Monitoring Trust in Human-Machine Interactions for Public Sector Applications

• URL: http://arxiv.org/abs/2010.08140v1
• Date: Fri, 16 Oct 2020 03:59:28 GMT
• Title: Monitoring Trust in Human-Machine Interactions for Public Sector Applications
• Abstract summary: This work addresses the capacity of psychophysiological sensors and measures to detect levels of trust for humans using AI-supported Human-Machine Interaction (HMI) A challenge to analyzing the EEG and GSR data is the large amount of training data required due to a large number of variables in the measurements. We used the Local Interpretable Model-agnostic Explanations (LIME) model as a process to reduce the volume of data required to monitor and enhance trust in HMI systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The work reported here addresses the capacity of psychophysiological sensors and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR) to detect levels of trust for humans using AI-supported Human-Machine Interaction (HMI). Improvements to the analysis of EEG and GSR data may create models that perform as well, or better than, traditional tools. A challenge to analyzing the EEG and GSR data is the large amount of training data required due to a large number of variables in the measurements. Researchers have routinely used standard machine-learning classifiers like artificial neural networks (ANN), support vector machines (SVM), and K-nearest neighbors (KNN). Traditionally, these have provided few insights into which features of the EEG and GSR data facilitate the more and least accurate predictions - thus making it harder to improve the HMI and human-machine trust relationship. A key ingredient to applying trust-sensor research results to practical situations and monitoring trust in work environments is the understanding of which key features are contributing to trust and then reducing the amount of data needed for practical applications. We used the Local Interpretable Model-agnostic Explanations (LIME) model as a process to reduce the volume of data required to monitor and enhance trust in HMI systems - a technology that could be valuable for governmental and public sector applications. Explainable AI can make HMI systems transparent and promote trust. From customer service in government agencies and community-level non-profit public service organizations to national military and cybersecurity institutions, many public sector organizations are increasingly concerned to have effective and ethical HMI with services that are trustworthy, unbiased, and free of unintended negative consequences.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• The SMART+ Framework for AI Systems [0.2039123720459736]
  Artificial Intelligence (AI) systems are now an integral part of multiple industries.<n>In clinical research, AI supports automated adverse event detection in clinical trials, patient eligibility screening for protocol enrollment, and data quality validation.<n>Beyond healthcare, AI is transforming finance through real-time fraud detection, automated loan risk assessment, and algorithmic decision-making.
  arXiv  Detail & Related papers  (2025-12-09T13:33:14Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• When Assurance Undermines Intelligence: The Efficiency Costs of Data Governance in AI-Enabled Labor Markets [5.3700224653806865]
  We show that restricting data use significantly reduced GenAI efficiency, leading to lower matching rates, higher employee turnover, and heightened labor market frictions.<n>Our findings reveal the unintended efficiency costs of well-intentioned data governance and highlight that information assurance, while essential for trust, can undermine intelligence-driven efficiency when misaligned with AI system design.
  arXiv  Detail & Related papers  (2025-11-02T05:35:37Z)
• How can we assess human-agent interactions? Case studies in software agent design [52.953425368394306]
  We make two major steps towards the rigorous assessment of human-agent interactions.<n>We propose PULSE, a framework for more efficient human-centric evaluation of agent designs.<n>We deploy the framework on a large-scale web platform built around the open-source software agent OpenHands.
  arXiv  Detail & Related papers  (2025-10-10T19:04:28Z)
• Machine Learning based Enterprise Financial Audit Framework and High Risk Identification [6.433444278723668]
  This study proposes an AI-driven framework for enterprise financial audits and high-risk identification.<n>Using a dataset from the Big Four accounting firms (EY, PwC, Deloitte, KPMG) from 2020 to 2025, the research examines trends in risk assessment, compliance violations, and fraud detection.<n>The study recommends adopting Random Forest as a core model, enhancing features via engineering, and implementing real-time risk monitoring.
