On the public citation graph
Towards a Better Understanding of Evaluating Trustworthiness in AI Systems
0 signed · 204 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/aaai.12062openalex-snapshot
The AI field needs translational Ethical AI research
inferredcites →https://doi.org/10.1002/poi3.198openalex-snapshot
Liable, but Not in Control? Ensuring Meaningful Human Agency in Automated Decision‐Making Systems
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_40openalex-snapshot
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow
inferredcites →https://doi.org/10.1007/978-3-030-86365-4_35openalex-snapshot
Towards Measuring Bias in Image Classification
inferredcites →https://doi.org/10.1007/978-3-030-98464-9openalex-snapshot
Requirements Engineering: Foundation for Software Quality
inferredcites →https://doi.org/10.1007/978-3-030-98464-9_1openalex-snapshot
Transparency and Explainability of AI Systems: Ethical Guidelines in Practice
inferredcites →https://doi.org/10.1007/978-3-031-09037-0_8openalex-snapshot
Metrics for Saliency Map Evaluation of Deep Learning Explanation Methods
inferredcites →https://doi.org/10.1007/978-3-319-90403-0_9openalex-snapshot
Perturbation-Based Explanations of Prediction Models
inferredcites →https://doi.org/10.1007/978-3-319-98131-4openalex-snapshot
Explainable and Interpretable Models in Computer Vision and Machine Learning
inferredcites →https://doi.org/10.1007/978-3-319-98131-4_1openalex-snapshot
Considerations for Evaluation and Generalization in Interpretable Machine Learning
inferredcites →https://doi.org/10.1007/978-981-97-8702-9openalex-snapshot
Pattern Recognition and Artificial Intelligence
inferredcites →https://doi.org/10.1007/bf02639315openalex-snapshot
Accountability in a computerized society
inferredcites →https://doi.org/10.1007/s00146-022-01454-7openalex-snapshot
Enhancing human agency through redress in Artificial Intelligence Systems
inferredcites →https://doi.org/10.1007/s00146-023-01635-yopenalex-snapshot
Accountability in Artificial Intelligence: What It Is and How It Works
inferredcites →https://doi.org/10.1007/s00779-017-1104-3openalex-snapshot
A systematic literature review of data governance and cloud data governance
inferredcites →https://doi.org/10.1007/s10462-023-10562-9openalex-snapshot
A survey of uncertainty in deep neural networks
inferredcites →https://doi.org/10.1007/s10618-022-00831-6openalex-snapshot
Counterfactual explanations and how to find them: literature review and benchmarking
inferredcites →https://doi.org/10.1007/s10676-010-9242-6openalex-snapshot
A framework for the ethical impact assessment of information technology
inferredcites →https://doi.org/10.1007/s11023-018-9482-5openalex-snapshot
AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations
inferredcites →https://doi.org/10.1007/s11263-017-1059-xopenalex-snapshot
Top-Down Neural Attention by Excitation Backprop
inferredcites →https://doi.org/10.1007/s11633-019-1211-xopenalex-snapshot
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
inferredcites →https://doi.org/10.1007/s11948-020-00277-3openalex-snapshot
Actionable Principles for Artificial Intelligence Policy: Three Pathways
inferredcites →https://doi.org/10.1007/s13042-022-01647-yopenalex-snapshot
A survey on federated learning: challenges and applications
inferredcites →https://doi.org/10.1007/s43681-022-00171-7openalex-snapshot
Operationalising AI Governance through Ethics-Based Auditing: An Industry Case Study
inferredcites →https://doi.org/10.1016/j.chb.2019.04.019openalex-snapshot
Role of fairness, accountability, and transparency in algorithmic affordance
inferredcites →https://doi.org/10.1016/j.clsr.2009.02.002openalex-snapshot
Privacy impact assessment: Its origins and development
inferredcites →https://doi.org/10.1016/j.clsr.2020.105404openalex-snapshot
Information privacy, impact assessment, and the place of ethics
inferredcites →https://doi.org/10.1016/j.clsr.2022.105681openalex-snapshot
