Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support

• URL: http://arxiv.org/abs/2008.02359v2
• Date: Thu, 13 Aug 2020 08:06:02 GMT
• Title: Risk, Trust, and Bias: Causal Regulators of Biometric-Enabled Decision Support
• Authors: Kenneth Lai, Helder C. R. Oliveira, Ming Hou, Svetlana N. Yanushkevich, and Vlad P. Shmerko
• Abstract summary: Risk, trust, and bias (R-T-B) are emerging measures of performance of such systems. This paper offers a complete taxonomy of the R-T-B causal performance regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making.
• Score: 6.32220198667533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biometrics and biometric-enabled decision support systems (DSS) have become a mandatory part of complex dynamic systems such as security checkpoints, personal health monitoring systems, autonomous robots, and epidemiological surveillance. Risk, trust, and bias (R-T-B) are emerging measures of performance of such systems. The existing studies on the R-T-B impact on system performance mostly ignore the complementary nature of R-T-B and their causal relationships, for instance, risk of trust, risk of bias, and risk of trust over biases. This paper offers a complete taxonomy of the R-T-B causal performance regulators for the biometric-enabled DSS. The proposed novel taxonomy links the R-T-B assessment to the causal inference mechanism for reasoning in decision making. Practical details of the R-T-B assessment in the DSS are demonstrated using the experiments of assessing the trust in synthetic biometric and the risk of bias in face biometrics. The paper also outlines the emerging applications of the proposed approach beyond biometrics, including decision support for epidemiological surveillance such as for COVID-19 pandemics.

Related papers

• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• A Human-Centered Risk Evaluation of Biometric Systems Using Conjoint Analysis [0.6199770411242359]
  This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases.
  arXiv  Detail & Related papers  (2024-09-17T14:18:21Z)
• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations [8.8690305802668]
  A critical attribute of cyber-physical systems (CPS) is robustness, denoting its capacity to operate safely. This paper proposes a novel specification-based robustness, which characterizes the effectiveness of a controller in meeting a specified system requirement. We present an innovative two-layer simulation-based analysis framework designed to identify subtle robustness violations.
  arXiv  Detail & Related papers  (2023-11-13T16:44:43Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Differential privacy and robust statistics in high dimensions [49.50869296871643]
  High-dimensional Propose-Test-Release (HPTR) builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism. We show that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.
  arXiv  Detail & Related papers  (2021-11-12T06:36:40Z)
• Reliability of Decision Support in Cross-spectral Biometric-enabled Systems [2.278720757613755]
  This paper addresses the evaluation of the performance of the decision support system that utilizes face and facial expression biometrics. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system.
  arXiv  Detail & Related papers  (2020-08-13T07:43:14Z)
• Assessing Risks of Biases in Cognitive Decision Support Systems [5.480546613836199]
  This paper addresses a challenging research question on how to manage an ensemble of biases? We provide performance projections of the cognitive Decision Support System operational landscape in terms of biases. We also provide a motivational experiment using face biometric component of the checkpoint system which highlights the discovery of an ensemble of biases.
  arXiv  Detail & Related papers  (2020-07-28T16:53:45Z)
• Watchlist Risk Assessment using Multiparametric Cost and Relative Entropy [0.0]
  We propose a multiparametric cost assessment and relative entropy measures as risk detectors. We experimentally demonstrate the effects of mis-identification and impersonation under various watchlist screening scenarios and constraints.
  arXiv  Detail & Related papers  (2020-07-22T10:27:53Z)
• Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals [59.34844017757795]
  The reliability of spoofing countermeasures (CMs) is gauged using the equal error rate (EER) metric. This paper presents several new extensions to the tandem detection cost function (t-DCF) It is hoped that adoption of the t-DCF for the CM assessment will help to foster closer collaboration between the anti-spoofing and ASV research communities.
  arXiv  Detail & Related papers  (2020-07-12T12:44:08Z)

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.