Subjects: Psychology >> Social Psychology submitted time 2024-05-03
Abstract： The advancement of artificial intelligence (AI) technology significantly contributes to enhancing productivity; however, concerns regarding potential technological unemployment have garnered considerable attention. The uncertainty surrounding the occurrence, timing, and scale of AI-induced unemployment impedes definitive conclusions. This uncertainty may also lead the public to be influenced by encountered information concerning AI-induced unemployment. Media coverage on AI-induced unemployment often presents extensive information regarding affected industries, occupations, and probability scales, establishing two numerical information frameworks: one emphasizing factors influencing unemployment distribution across industries and another emphasizing the probability of unemployment occurrence. Comparatively, the probability framework, as opposed to the factor framework, allows individuals to formulate judgments indicating a reduced likelihood of AI-induced unemployment, thereby mitigating the perceived threat of AI, especially among individuals with high ambiguity tolerance. Building upon the foundational assumption that the probability framework alleviates AI threat perception, this study, comprising seven recursive experiments, investigates the mediating role of judgments on AI-induced unemployment likelihood and the moderating role of individual ambiguity tolerance. Experiment 1 juxtaposes AI threat perception elicited by general AI-induced unemployment descriptions, factor frameworks, and probability frameworks. Experiment 2 validates the mediating role of likelihood judgments. Experiments 3 and 4 respectively eliminate potential influences of probability values and unemployment scale. Experiment 5 explores ambiguity tolerance’s moderating effect. Experiments 6 and 7 examine subsequent AI threat effects, including support for AI development policies and willingness to recommend various occupations. The primary findings are as follows. Firstly, introducing AI-induced unemployment through a probability framework effectively diminishes AI threat perception (Experiments 1-7). Secondly, this effect is mediated by perceived likelihood, whereby the probability framework prompts individuals to form judgments indicating decreased AI-induced unemployment likelihood, thus reducing AI threat (Experiments 2-5). Thirdly, the information framework effect is moderated by ambiguity tolerance, primarily manifesting among individuals tolerant of ambiguous information (Experiment 5). Fourthly, individuals influenced by the probability framework demonstrate increased support for policies related to AI development, with AI threat playing a mediating role (Experiment 6). Lastly, individuals influenced by the probability framework exhibit a heightened willingness to recommend jobs involving frequent AI interaction (Experiment 7). This study extends prior research by elucidating how external factors such as information frames contribute to variations in AI threat perception. Unlike the extensively studied valence information frame, numerical information frames impact AI threat perception by altering individuals’ likelihood judgments. Our findings shed light on the effects of the numerical information framework on AI-induced unemployment threat perception, policy support, and job recommendation willingness.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： With the advantages of objectivity, accuracy, high speed and low cost, algorithmic decision-making has been widely used in human daily life, such as medical, judicial, recruitment and transportation situations. How will people react to the shift from traditional human decision-making to the newly emerged algorithmic decision-making? If people perceive algorithms as social actors, there would be no difference when faced with the same decision made by two different agents. However, researches show that algorithmic decision-making is more related to different responses in individuals than human decision-making on the same content. In other words, people will approach or avoid the same algorithmic decision-making, which is defined as the algorithmic decision-making approach and avoidance. Specifically, the algorithmic decision-making approach means that algorithmic decision-making is considered fairer, less biased, less discriminatory, more trustworthy, and more acceptable than human decision-making. But the algorithmic decision-making avoidance is the other way around. By analogy with the distinct ideologies when facing outgroup members, the process motivation model of algorithmic decision-making approach and avoidance simulates human psychological motivation when facing the same decisions made by algorithms and humans. Based on the premise that quasi-social interaction (relationship) and interpersonal interaction (relationship) develop parallel, the theory summarizes the three interaction stages between humans and algorithms. Namely, the interaction of initial behavior, the establishment of quasi-social relationships and the formation of identity. Furthermore, it elaborates how cognitional, relational, and existential motivation trigger individual approach and avoidance responses in each specific stage. More precisely, it occurs to meet the cognitive motivational needs to reduce uncertainty, complexity, and ambiguity in the interaction of the initial behavior stage, fulfill the relational motivational needs for establishing belonging and social identity in the establishment of the quasi-social relationship stage, and to satisfy the motivational needs for coping with threats and seeking security in the identity formation stage. In accordance with the three psychological motivations of cognition, relationship, and existence, the process motivational theory introduces six influencing factors, such as cognitive load, decision transparency, moral status, interpersonal interaction, reality threat and identity threat respectively. For future directions, we suggest that more researches are needed to explore how mind perception and intergroup perception influence algorithmic decision-making approach and avoidance. Meanwhile, what is the reversal process of the algorithmic decision-making approach and avoidance from a social perspective and what other possible motivations are associated with it are also worthy of consideration.
Subjects: Psychology >> Social Psychology submitted time 2022-07-22
Abstract： Algorithms are often used for decision-making. However, algorithmic decision-making is more related to different responses in individuals than human decision-making on the same content. The phenomenon is defined as the algorithmic decision-making approach and avoidance. The approach means that algorithmic decision-making is considered fairer, less biased, less discriminatory, more trustworthy, and more acceptable than human decision-making. But the avoidance is the other way around. To explain the phenomenon of the algorithmic decision-making approach and avoidance better, the process motivation model of algorithmic decision-making approach and avoidance is employed in the review. It summarizes three stages of the interaction between human and algorithm, namely, the interaction of initial behavior, the establishment of quasi-social relationship and the formation of identity. Moreover, it elaborates how cognitional, relational, and existential motivation trigger individual approach and avoidance responses in each specific stage. For future directions, we suggest that more researches are needed to explore how mind perception and intergroup perception influence algorithmic decision-making approach and avoidance. Meanwhile, what is the reversal process of algorithmic decision-making approach and avoidance from a more social perspective and what other possible motivations are associated with it are also worth of considered.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Social Psychology submitted time 2021-11-16
Abstract： Algorithm aversion refers to the phenomenon of people preferring human decisions but being reluctant to use superior algorithm decisions. The three-dimensional motivational model of algorithm aversion summarizes the three main reasons: the doubt of algorithm agents, the lack of moral standing, and the annihilation of human uniqueness, corresponding to the three psychological motivations, i.e., trust, responsibility, and control, respectively. Given these motivations of algorithm aversion, increasing human trust in algorithms, strengthening algorithm agents' responsibility, and exploring personalized algorithms to salient human control over algorithms should be feasible options to weaken algorithm aversion. Future research could further explore the boundary conditions and other possible motivations of algorithm aversion from a more social perspective. " "
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