The flaws of policies requiring human oversight of government algorithms
inferredcites →https://doi.org/10.1016/j.comcom.2019.04.011openalex-snapshot
Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges
inferredcites →https://doi.org/10.1016/j.giq.2020.101493openalex-snapshot
Data governance: Organizing data for trustworthy Artificial Intelligence
inferredcites →https://doi.org/10.1016/j.inffus.2019.12.012openalex-snapshot
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.008openalex-snapshot
CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
inferredcites →https://doi.org/10.1016/s0166-4115(08)62386-9openalex-snapshot
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
inferredcites →https://doi.org/10.1017/cbo9781107337756openalex-snapshot
Secure Multiparty Computation and Secret Sharing
inferredcites →https://doi.org/10.1038/s42256-019-0088-2openalex-snapshot
The global landscape of AI ethics guidelines
inferredcites →https://doi.org/10.1038/s42256-022-00516-1openalex-snapshot
Advances, challenges and opportunities in creating data for trustworthy AI
inferredcites →https://doi.org/10.1080/00140139408964957openalex-snapshot
Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems
inferredcites →https://doi.org/10.1080/08838151.2020.1843357openalex-snapshot
User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability
inferredcites →https://doi.org/10.1080/13600869.2017.1298547openalex-snapshot
Accountability for the use of algorithms in a big data environment
inferredcites →https://doi.org/10.1080/14615517.2013.782978openalex-snapshot
Human rights and impact assessment: clarifying the connections in practice
inferredcites →https://doi.org/10.1080/2573234x.2021.1952913openalex-snapshot
A social evaluation of the perceived goodness of explainability in machine learning
inferredcites →https://doi.org/10.1089/big.2016.0047openalex-snapshot
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
inferredcites →https://doi.org/10.1093/cybsec/tyy001openalex-snapshot
Forgetting personal data and revoking consent under the GDPR: Challenges and proposed solutions
inferredcites →https://doi.org/10.1093/jopart/mus034openalex-snapshot
The Accountability Cube: Measuring Accountability
inferredcites →https://doi.org/10.1093/oxfordhb/9780190067397.013.5openalex-snapshot
AI Governance by Human Rights–Centered Design, Deliberation, and Oversight
inferredcites →https://doi.org/10.1108/ics-04-2019-0047openalex-snapshot
Evaluating privacy impact assessment methods: guidelines and best practice
inferredcites →https://doi.org/10.1108/jeim-06-2020-0233openalex-snapshot
Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption
inferredcites →https://doi.org/10.1109/access.2017.2706947openalex-snapshot
Privacy-Preserving Data Mining: Methods, Metrics, and Applications
inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.1109/access.2020.3045078openalex-snapshot
Privacy and Security Issues in Deep Learning: A Survey
inferredcites →https://doi.org/10.1109/access.2022.3159694openalex-snapshot
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network
inferredcites →https://doi.org/10.1109/bigdata.2017.8258038openalex-snapshot
The ML test score: A rubric for ML production readiness and technical debt reduction
inferredcites →https://doi.org/10.1109/bigdata38862.2016openalex-snapshot
2016 IEEE International Conference on Big Data (Big Data)
inferredcites →https://doi.org/10.1109/cogmi56440.2022.00024openalex-snapshot
Face Biometric Fairness Evaluation on Real vs Synthetic Cross-Spectral Images
inferredcites →https://doi.org/10.1109/csf.2018.00027openalex-snapshot
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
inferredcites →https://doi.org/10.1109/csf43763.2018openalex-snapshot
2018 IEEE 31st Computer Security Foundations Symposium (CSF)
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01501openalex-snapshot
Natural Adversarial Examples
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00167openalex-snapshot
