Current Opinion in Psychology最新文献

There is considerable enthusiasm for the potential of artificial intelligence (AI) to improve financial well-being. Despite this enthusiasm, it is important to underscore AI's potential adverse effects on consumers' financial decisions. We introduce the AI-IMPACT model, a unifying theoretical framework for how AI can influence consumers' financial decisions. The model details how AI impacts the marketplace, affecting psychological processes and consumer traits core to financial decision-making (e.g., pain of payment, financial literacy). We use the AI-IMPACT model to illustrate one way AI can reduce financial well-being as its influence on the marketplace (e.g., facilitating biometric payment methods) decreases consumers' pain of payment, increasing spending. Lastly, we use the AI-IMPACT model to identify areas for future research at the intersection of AI and financial decision-making.

This review presents a framework for understanding how consumers respond to artificial intelligence (AI) and related technologies, such as robots, algorithms, or chatbots. Drawing on a systematic review of the literature (N = 111), we describe how AI technologies influence a variety of consumer-relevant outcomes, including consumer satisfaction and the propensity to rely on AI. We also highlight the important role that consumer characteristics along with contextual characteristics (i.e., the micro and macro context) play in shaping the AI-consumer interaction. We then discuss novel theoretical perspectives that could shed light on the psychological processes triggered by AI-consumer interactions. We conclude by adopting a meta-scientific perspective and discussing how AI may change the process of scientific discovery.

Understanding human-artificial intelligence (AI) interactions is a growing academic interest. This article conceptualizes AI as a persuasion agent and reviews the recent literature on AI through the lens of persuasion knowledge. It presents research on AI acceptance and aversion in terms of the properties of the AI itself (e.g., anthropomorphism, functionality, and usability), the properties of individuals interacting with AI (e.g., individual differences in judgments of AI, perceived uniqueness, and task performance), and the context of the interaction (e.g., type of decision, domain, and usage occasion). In assessing AI interaction research through this lens, we systematically categorize these findings and identify promising future research directions.

This article briefly summarizes trust as a multi-dimensional construct, and trust in AI as a unique but related construct. It argues that because trust in AI is couched within an economic landscape, these two frameworks should be combined to understand the dynamics of trust in AI as it is currently implemented. The review focuses on healthcare and law enforcement as two industries that have adopted AI in ways that do and do not engender trust from stakeholders. The framework is applied to both industries to highlight where and why varying trust in AI is observed. Then seven research questions are posed, and researchers are encouraged to test the proposed framework in other AI-reliant contexts, like education and employment.

Contemporary, multidisciplinary research sheds light on data privacy implications of artificial intelligence (AI). This review adopts an AI ecosystem perspective and proposes a process-outcome continuum to classify AI technologies; this perspective helps to understand the nuances of AI relative to psychological aspects of privacy decision-making. Specifically, different types of AI affect traditionally studied privacy decision-making frameworks including the privacy calculus, psychological ownership, and social influence in varied ways. By understanding how the process- or outcome-orientation of an AI technology affects privacy decision-making, we explain how AI creates privacy benefits but also poses challenges. Future research is needed across privacy decision-making, but also more generally at the intersection of privacy and AI, to help foster an ethical, sustainable society.

Historically, the use of technology in organizations has reshaped the nature of human work. In this article, we overview how current waves of artificially intelligent (AI) technologies are following this trend, showing how its uses can both automate and complement human labor, alongside creating new forms of human work. However, AI can also generate both upsides and downsides for workers' experiences, which are dependent upon a range of factors such as how the technology is used and the support employees receive during digital transitions. We conclude by outlining how AI literacy and other human-centered skills will play an increasingly important role in future workplaces.

Race is directly or indirectly incorporated into many AI systems. These systems, which automate typically human tasks, are used across various domains such as predictive policing, disease detection, government resource allocation, and loan approvals. However, these tools have been criticized for handling race insensitively or inaccurately. Despite the prevalent use of race in these AI systems, it is often not properly defined. It is treated as an obvious concept and represented as fixed categories, which fail to fully incorporate the social meaning surrounding race. Thus, in this review article, we define race and discuss how it is represented in AI systems. We also explore the consequences of such representations and offer recommendations on how to incorporate race more appropriately in these systems.

Machine Learning (ML) affords researchers tools to advance beyond research methods commonly employed in psychology, business, and public policy studies of federal nutrition programs and participant food decision-making. It is a sub domain of AI that is applied for feature extraction – a crucial step in decision making. These features are used in context-specific automated decisions resulting in predictive AI models. Whereas many prior studies rely on retrospective, static, “one-shot” decision-making in controlled laboratory environments, ML allows researchers to refine predictions about participation and food behaviors using large-scale datasets. We propose a case study using ML to predict an aspect of participation in a large, publicly funded nutrition education program (The Expanded Food and Nutrition Education Program). Participation has important downstream implications for diet quality, food security, and other important nutrition related decisions. We then suggest a process for validating the ML insights using qualitative research and survey data.

Facial inference, a cornerstone of person perception, has traditionally been studied through human judgments about personality traits and abilities based on people's faces. Recent advances in artificial intelligence (AI) have introduced new dimensions to this field, employing machine learning algorithms to reveal people's character, capabilities, and social outcomes based just on their faces. This review examines recent research on human and AI-based facial inference across psychology, business, computer science, legal, and policy studies to highlight the need for scientific consensus on whether or not people's faces can reveal their inner traits, and urges researchers to address the critical concerns around epistemic validity, practical relevance, and societal welfare before recommending AI-based facial inference for consequential uses.

In this piece, we propose that entertainment media is an understudied source of misinformation and relationship science is an understudied domain of misinformation. We discuss two ways that relationship misinformation can appear in entertainment media – in the form of blatant claims and subtle content – and we provide an example of each from reality and entertainment television. We also propose an agenda for studying relationship misinformation and a set of questions to guide future research. We conclude by calling attention to the potential harms of such information on individuals and relationships.