Prosocial behavior toward machines

Nielsen, Yngwie Asbjørn; Pfattheicher, Stefan; Keijsers, Merel

doi:10.1016/j.copsyc.2021.08.004

Cited by 17 publications

(8 citation statements)

References 46 publications

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“…The fact that the participants cooperate with their partners and punish their partners even when cooperation and punishment go against their financial interests, as well as the fact that the communication of emotions to the partners but not the private expression of emotions affected moral punishment, suggests that the present paradigm taps into mechanisms of social interactions. This is in line with recent experimental findings demonstrating that beliefs about the human versus preprogrammed nature of partners has surprisingly little effects on the behavior in economic games 87 , 88 . Nevertheless, it is of course an important goal for future research to explore whether the present conclusions hold in more ecologically realistic settings.…”

Section: Discussionsupporting

confidence: 91%

Communicating emotions, but not expressing them privately, reduces moral punishment in a Prisoner’s Dilemma game

Philippsen,

Mieth,

Buchner

et al. 2023

Sci Rep

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The existence of moral punishment, that is, the fact that cooperative people sacrifice resources to punish defecting partners requires an explanation. Potential explanations are that people punish defecting partners to privately express or to communicate their negative emotions in response to the experienced unfairness. If so, then providing participants with alternative ways to privately express or to communicate their emotions should reduce moral punishment. In two experiments, participants interacted with cooperating and defecting partners in a Prisoner’s Dilemma game. After each round, participants communicated their emotions to their partners (Experiments 1 and 2) or only expressed them privately (Experiment 2). Each trial concluded with a costly punishment option. Compared to a no-expression control group, moral punishment was reduced when emotions were communicated to the defecting partner but not when emotions were privately expressed. Moral punishment may thus serve to communicate emotions to defecting partners. However, moral punishment was only reduced but far from being eliminated, suggesting that the communication of emotions does not come close to replacing moral punishment. Furthermore, prompting participants to focus on their emotions had undesirable side-effects: Privately expressing emotions diminished cooperation, enhanced hypocritical punishment (i.e., punishment of defecting partners by defecting participants), and induced an unspecific bias to punish the partners irrespective of their actions.

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Section: Discussionsupporting

confidence: 91%

Communicating emotions, but not expressing them privately, reduces moral punishment in a Prisoner’s Dilemma game

Philippsen,

Mieth,

Buchner

et al. 2023

Sci Rep

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“…The previous paradigm was displaced by ML and deep learning due to the greater accessibility of digital data and computer power in the 1990s. With the first being model-driven and the second being data-driven, we can distinguish the second AI era from the third (Delipetrev et al , 2020; Nielsen et al , 2022).…”

Section: Theme Derivation Description and Progressionmentioning

confidence: 99%

“…ML techniques use artificial neural networks that are capable of learning complex patterns from data and decoding complex manufacturing patterns. It aids a variety of manufacturing tasks, such as intelligent inspection, predictive maintenance, quality improvement, process optimization, supply chain management and task scheduling (Aceta et al , 2022; Nielsen et al , 2022).…”

Section: Theme Derivation Description and Progressionmentioning

confidence: 99%

Human machine interactions: from past to future- a systematic literature review

Jain,

Gupta,

Temperini

et al. 2024

JMH

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Purpose This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs). Design/methodology/approach To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework. Findings Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions. Research limitations/implications There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today. Originality/value This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

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“…For decades, the relationship between computers and humans has been investigated in the context of anthropomorphization. The highly influential theoretical framework called Computers as Social Actors (CASA; Reeves & Nass, 1996; for a recent overview, see Nielsen et al, 2022) holds that humans expect computers to function in alignment with certain social rules, leading them to perceive technology as a human-like partner. These assumptions have been extended to AI systems (e.g., Tschopp et al, 2023) and to devices such as smartphones (e.g., Wang, 2017) and smartwatches (Makady, 2023).…”

Section: Anthropomorphization or Assistance?mentioning

confidence: 99%

Placebo or Assistant? Generative AI Between Externalization and Anthropomorphization

Skulmowski

2024

Educ Psychol Rev

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Generative AIs have been embraced by learners wishing to offload (parts of) complex tasks. However, recent research suggests that AI users are at risk of failing to correctly monitor the extent of their own contribution when being assisted by an AI. This difficulty in keeping track of the division of labor has been shown to result in placebo and ghostwriter effects. In case of the AI-based placebo effect, users overestimate their ability while or after being assisted by an AI. The ghostwriter effect occurs when AI users do not disclose their AI use despite being aware of the contribution made by an AI. These two troubling effects are discussed in the context of the conflict between cognitive externalization and anthropomorphization. While people tend to offload cognitive load into their environment, they also often perceive technology as human-like. However, despite the natural conversations that can be had with current AIs, the desire to attribute human-like qualities that would require the acknowledgment of AI contributions appears to be lacking. Implications and suggestions on how to improve AI use, for example, by employing embodied AI agents, are discussed.

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Prosocial behavior toward machines

Cited by 17 publications

References 46 publications

Communicating emotions, but not expressing them privately, reduces moral punishment in a Prisoner’s Dilemma game

Communicating emotions, but not expressing them privately, reduces moral punishment in a Prisoner’s Dilemma game

Human machine interactions: from past to future- a systematic literature review

Placebo or Assistant? Generative AI Between Externalization and Anthropomorphization

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