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
inferredcites →https://doi.org/10.1109/eurosp51992.2021.00025openalex-snapshot
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
inferredcites →https://doi.org/10.1109/iccv48922.2021.00823openalex-snapshot
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
inferredcites →https://doi.org/10.1109/icde.2007.367856openalex-snapshot
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
inferredcites →https://doi.org/10.1109/icde11332.2006openalex-snapshot
2007 IEEE 23rd International Conference on Data Engineering
inferredcites →https://doi.org/10.1109/icstw55395.2022.00030openalex-snapshot
A Combinatorial Approach to Fairness Testing of Machine Learning Models
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9206975openalex-snapshot
Towards Best Practice in Explaining Neural Network Decisions with LRP
inferredcites →https://doi.org/10.1109/isc255366.2022.9922069openalex-snapshot
Federated Trustworthy AI Architecture for Smart Cities
inferredcites →https://doi.org/10.1109/jiot.2020.2969326openalex-snapshot
A Survey on Access Control in the Age of Internet of Things
inferredcites →https://doi.org/10.1109/jproc.2021.3060483openalex-snapshot
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
inferredcites →https://doi.org/10.1109/mc.2022.3148714openalex-snapshot
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
inferredcites →https://doi.org/10.1109/msec.2018.2888775openalex-snapshot
Privacy-Preserving Machine Learning: Threats and Solutions
inferredcites →https://doi.org/10.1109/msp.2020.2975749openalex-snapshot
Federated Learning: Challenges, Methods, and Future Directions
inferredcites →https://doi.org/10.1109/seaa.2018.00018openalex-snapshot
Software Engineering Challenges of Deep Learning
inferredcites →https://doi.org/10.1109/sp.2017.41openalex-snapshot
Membership Inference Attacks Against Machine Learning Models
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/sp37862.2018openalex-snapshot
2018 IEEE Symposium on Security and Privacy (SP)
inferredcites →https://doi.org/10.1109/sp46214.2022.9833649openalex-snapshot
Membership Inference Attacks From First Principles
inferredcites →https://doi.org/10.1109/tnnls.2016.2599820openalex-snapshot
Evaluating the Visualization of What a Deep Neural Network Has Learned
inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-snapshot
Classification with Noisy Labels by Importance Reweighting
inferredcites →https://doi.org/10.1109/tpami.2018.2815601openalex-snapshot
What Do Different Evaluation Metrics Tell Us About Saliency Models?
inferredcites →https://doi.org/10.1109/tts.2021.3066209openalex-snapshot
Z-Inspection ® : A Process to Assess Trustworthy AI
inferredcites →https://doi.org/10.1109/tts.2021.3077595openalex-snapshot
AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries
inferredcites →https://doi.org/10.1109/wacv.2018.00097openalex-snapshot
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/wacv37755.2017openalex-snapshot
2017 IEEE Winter Conference on Applications of Computer Vision (WACV)
inferredcites →https://doi.org/10.1111/1758-5899.12403openalex-snapshot
Strategic Implications of Openness in AI Development
inferredcites →https://doi.org/10.1111/j.1468-0386.2007.00378.xopenalex-snapshot
Analysing and Assessing Accountability: A Conceptual Framework 1
inferredcites →https://doi.org/10.1111/rego.12369openalex-snapshot
How to regulate algorithmic decision‐making: A framework of regulatory requirements for different applications
inferredcites →https://doi.org/10.1142/s0218488502001648openalex-snapshot
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
inferredcites →https://doi.org/10.1145/1054972.1055028openalex-snapshot
A method to standardize usability metrics into a single score
inferredcites →https://doi.org/10.1145/1102351openalex-snapshot
Proceedings of the 22nd international conference on Machine learning - ICML '05
