses. U.S. Christians see God as both loving and morally concerned for others but mostly as unconditionally loving for themselves. These mixed views explain how people judge others and their own actions before and after receiving forgiveness. Our findings echo Alexander Pope's truth: to err is human; to forgive-mostly unconditionally-is divine.

For a variety of reasons connected to the rise of the Internet and our current political environment, societies around the world are facing an existential crisis–a lack of empathy. Today, people are increasingly struggling to be empathetic to others in their communities and in society more broadly. In the current research we (a team of academic researchers and practitioners) attempt to tackle this problem of decreasing empathy by testing and evaluating a school-based intervention program developed by Narrative 4 in American high school classrooms (N=380, 10th and 11th graders) using an experimental approach (i.e., comparing Narrative 4 programming to a control condition). This 10-session program focused on building empathy, perspective taking, and active listening skills (among other skills) through narrative storytelling exercises. Results indicated the program was successful in promoting students’ reported empathy, perspective taking, curiosity to diverse ideas, and active listening skills as compared to the control condition. Additionally, this greater empathy led participants to report more pro-sociality (e.g., greater willingness to be civically engaged, more respect for others in their classroom and community more broadly) and less affective polarization towards those who disagree with them on politics. At the end of the program, participants also reported positive experiences with the program. Taken together, this research highlights the merits of Narrative 4 programming for promoting empathy and pro-sociality among high school students, the merits of academic and practitioner partnerships, and a promising narrative-based intervention for promoting empathy in society.

Moral psychology has long assumed that morality is a cooperative phenomenon that suppresses self-interest in the service of group welfare. But do people also use morality in self-interested ways? We argue that, although moral judgments must rely on cooperative principles, they are not always driven by impartial concern for cooperative welfare. Rather, people routinely invoke these same principles to advance their own interests, even as they genuinely experience those positions as principled. We refer to this tendency to deploy cooperative moral principles in the service of self-interest as strategic morality. We present a model of strategic morality, considering how distinct moral arrangements afford varying opportunities to different perceivers, and how perceptions of these affordances calibrate individuals’ moral positions in ways that serve their interests. We review evidence for our model across multiple domains of moral judgment, including mating strategies, economic interests, vulnerabilities, and kin interests, and examine how preferences for others’ moral character are similarly calibrated to self-interest. Finally, we consider how strategic morality complements rather than competes with existing approaches in moral psychology. Understanding morality as both a social ideal and a strategic instrument generates new hypotheses about how moral thought varies across individuals, cultures, and ecologies.

Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judgments are unstable across wording variations. We demonstrate simple prompting strategies that mitigate these limitations. Across two datasets–a U.S.-representative set of 144 moral scenarios and 38 moral beliefs from the International Social Survey Programme's Family and Changing Gender Roles module covering 32 countries–we show how simple elicitation techniques help improve AI-human alignment. First, prompting models to report standard deviations and response proportions recovers the full range of human responses better than common strategies. Second, ensuring scenarios are clear to human participants–as reflected in human confusion ratings–boosts model alignment, and LLMs can track human confusion ratings. At the same time, we find that LLMs' estimates of their own error are poorly calibrated, though they can predict human variability relatively well. These results suggest that asking better questions to LLMs can yield better answers.

Questions of legal blame and punishment hinge on judgments of victimhood: who is a victim and who is a victimizer? The law presumes that we judge victimhood objectively, but science shows that victimhood relies more on biased perceptions than impartial facts. The disconnect between victimhood in the law versus victimhood in our minds undermines the quest for justice. Here, we review three key psychological principles of victimhood and how they cause trouble in the judicial system. We then propose solutions to these challenges. Principle One: Victimhood is subjective-legal judgments hinge on who seems like a victim in our minds. Principle Two: Victimhood is stereotypical-who seems like a victim is biased, with people easily accepting the victimization of the vulnerable (e.g., children) but not the more powerful (e.g., strong men). Principle Three: Victimhood is sticky-once we identify the victim (or victimizer) in a situation, it is hard to change our minds. When the law fails to appreciate these principles, it causes distrust of verdicts, denials of true suffering, and continued condemnation of the exonerated. Potential solutions for these issues includes victim impact statements, restorative justice, and acknowledgments of legal errors. Understanding the true nature of victimhood is essential for a fair legal system.

