arXiv subjects

Christoph Bartneck

Publications and source records attributed to Christoph Bartneck.

8 recordsLinked to original sources

arXiv2026

Turing's First Imitation Game: Design Concepts and a Human-Approximates-Machine Reading

This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games. Its first contribution is to identify and integrate the design concepts underlying the 1948 chess-based imitation game: the possibility that intelligent machines may make mistakes, the exclusion of irrelevant physical features, the role of the human judge, and Turing's claim that intellectual activity consists mainly of search. The paper's second contribution is to argue that restricting the human contestant to a rather poor chess player increases the role of intellectual search and makes human behaviour more comparable to machine behaviour. This interpretation presents the 1948 game as a human-approximates-machine game and suggests that the imitation game framework can be used not only to ask whether machines imitate humans, but also to examine when human intelligence becomes machine-like under specific task constraints.

cs.HC↗

arXiv2026

Ethical Asymmetry in Human-Robot Interaction - An Empirical Test of Sparrow's Hypothesis

The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialism, and virtue ethics. We conducted a mixed within/between experiment to investigate Sparrow's proposed ethical asymmetry hypothesis in human treatment of robots. The moral permissibility of action (MPA) was manipulated as a subject grouping variable, and virtue type (prudence, justice, courage, and temperance) was controlled as a within-subjects factor. We tested moral stimuli using an online questionnaire with Perceived Moral Permissibility of Action (PMPA) and Perceived Virtue Scores (PVS) as response measures. The PVS measure was based on an adaptation of the established Questionnaire on Cardinal Virtues (QCV), while the PMPA was based on Malle et al. [39] work. We found that the MPA significantly influenced the PMPA and perceived virtue scores. The best-fitting model to describe the relationship between PMPA and PVS was cubic, which is symmetrical in nature. Our study did not confirm Sparrow's asymmetry hypothesis. The adaptation of the QCV is expected to have utility for future studies, pending additional psychometric property assessments.

cs.HC↗

arXiv2026

A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence

Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely. Passing the test holds significance as evidence that a machine demonstrates human-like intelligence, and as a marker for artificial-general intelligence in commercial and legal domains. We conducted Turing's three-player imitation game with an LLM by following the guidelines identified by Turing and applying scientific standards wherever detailed instructions were missing. We performed a computer-imitates-human game without duration constraints and a man-imitates-woman game as a benchmark. In the computer-imitates-human game, only one participant misidentified the large language model, indicating that claims of large language models' passing the Turing test are premature. Participants required over five minutes for both tasks, with the man-imitates-woman game taking longer; shorter time limits elsewhere may explain earlier positive results. We expect the Turing Test to remain central in assessing machine intelligence for years to come.

cs.HC↗

arXiv2026

Deferred Acceptance Algorithm Improves Peer Review Process

The peer review process is essential to the success of science, but it also delays publications and absorbs considerable effort. Journals find it increasingly difficult to recruit competent reviewers. This study presents the results of agent-based simulation that models the current peer review process. We compared it to the simulation of a new peer review process that uses the Deferred Acceptance Algorithm (DAA) to match papers to journals. The matches are just as good while dramatically reducing the required number of reviews and delays. The results show that it is possible for the scientific community to significantly optimise the peer review process.

cs.DL↗

arXiv2025

People Perceive More Phantom Costs From Autonomous Agents When They Make Unreasonably Generous Offers

People often reject offers that are too generous due to the perception of hidden drawbacks referred to as "phantom costs." We hypothesized that this perception and the decision-making vary based on the type of agent making the offer (human vs. robot) and the degree to which the agent is perceived to be autonomous or have the capacity for self-interest. To test this conjecture, participants (N = 855) engaged in a car-buying simulation where a human or robot sales agent, described as either autonomous or not, offered either a small (5%) or large (85%) discount. Results revealed that the robot was perceived as less self-interested than the human, which reduced the perception of phantom costs. While larger discounts increased phantom costs, they also increased purchase intentions, suggesting that perceived benefits can outweigh phantom costs. Importantly, phantom costs were not only attributed to the agent participants interacted with, but also to the product and the agent's manager, highlighting at least three sources of suspicion. These findings deepen our understanding of to whom people assign responsibility and how perceptions shape both human-human and human-robot interactions, with implications for ethical AI design and marketing strategies.

cs.HC↗

arXiv2024

Too good to be true: People reject free gifts from robots because they infer bad intentions

A recent psychology study found that people sometimes reject overly generous offers from people because they imagine hidden ''phantom costs'' must be part of the transaction. Phantom costs occur when a person seems overly generous for no apparent reason. This study aims to explore whether people can imagine phantom costs when interacting with a robot. To this end, screen or physically embodied agents (human or robot) offered to people either a cookie or a cookie + \$2. Participants were then asked to make a choice whether they would accept or decline the offer. Results showed that people did perceive phantom costs in the offer + \$2 conditions when interacting with a human, but also with a robot, across both embodiment levels, leading to the characteristic behavioral effect that offering more money made people less likely to accept the offer. While people were more likely to accept offers from a robot than from a human, people more often accepted offers from humans when they were physically compared to screen embodied but were equally likely to accept the offer from a robot whether it was screen or physically embodied. This suggests that people can treat robots (and humans) as social agents with hidden intentions and knowledge, and that this influences their behavior toward them. This provides not only new insights on how people make decisions when interacting with a robot but also how robot embodiment impacts HRI research.

cs.HC↗

arXiv2023

Detecting The Corruption Of Online Questionnaires By Artificial Intelligence

Online questionnaires that use crowd-sourcing platforms to recruit participants have become commonplace, due to their ease of use and low costs. Artificial Intelligence (AI) based Large Language Models (LLM) have made it easy for bad actors to automatically fill in online forms, including generating meaningful text for open-ended tasks. These technological advances threaten the data quality for studies that use online questionnaires. This study tested if text generated by an AI for the purpose of an online study can be detected by both humans and automatic AI detection systems. While humans were able to correctly identify authorship of text above chance level (76 percent accuracy), their performance was still below what would be required to ensure satisfactory data quality. Researchers currently have to rely on the disinterest of bad actors to successfully use open-ended responses as a useful tool for ensuring data quality. Automatic AI detection systems are currently completely unusable. If AIs become too prevalent in submitting responses then the costs associated with detecting fraudulent submissions will outweigh the benefits of online questionnaires. Individual attention checks will no longer be a sufficient tool to ensure good data quality. This problem can only be systematically addressed by crowd-sourcing platforms. They cannot rely on automatic AI detection systems and it is unclear how they can ensure data quality for their paying clients.

cs.HC↗

arXiv2017

The Relationship Between Emotion Models and Artificial Intelligence

Emotions play a central role in most forms of natural human interaction so we may expect that computational methods for the processing and expression of emotions will play a growing role in human-computer interaction. The OCC model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This study reflects on the limitations of the OCC model specifically, and on the emotion models in general due to their dependency on artificial intelligence.

cs.HC↗