inferredcites →https://doi.org/10.1145/1102351.1102430openalex-snapshot
Predicting good probabilities with supervised learning
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1145/1518701.1518803openalex-snapshot
A survey of software learnability
inferredcites →https://doi.org/10.1145/1536414.1536440openalex-snapshot
Fully homomorphic encryption using ideal lattices
inferredcites →https://doi.org/10.1145/2623330openalex-snapshot
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
inferredcites →https://doi.org/10.1145/2783258.2783311openalex-snapshot
Certifying and Removing Disparate Impact
inferredcites →https://doi.org/10.1145/2810103.2813677openalex-snapshot
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
inferredcites →https://doi.org/10.1145/3097983openalex-snapshot
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
inferredcites →https://doi.org/10.1145/3097983.3098095openalex-snapshot
Algorithmic Decision Making and the Cost of Fairness
inferredcites →https://doi.org/10.1145/3194770.3194776openalex-snapshot
Fairness definitions explained
inferredcites →https://doi.org/10.1145/3219819.3220046openalex-snapshot
A Unified Approach to Quantifying Algorithmic Unfairness
inferredcites →https://doi.org/10.1145/3243734openalex-snapshot
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
inferredcites →https://doi.org/10.1145/3243734.3243855openalex-snapshot
Machine Learning with Membership Privacy using Adversarial Regularization
inferredcites →https://doi.org/10.1145/3287560.3287596openalex-snapshot
Model Cards for Model Reporting
inferredcites →https://doi.org/10.1145/3290605.3300233openalex-snapshot
Guidelines for Human-AI Interaction
inferredcites →https://doi.org/10.1145/3301275.3302324openalex-snapshot
Towards human-guided machine learning
inferredcites →https://doi.org/10.1145/3313831.3376445openalex-snapshot
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
inferredcites →https://doi.org/10.1145/3322640.3326699openalex-snapshot
Human intervention in automated decision-making
inferredcites →https://doi.org/10.1145/3351095.3372833openalex-snapshot
What to account for when accounting for algorithms
inferredcites →https://doi.org/10.1145/3351095.3372834openalex-snapshot
The relationship between trust in AI and trustworthy machine learning technologies
inferredcites →https://doi.org/10.1145/3351095.3372852openalex-snapshot
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
inferredcites →https://doi.org/10.1145/3351095.3372873openalex-snapshot
Closing the AI accountability gap
inferredcites →https://doi.org/10.1145/3351095.3372875openalex-snapshot
Multi-layered explanations from algorithmic impact assessments in the GDPR
inferredcites →https://doi.org/10.1145/3368089.3417045openalex-snapshot
Beyond accuracy: assessing software documentation quality
inferredcites →https://doi.org/10.1145/3391403openalex-snapshot
Proceedings of the 21st ACM Conference on Economics and Computation
inferredcites →https://doi.org/10.1145/3391403.3399473openalex-snapshot
Fair Prediction with Endogenous Behavior
inferredcites →https://doi.org/10.1145/3411764.3445315openalex-snapshot
Manipulating and Measuring Model Interpretability
inferredcites →https://doi.org/10.1145/3442188.3445921openalex-snapshot
Reviewable Automated Decision-Making
inferredcites →https://doi.org/10.1145/3442188.3445935openalex-snapshot
Algorithmic Impact Assessments and Accountability
inferredcites →https://doi.org/10.1145/3457607openalex-snapshot
A Survey on Bias and Fairness in Machine Learning
inferredcites →https://doi.org/10.1145/3461702.3462514openalex-snapshot
Scaling Guarantees for Nearest Counterfactual Explanations
inferredcites →https://doi.org/10.1145/3491209openalex-snapshot
Trustworthy Artificial Intelligence: A Review
inferredcites →https://doi.org/10.1145/3494672openalex-snapshot
A Review on Fairness in Machine Learning
inferredcites →https://doi.org/10.1145/3514094.3534187openalex-snapshot
Responsible AI Systems: Who are the Stakeholders?