Giving empathy is good, but providing emotional support is hard work. Empathic AI gives cheap and easy support, but may be ethically costly. When AI provides empathy, people may feel less compelled to support others. Four studies provide evidence for “empathy offloading:” when people see AI as capable of empathy, they become less willing to empathize with the suffering of others, even without realizing it. When AI seems more capable of empathy people—both with correlations (Study 1) and causal experiments (Study 2)—people report more empathy offloading. When people learn that someone has received emotional support from large language models (LLMs), they engage less with others’ mental states (Study 3) and offer fewer empathic responses (Study 4). Empathic AI might provide low-cost, easily accessible emotional support, but it risks fraying the ethical and emotional commitments we make to other humans. When someone can go cry to AI, why bother getting splashed with their tears?

In a world of deepfakes, generative AI, and ongoing debates over cultural appropriation, questions about what makes art “authentic” are newly urgent. We build on work from science and the humanities to offer a unified psychological account of perceived art authenticity . Across six studies, we find that people see art as more authentic when it appears to reveal a mind—a creator's lived experience, emotions, and intentions. The importance of “revealed mind” helps clarify central debates surrounding authenticity, explaining why replicas can sometimes feel authentic, and why audiences celebrate art that reflects an artist's lived experience and grow outraged when an artist tells someone else's story. Perceptions of revealed mind are especially important in the age of AI, explaining why AI-generated art feels inauthentic even when technically impressive. Together, our findings suggest that art feels most authentic when it evokes a sense of connection to the artist's mind.

AI makes it easier to work, and easier to harm. Six preregistered studies from two cultures (N = 1,210, in the United States and China) show that AI psychologically licenses harm. Past work has shown that delegating to AI increases cheating. We show that using AI causally increased the financial harm people inflicted on others, led people to endorse more harmful acts, and—crucially—led them to give more electric shocks to an innocent person. We argue this happens because AI obscures moral perception. Moral condemnation depends on detecting key situational elements, specifically an intentional agent causing damage to a suffering victim, and acting through AI weakens each part of that perception. It loosens the user's sense of agency, stretches the causal link to the outcome, and holds the victim out of view. Consistent with this account, the harm-licensing effect was amplified when people perceived AI as autonomous and disappeared when the victim was made vivid. These findings connect a growing literature on AI and dishonesty to a long-standing theory of moral cognition. AI does not create a new kind of wrongdoing. It engages an old one, obscuring the elements that moral cognition is built to detect.

Empathic Artificial Intelligence (AI) can be helpful—making people feel supported—but it has a dark side. When AI can provide emotional support, people become less willing to empathize with the suffering of others. Four studies reveal “empathy offloading:” when people see AI as capable of empathy, people save themselves the emotional labor of emotionally connecting with others. Higher perceptions of AI’s capacity for empathy both correlationally (Study 1) and causally (Study 2) increase empathy offloading. When people learn that someone has received emotional support from large language models (LLMs), they become less willing to empathize with them (Studies 3 and 4). Empathy offloading extends beyond simple scale ratings and includes reduced perspective-taking (Study 3) and offering fewer empathic responses (Study 4). Empathic AI might provide low-cost, easily accessible emotional support, but it risks fraying the ethical and emotional commitments to other human beings.

Moral disagreement across politics revolves around the key question, "Who is a victim?" Twelve studies explain moral conflict with assumptions of vulnerability (AoVs): liberals and conservatives disagree about who is especially vulnerable to victimization, harm, and mistreatment. AoVs predict moral judgments, implicit attitudes, and charitable behavior-and explain the link between ideology and moral judgment (usually better than moral foundations). Four clusters of targets-the Environment, the Othered, the Powerful, and the Divine-explain many political debates, from immigration and policing to religion and racism. In general, liberals see vulnerability as group-based, dividing the moral world into groups of vulnerable victims and invulnerable oppressors. Conservatives downplay group-based differences, seeing vulnerability as more individual and evenly distributed. AoVs can be experimentally manipulated and causally impact moral evaluations. These results support a universal harm-based moral mind (Theory of Dyadic Morality): moral disagreement reflects different understandings of harm, not different foundations.