  arXiv  Detail & Related papers  (2025-07-08T00:22:49Z)
• Report on NSF Workshop on Science of Safe AI [75.96202715567088]
  New advances in machine learning are leading to new opportunities to develop technology-based solutions to societal problems.<n>To fulfill the promise of AI, we must address how to develop AI-based systems that are accurate and performant but also safe and trustworthy.<n>This report is the result of the discussions in the working groups that addressed different aspects of safety at the workshop.
  arXiv  Detail & Related papers  (2025-06-24T18:55:29Z)
• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions [0.0]
  This paper introduces the Privacy-Preserving Zero-Shot Learning (PP-ZSL) framework, a novel approach leveraging large language models (LLMs) in a zero-shot learning mode. Unlike conventional machine learning methods, PP-ZSL eliminates the need for local training on sensitive data by utilizing pre-trained LLMs to generate responses directly. The framework incorporates real-time data anonymization to redact or mask sensitive information, retrieval-augmented generation (RAG) for domain-specific query resolution, and robust post-processing to ensure compliance with regulatory standards.
  arXiv  Detail & Related papers  (2024-12-10T17:20:47Z)
• CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments [90.29937153770835]
  We introduce CRMArena, a benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments. We show that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities. Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments.
  arXiv  Detail & Related papers  (2024-11-04T17:30:51Z)
• Evaluating Cultural and Social Awareness of LLM Web Agents [113.49968423990616]
  We introduce CASA, a benchmark designed to assess large language models' sensitivity to cultural and social norms. Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations. Experiments show that current LLMs perform significantly better in non-agent environments.
  arXiv  Detail & Related papers  (2024-10-30T17:35:44Z)
• NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness [0.0]
  We present NeuralSentinel (NS), a tool able to validate the reliability and trustworthiness of AI models. NS help non-expert staff increase their confidence in this new system by understanding the model decisions. This tool was deployed and used in a Hackathon event to evaluate the reliability of a skin cancer image detector.
  arXiv  Detail & Related papers  (2024-02-12T09:24:34Z)
• Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems [1.1034992901877594]
  Large-scale foundation models (LFMs) like BERT and GPT signifies a significant advancement in AI technology. ChatGPT stands as a remarkable accomplishment within this research paradigm, harboring potential for General Artificial Intelligence. Considering the ongoing enhancement in data acquisition technology and data processing capability, LFMs are anticipated to assume a crucial role in the PHM domain of ICPS.
  arXiv  Detail & Related papers  (2023-12-11T09:58:46Z)
• CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction [8.568323820210686]
  Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Data privacy is a crucial concern in the interaction between humans and robots, as companies need to protect sensitive data. Federated learning (FL) offers a solution by enabling the distributed training of models without sharing raw data.
  arXiv  Detail & Related papers  (2023-08-29T01:34:33Z)
• Distributed data analytics [8.415530878975751]
  Recommendation systems are a key component of online service providers. Financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance.
  arXiv  Detail & Related papers  (2022-03-26T14:10:51Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM) NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention [66.93307963324834]
  We propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems. The proposed system reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.
  arXiv  Detail & Related papers  (2021-12-06T18:59:02Z)
• The Powerful Use of AI in the Energy Sector: Intelligent Forecasting [7.747343962518897]
  This paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector. The goal is to provide a high level of confidence to energy utility users.
  arXiv  Detail & Related papers  (2021-11-03T05:30:42Z)
• Transformers for prompt-level EMA non-response prediction [62.41658786277712]
  Ecological Momentary Assessments (EMAs) are an important psychological data source for measuring cognitive states, affect, behavior, and environmental factors. Non-response, in which participants fail to respond to EMA prompts, is an endemic problem. The ability to accurately predict non-response could be utilized to improve EMA delivery and develop compliance interventions.
  arXiv  Detail & Related papers  (2021-11-01T18:38:47Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.