Conceptualizing Trustworthiness and Trust in Communications

• URL: http://arxiv.org/abs/2408.01447v1
• Date: Tue, 23 Jul 2024 06:11:13 GMT
• Title: Conceptualizing Trustworthiness and Trust in Communications
• Authors: Gerhard P. Fettweis, Patricia Grünberg, Tim Hentschel, Stefan Köpsell,
• Abstract summary: We present a novel holistic approach on how to tackle trustworthiness systematically in the context of communications. We propose a first attempt to incorporate objective system properties and subjective beliefs to establish trustworthiness-based trust, in particular in the context of the future Tactile Internet connecting robotic devices.
• Score: 17.69113057959175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trustworthiness and trust are basic factors in common societies that allow us to interact and enjoy being in crowds without fear. As robotic devices start percolating into our daily lives they must behave as fully trustworthy objects, such that humans accept them just as we trust interacting with other people in our daily lives. How can we learn from system models and findings from social sciences and how can such learnings be translated into requirements for future technical solutions? We present a novel holistic approach on how to tackle trustworthiness systematically in the context of communications. We propose a first attempt to incorporate objective system properties and subjective beliefs to establish trustworthiness-based trust, in particular in the context of the future Tactile Internet connecting robotic devices. A particular focus is on the underlying communications technology.

Related papers

• Trust in AI: Progress, Challenges, and Future Directions [6.724854390957174]
  The increasing use of artificial intelligence (AI) systems in our daily life explains the significance of trust/distrust in AI from a user perspective. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion.
  arXiv  Detail & Related papers  (2024-03-12T20:26:49Z)
• Common (good) practices measuring trust in HRI [55.2480439325792]
  Trust in robots is widely believed to be imperative for the adoption of robots into people's daily lives. Researchers have been exploring how people trust robot in different ways. Most roboticists agree that insufficient levels of trust lead to a risk of disengagement.
  arXiv  Detail & Related papers  (2023-11-20T20:52:10Z)
• A Diachronic Perspective on User Trust in AI under Uncertainty [52.44939679369428]
  Modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust. We study the evolution of user trust in response to trust-eroding events using a betting game.
  arXiv  Detail & Related papers  (2023-10-20T14:41:46Z)
• A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy [47.89042524852868]
  Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems.
  arXiv  Detail & Related papers  (2023-02-21T12:52:12Z)
• Designing for Responsible Trust in AI Systems: A Communication Perspective [56.80107647520364]
  We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH. We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. We propose a checklist of requirements to help technology creators identify appropriate cues to use.
  arXiv  Detail & Related papers  (2022-04-29T00:14:33Z)
• Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient [0.0]
  The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning. We draw from statistical learning theory and sociological lenses on human-automation trust to motivate an AI-as-tool framework. We clarify the role of interpretability in trust with a ladder of model access.
  arXiv  Detail & Related papers  (2022-02-10T19:59:23Z)
• Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI [55.4046755826066]
  We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people) We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold. We discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted.
  arXiv  Detail & Related papers  (2020-10-15T03:07:23Z)
• How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks [94.65749466106664]
  We conduct a thought experiment and explore two key questions about trust in relation to confidence. We introduce a suite of metrics for assessing the overall trustworthiness of deep neural networks based on their behaviour when answering a set of questions. The proposed metrics are by no means perfect, but the hope is to push the conversation towards better metrics.
  arXiv  Detail & Related papers  (2020-09-12T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.