Self-imposed Filter Bubble Model for Argumentative Dialogues

Aicher, Annalena; Kornmüller, Daniel; Minker, Wolfgang; Ultes, Stefan

doi:10.1145/3571884.3597131

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…In a separate study, Aicher et al delved into the terrain of users' beliefs and opinions, characterizing them as self-imposed filter bubbles [23]. Recognizing the challenge these bubbles pose in limiting perspective and understanding, they develop a method to reduce the probability of users getting into such a bubble.…”

Section: Argumentative Dialogue Systemsmentioning

confidence: 99%

Argumentation Effect of a Chatbot for Ethical Discussions about Autonomous AI Scenarios

Hauptmann,

Krenzer,

Völkel

et al. 2023

Preprint

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This study presents a German-language AI chatbot adept at fostering argumentative dialogues across various topics. A core component is the Argumentative Knowledge Graph that powers argument understanding and generation. We showcase the Argumentation Effect of our chatbot in a comprehensive user study. This effect is characterized by the chatbot's successful ability to reshape users' attitudes towards ethically challenging topics, such as autonomous AI systems acting as medicine doctors, by imparting novel perspectives and insights. This led to a substantial gain in user knowledge and acceptance, underscoring the transformative role dialogue systems can play in promoting more informed discussions on complex ethical matters. In tandem with this, we introduced a preliminary methodology for advancing the chatbot's Argument Recognition Unit (ARU). By harnessing real-world user arguments and generated samples from an advanced language model, we achieved significant improvements in the confidence and accuracy of argument prediction. In summary, our research demonstrates how an AI chatbot can instigate a powerful Argumentation Effect, reshaping attitudes, widening knowledge, and facilitating informed discourse on ethically charged topics. This is further amplified by improved argument recognition capabilities, opening promising avenues for more effective, and thus more engaging, user-chatbot dialogues.

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Section: Argumentative Dialogue Systemsmentioning

confidence: 99%

Argumentation Effect of a Chatbot for Ethical Discussions about Autonomous AI Scenarios

Hauptmann,

Krenzer,

Völkel

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

BEA: Building Engaging Argumentation

Aicher,

Weber,

André

et al. 2024

Robust Argumentation Machines

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Exchanging arguments and knowledge in conversations is an intuitive way for humans to form opinions and reconcile opposing viewpoints. The vast amount of information available on the internet, often accessed through search engines, presents a considerable challenge. Managing and filtering this overwhelming wealth of data raises the potential for intellectual isolation. This can stem either from personalized searches that create “filter bubbles” by considering a user’s history and preferences, or from the intrinsic, albeit unconscious, tendency of users to seek information that aligns with their existing beliefs, forming “self-imposed filter bubbles”.To address this issue, we introduce a model aimed at engaging the user in a critical examination of presented arguments and propose the use of a virtual agent engaging in a deliberative dialogue with human users to facilitate a fair and unbiased opinion formation. Our experiments have demonstrated the success of these models and their implementation. As a result, this work offers valuable insights for the design of future cooperative argumentative dialogue systems.

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Argumentation effect of a chatbot for ethical discussions about autonomous AI scenarios

Hauptmann,

Krenzer,

Völkel

et al. 2024

Knowl Inf Syst

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This paper explores the potential of a German-language chatbot to engage users in argumentative dialogues on ethically sensitive topics. Utilizing an argumentative knowledge graph, the chatbot is equipped to engage in discussions on the ethical implications of autonomous AI systems in hypothetical future scenarios in the fields of medicine, law, and self-driving cars. In a study with 178 student participants, we investigated the chatbot’s argumentation effect—its ability to offer new perspectives, gain user acceptance, and broaden users’ viewpoints on complex issues. The results indicated a substantial argumentation effect, with 13–21% of participants shifting their opinions to more moderate stances after interacting with the chatbot. This shift demonstrates the system’s effectiveness in fostering informed discourse and increasing users’ understanding of AI ethics. While the chatbot was well-received, with users acknowledging the quality of its arguments, we identified opportunities for improvement in its argument recognition capabilities. Despite this, our results indicate the chatbot’s potential as an educational tool in engaging users with the ethical dimensions of AI technology and promoting informed discourse.

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Self-imposed Filter Bubble Model for Argumentative Dialogues

Cited by 4 publications

References 27 publications

Argumentation Effect of a Chatbot for Ethical Discussions about Autonomous AI Scenarios

Argumentation Effect of a Chatbot for Ethical Discussions about Autonomous AI Scenarios

BEA: Building Engaging Argumentation

Argumentation effect of a chatbot for ethical discussions about autonomous AI scenarios

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