Mind perception shapes human-AI interaction, yet there is a lack of comprehensive review for understanding the antecedents and outcomes of the mind perception of AI. This review analyzes 153 empirical studies from the past 20 years and synthesizes the key antecedents and outcomes of the mind perception of AI. We first identify human-related antecedents (e.g., demographics, personality traits, psychological conditions), AI-related factors (e.g., humanlike appearance, transparency, social identity cues, functions), and interaction-related factors (e.g., how humans interact with AI, how AI responds, interaction outcomes) that shape perceptions of AI’s agency and experience. These perceptions of AI’s agency and experience lead to cognitive, emotional, attitudinal, and behavioral consequences that are different from human-human interactions. By comparing findings across the dimensions of agency and experience, we offer nuanced insights into how people perceive AI’s mind and how these perceptions affect human-AI interactions. Finally, we suggest promising avenues for future research.

Expectation cues such as source labels, expertise signals, or identity-based indicators can bias how humans interpret and evaluate information. In high-stakes domains like healthcare, education, and law, such biases threaten the objectivity of decision-making. As LLMs increasingly provide decision support in these contexts, this study aims to examine whether LLMs exhibit expectation-driven bias akin to that of humans. Across two experiments (N = 1260), we manipulated expectations via priming statements and measured shifts in judgment scores. In both humans and LLMs, higher expectations led to more favorable evaluations for suggestions of equivalent quality, and greater mismatches between expectations and actual performance produced stronger judgment distortions. Notably, humans tended to adjust their evaluations unconsciously, whereas LLMs revised their outputs in a consistent and traceable manner. These findings reveal both shared sensitivities and distinct adjustment patterns, offering design insights for building expectation-aware AI systems that promote fair and transparent human–AI interaction.

People tend to morally prioritize their extremely close others (loved ones; such as family, partners, or friends) while disregarding extremely distant others (societal outcasts; such as pariahs, villains, or criminals). Across seven primary and 15 supplemental studies, we document how multicultural experiences might alter this universal default through moral compression: the simultaneous expansion of moral concern toward societal outcasts and contraction away from loved ones, effectively "compressing" the outermost and innermost anchors of the moral circle closer toward each other. Specifically, we find that greater breadth of living abroad experiences can compress the moral circle through heightened identification with all humanity, an association strengthened by cultural distance between home and host countries. Moral compression reflects a shift toward more impartial moral treatment under cognitive constraints. Whereas centuries of moral thought have idealized an ever-expanding moral circle, we draw from theories of bounded cognition and social identity abstraction to explore a costly trade-off of moral change. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

People readily moralize health, whether by criticizing smokers or treating exercise as noble. Drawing from the theory of dyadic morality, we theorized that people moralize health most strongly when they perceive poor health as a source of suffering. Through five studies (total N = 2,055), we show that perceived harm can drive the moralization of health. We identified three types of harm—personal, interpersonal, and collective—that people perceive as relevant to health and created a 15-item measure to capture each (Study 1). Perceived interpersonal harm reliably predicted moralizations of health, whether health was conceived broadly (Study 2) or as a concrete health issue (e.g., smoking, eating healthfully, disease prevention; Study 3). Experimentally manipulating the interpersonal harmfulness of a health behavior caused participants to moralize it (Studies 4 and 5), whereas disgust had no unique effect (Study 4). We suggest that perceived harm plays a key role in moralizing health.

Political polarization is driving disconnection and animosity between opponents in the United States. We propose perceiving opponents as self-disclosing helps foster connection and reduce animosity. Building on research demonstrating that self-disclosure fosters interpersonal relationships, we test whether vignettes expressing political views that seem self-disclosing increase connection, respect, and willingness to interact among opponents. Across six studies, we demonstrate self-disclosure reduces partisan animosity by building connection between political opponents. Previous work shows that vignettes about opponents’ personal experiences bridge divides better than fact-based vignettes. The results from the current research suggest this is because experiences are especially self-disclosing. Leveraging this, we find that many statements partisans share can improve connection and reduce animosity when they are perceived as self-disclosing. We test this through manipulating the self-disclosure of statements in experiments and by teaching people how to make their statements more self-disclosing. Results indicate self-disclosure is consistently effective for liberal and moderate partisans but in many cases are also effective for conservatives. By highlighting the power of self-disclosure, our findings offer a promising path toward bridging partisan divides.

Not many of us will try to marry a robot, but everyone interacts with machines. How does the human mind react to the rise of machines? This chapter will explore the psychology of the machines and technology transforming our modern world—especially robots and artificial intelligence.