inferredcites →https://doi.org/10.1145/3523273openalex-snapshot
Membership Inference Attacks on Machine Learning: A Survey
inferredcites →https://doi.org/10.1145/3531146.3533182openalex-snapshot
Designing for Responsible Trust in AI Systems: A Communication Perspective
inferredcites →https://doi.org/10.1145/3533378openalex-snapshot
Challenges in Deploying Machine Learning: A Survey of Case Studies
inferredcites →https://doi.org/10.1145/3546872openalex-snapshot
Trustworthy AI: A Computational Perspective
inferredcites →https://doi.org/10.1145/3555803openalex-snapshot
Trustworthy AI: From Principles to Practices
inferredcites →https://doi.org/10.1145/3616865openalex-snapshot
Fairness in Machine Learning: A Survey
inferredcites →https://doi.org/10.1145/57167openalex-snapshot
Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88
inferredcites →https://doi.org/10.1145/582192openalex-snapshot
Proceedings of the 1983 ACM SIGMOD international conference on Management of data - SIGMOD '83
inferredcites →https://doi.org/10.1177/0049124118782533openalex-snapshot
Fairness in Criminal Justice Risk Assessments: The State of the Art
inferredcites →https://doi.org/10.1177/2053951720983865openalex-snapshot
The algorithm audit: Scoring the algorithms that score us
inferredcites →https://doi.org/10.1207/s15327566ijce0401_04openalex-snapshot
Foundations for an Empirically Determined Scale of Trust in Automated Systems
inferredcites →https://doi.org/10.1504/ijbdi.2016.073904openalex-snapshot
Privacy models for big data: a survey
inferredcites →https://doi.org/10.1609/aaai.v33i01.33013681openalex-snapshot
Interpretation of Neural Networks Is Fragile
inferredcites →https://doi.org/10.1609/aimag.v35i4.2513openalex-snapshot
Power to the People: The Role of Humans in Interactive Machine Learning
inferredcites →https://doi.org/10.1613/jair.1.13197openalex-snapshot
Impact of Imputation Strategies on Fairness in Machine Learning
inferredcites →https://doi.org/10.17863/cam.69875openalex-snapshot
Depth Uncertainty in Neural Networks
inferredcites →https://doi.org/10.18420/inf2022_26openalex-snapshot
Towards the Operationalization of Trustworthy AI: Integrating the EU Assessment List into a Procedure Model for the Development and Operation of AI-Systems
inferredcites →https://doi.org/10.18653/v1/2023.findings-acl.719openalex-snapshot
Membership Inference Attacks against Language Models via Neighbourhood Comparison
inferredcites →https://doi.org/10.18653/v1/p19-1355openalex-snapshot
Energy and Policy Considerations for Deep Learning in NLP
inferredcites →https://doi.org/10.2139/ssrn.3063289openalex-snapshot
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
inferredcites →https://doi.org/10.2139/ssrn.3357652openalex-snapshot
The Importance of Data Access Regimes for Artificial Intelligence and Machine Learning
inferredcites →https://doi.org/10.2139/ssrn.3518482openalex-snapshot
Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI
inferredcites →https://doi.org/10.2139/ssrn.4064091openalex-snapshot
capAI - A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act
inferredcites →https://doi.org/10.24963/ijcai.2020/417openalex-snapshot
Evaluating and Aggregating Feature-based Model Explanations
inferredcites →https://doi.org/10.24963/ijcai.2021/591openalex-snapshot
Recent Advances in Adversarial Training for Adversarial Robustness
inferredcites →https://doi.org/10.3389/fdgth.2022.730430openalex-snapshot
Ethical Principles in Digital Palliative Care for Children: The MyPal Project and Experiences Made in Designing a Trustworthy Approach
inferredcites →https://doi.org/10.3390/app11135781openalex-snapshot
A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System
inferredcites →https://doi.org/10.3390/bdcc5020020openalex-snapshot
Traceability for Trustworthy AI: A Review of Models and Tools
inferredcites →https://doi.org/10.3390/e23010018openalex-snapshot
Explainable AI: A Review of Machine Learning Interpretability Methods
inferredcites →https://doi.org/10.3390/electronics8080832openalex-snapshot
Machine Learning Interpretability: A Survey on Methods and Metrics
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.1808.00023openalex-snapshot
The Measure and Mismeasure of Fairness
inferredcites →https://doi.org/10.48550/arxiv.1902.02918openalex-snapshot
Certified Adversarial Robustness via Randomized Smoothing
inferredcites →https://doi.org/10.48550/arxiv.1905.10964openalex-snapshot
Combating Label Noise in Deep Learning Using Abstention
inferredcites →https://doi.org/10.48550/arxiv.1906.08976openalex-snapshot
Mitigating Gender Bias in Natural Language Processing: Literature Review
inferredcites →https://doi.org/10.48550/arxiv.1912.02781openalex-snapshot
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
inferredcites →https://doi.org/10.48550/arxiv.2101.09385openalex-snapshot
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
inferredcites →https://doi.org/10.48550/arxiv.2207.07411openalex-snapshot
Plex: Towards Reliability using Pretrained Large Model Extensions
inferredcites →https://doi.org/10.48550/arxiv.2211.02578openalex-snapshot
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
inferredcites →https://doi.org/10.48550/arxiv.2311.01796openalex-snapshot
Learning to Augment Distributions for Out-of-Distribution Detection
inferredcites →https://doi.org/10.52202/068431-1403openalex-snapshot
Diagnosing Failures of Fairness Transfer Across Distribution Shift in Real-World Medical Settings
inferredcites →https://doi.org/10.52202/085713openalex-snapshot
Advances in Neural Information Processing Systems 19
inferredcites →https://doi.org/10.6028/nist.ai.100-1openalex-snapshot
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-19099-4_38openalex-snapshot
Continuous Assessment-Driven Requirement Elicitation for Trustworthy AI Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-38498-0_10openalex-snapshot
Conceptual Modelling of the EU AI Act Towards Trustworthiness and Regulatory Compliance for AI Systems
inferred← citeshttps://doi.org/10.1007/s12065-026-01259-3openalex-snapshot
Intelligent agents: architectures, learning paradigms, deployment models, and applications
inferred← citeshttps://doi.org/10.1007/s13347-026-01112-6openalex-snapshot
A Conceptual Crosswalk Between Trustworthy Records and Trustworthy AI
inferred← citeshttps://doi.org/10.1007/s43681-026-01349-zopenalex-snapshot
Evaluation methods and metrics towards trustworthy AI
inferred← citeshttps://doi.org/10.1007/s44163-026-02431-5openalex-snapshot
Systemic privacy risks of personal data exposure through conversational large language model agents
inferred← citeshttps://doi.org/10.1016/j.caeai.2026.100610openalex-snapshot
Face value: How avatar identity shapes epistemic trust in AI-mediated learning
inferred← citeshttps://doi.org/10.1016/j.compind.2025.104409openalex-snapshot
Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116110openalex-snapshot
Trustworthy virtual sensing via physics-residual transformers and conformal prediction
inferred← citeshttps://doi.org/10.1016/j.pmatsci.2026.101846openalex-snapshot
Trustworthy artificial intelligence in advanced manufacturing
inferred← citeshttps://doi.org/10.1016/j.rineng.2026.111026openalex-snapshot
Engineering evaluation of AI healthcare applications: Review mining, game-theoretic hybrid weight fusion, and GRA–TOPSIS-based alternative ranking
inferred← citeshttps://doi.org/10.1080/10447318.2026.2617137openalex-snapshot
The Power of Choice: Understanding the Role of Control in Learning Recommender Systems for Workplace Learning
inferred← citeshttps://doi.org/10.1109/aaiml67890.2026.11498170openalex-snapshot
Novel aspects of how trust in human-AI collaboration is formed
inferred← citeshttps://doi.org/10.1109/access.2025.3648410openalex-snapshot
Building Trustworthy AI in Healthcare
inferred← citeshttps://doi.org/10.1109/raaicon69033.2025.11502408openalex-snapshot
Adaptive Reflection Loops for Large Language Models Reducing Hallucinations and Enhancing Robust Reasoning
inferred← citeshttps://doi.org/10.1109/re68928.2026.00021openalex-snapshot
AI Transparency: Governance Compliance or Stakeholder Requirements?
inferred← citeshttps://doi.org/10.1145/3770916openalex-snapshot
A Survey of Defenses Against AI-Generated Visual Media: Detection, Disruption, and Authentication
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1013624openalex-snapshot
Nine quick tips for trustworthy machine learning in the biomedical sciences
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10177526/v1openalex-snapshot
Research Hotspots and Trend Analysis in AI-Enabled Accessible Design: Identification of Core Areas, Evolution of Future Trends, and Visualization through Knowledge Graphs
inferred← citeshttps://doi.org/10.2139/ssrn.6160607openalex-snapshot
Evaluation methods and metrics towards trustworthy AI: literature review with expert knowledge
inferred← citeshttps://doi.org/10.4018/979-8-3373-5483-5.ch003openalex-snapshot
Artificial Intelligence in Emotional Recognition in Animals
inferred← citeshttps://doi.org/10.47026/1810-1909-2026-2-56-72openalex-snapshot
The Bayesian method for evaluating trust in artificial intelligence systems
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.