Fetching the paper…
Reading the bibliography…
Trustworthy Artificial Intelligence (TAI) integrates ethics that align with human values, looking at their influence on AI behaviour and decision-making.
Piloting a survey-based assessment of transparency and trustworthiness with three medical ai tools,
J. Fehr, G. Jaramillo-Gutierrez, L. Oala, M. I. Gröschel, M. Bierwirth, P. Balachandran, A. Werneck-Leite, C. Lippert, · 1923
Earlier work this paper cites.
I. O. for Standardization, Iso/iec 27001: Information technology - security techniques - information security management systems - requirements, Standard, 2013
2013
Earlier work this paper cites.
National Institute of Standards and Technology (NIST), Framework for Improving Critical Infrastructure Cybersecurity, Tech Report, National Institute of Standards and Technology (NIST), 2014. URL: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdf , https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdf
2014
Earlier work this paper cites.
An end-to-end machine learning pipeline that ensures fairness policies,
S. Shaikh, H. Vishwakarma, S. Mehta, K. R. Varshney, K. N. Ramamurthy, I. Wei, · 2017
Earlier work this paper cites.
Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommendations,
L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum, C. Luetge, R. Madelin, U. Pagallo, F. Rossi, et al., · 2018
Earlier work this paper cites.
Information Systems Audit and Control Association (ISACA), COBIT 2019 Framework: Governance and Management Objectives, ISACA, 2018. URL: https://www.isaca.org/bookstore/cobit/
2018
Earlier work this paper cites.
Fairness and transparency of machine learning for trustworthy cloud services,
N. Antunes, L. Balby, F. Figueiredo, N. Lourenco, W. Meira, W. Santos, · 2018
Earlier work this paper cites.
Artificial intelligence ethics: Governance through social media,
J. Buenfil, R. Arnold, B. Abruzzo, C. Korpela, · 2019
Earlier work this paper cites.
Context-conscious fairness in using machine learning to make decisions,
M. S. A. Lee, · 2019
Earlier work this paper cites.
A random forest based predictor for medical data classification using feature ranking,
M. Z. Alam, M. S. Rahman, M. S. Rahman, · 2019
Earlier work this paper cites.
A neural network-based trust management system for edge devices in peer-to-peer networks.,
A. Alhussain, H. Kurdi, L. Altoaimy, · 2019
Earlier work this paper cites.
A. HLEG, Assessment list for trustworthy artificial intelligence (altai) for self-assessment, https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
2020
Earlier work this paper cites.
Transparency for whom? assessing discriminatory artificial intelligence,
T. Van Nuenen, X. Ferrer, J. M. Such, M. Coté, · 2020
Earlier work this paper cites.
S. Agarwal, Trade-offs between fairness, interpretability, and privacy in machine learning, Master’s thesis, University of Waterloo, 2020
2020
Earlier work this paper cites.
Automatic fairness testing of machine learning models,
A. Sharma, H. Wehrheim, · 2020
Earlier work this paper cites.
Trustworthy artificial intelligence,
S. Thiebes, S. Lins, A. Sunyaev, · 2021
Earlier work this paper cites.
Requirements for trustworthy artificial intelligence–a review,
D. Kaur, S. Uslu, A. Durresi, · 2021
Earlier work this paper cites.
Mapping value sensitive design onto ai for social good principles,
S. Umbrello, I. Van de Poel, · 2021
Earlier work this paper cites.
Enhancing trust in ai through industry self-governance,
J. Roski, E. J. Maier, K. Vigilante, E. A. Kane, M. E. Matheny, · 2021
Earlier work this paper cites.
Towards a framework for certification of reliable autonomous systems,
M. Fisher, V. Mascardi, K. Y. Rozier, B.-H. Schlingloff, M. Winikoff, N. Yorke-Smith, · 2021
Earlier work this paper cites.
Machine learning robustness, fairness, and their convergence,
J.-G. Lee, Y. Roh, H. Song, S. E. Whang, · 2021
Earlier work this paper cites.
Towards evaluating ethical accountability and trustworthiness in ai systems,
R. Dvorak, H. Liao, S. Schibel, B. Tribelhorn, · 2021
Cited alongside, same era.
J. Druce, M. Harradon, J. Tittle, · 2021
Cited alongside, same era.
Improving fairness and privacy in selection problems,
M. M. Khalili, X. Zhang, M. Abroshan, S. Sojoudi, · 2021
Cited alongside, same era.
Developing a novel fair-loan-predictor through a multi-sensitive debiasing pipeline: Dualfair,
J. Singh, A. Singh, A. Khan, A. Gupta, · 2021
Cited alongside, same era.
Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs,
M. S. A. Lee, L. Floridi, · 2021
Cited alongside, same era.
The foundation model transparency index,
R. Bommasani, K. Klyman, S. Longpre, S. Kapoor, N. Maslej, B. Xiong, D. Zhang, P. Liang, · 2023
Later among the works it cites.
MITRE Corporation, Mitre atlas, https://atlas.mitre.org/ , 2023
2023
Later among the works it cites.
K. G. BARZA, TOWARDS A ROBUST GENDER BIAS EVALUATION IN NLP, Ph.D. thesis, American University of Beirut, 2023
2023
Later among the works it cites.
Fairness score and process standardization: framework for fairness certification in artificial intelligence systems,
A. Agarwal, H. Agarwal, N. Agarwal, · 2023
Later among the works it cites.
Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and i@inproceedingstification and mitigation methods,
T. P. Pagano, R. B. Loureiro, F. V. Lisboa, R. M. Peixoto, G. A. Guimarães, G. O. Cruz, M. M. Araujo, L. L. Santos, M. A. Cruz, E. L. Oliveira, et al., · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Trustworthy artificial intelligence: a review,
D. Kaur, S. Uslu, K. J. Rittichier, A. Durresi, · 2022
Cited alongside, same era.
Trustworthy ai: A computational perspective,
H. Liu, Y. Wang, W. Fan, X. Liu, Y. Li, S. Jain, Y. Liu, A. Jain, J. Tang, · 2022
Cited alongside, same era.
Fairness audit of machine learning models with confi@inproceedingstial computing,
S. Park, S. Kim, Y.-s. Lim, · 2022
Cited alongside, same era.
Algorithmic fairness audits in intensive care medicine: artificial intelligence for all?,
D. van de Sande, J. van Bommel, E. Fung Fen Chung, D. Gommers, M. E. van Genderen, · 2022
Cited alongside, same era.
The role of explainability in assuring safety of machine learning in healthcare,
Y. Jia, J. McDermid, T. Lawton, I. Habli, · 2022
Cited alongside, same era.
Trust management for artificial intelligence: A standardization perspective,
T.-W. Um, J. Kim, S. Lim, G. M. Lee, · 2022
Cited alongside, same era.
A transparency index framework for ai in education,
M. A. Chaudhry, M. Cukurova, R. Luckin, · 2022
Cited alongside, same era.
Later among the works it cites.
Integrating fairness in the software design process: An interview study with hci and ml experts,
S. Ryan, C. Nadal, G. Doherty, · 2023
Later among the works it cites.
Towards responsible ai: A design space exploration of human-centered artificial intelligence user interfaces to investigate fairness,
Y. Nakao, L. Strappelli, S. Stumpf, A. Naseer, D. Regoli, G. D. Gamba, · 2023
Later among the works it cites.
To be high-risk, or not to be—semantic specifications and implications of the ai act’s high-risk ai applications and harmonised standards,
D. Golpayegani, H. J. Pandit, D. Lewis, · 2023
Later among the works it cites.
Mitigating age biases in resume screening ai models,
C. Harris, · 2023
Later among the works it cites.
Fairkit, fairkit, on the wall, who’s the fairest of them all? supporting fairness-related decision-making,
B. Johnson, J. Bartola, R. Angell, S. Witty, S. Giguere, Y. Brun, · 2023
Later among the works it cites.
W. H. Deng, N. Yildirim, M. Chang, M. Eslami, K. Holstein, M. Madaio, · 2023
Later among the works it cites.
H. Shen, T. Knearem, R. Ghosh, K. Alkiek, K. Krishna, Y. Liu, Z. Ma, S. Petridis, Y.-H. Peng, L. Qiwei, et al., · 2024
Closest in time.
E. Union, Final draft of the artificial intelligence act as of 2nd february 2024, https://artificialintelligenceact.eu/ai-act-explorer/ , 2024
2024
Closest in time.
Towards ai accountability infrastructure: Gaps and opportunities in ai audit tooling,
V. Ojewale, R. Steed, B. Vecchione, A. Birhane, I. D. Raji, · 2024
Closest in time.
An overview of key trustworthiness attributes and kpis for trusted ml-based systems engineering,
J. Mattioli, H. Sohier, A. Delaborde, K. Amokrane-Ferka, A. Awadid, Z. Chihani, S. Khalfaoui, G. Pedroza, · 2024
Closest in time.
Trustworthiness assurance assessment for high-risk ai-based systems,
G. Stettinger, P. Weissensteiner, S. Khastgir, · 2024
Closest in time.
Responsible ai pattern catalogue: A collection of best practices for ai governance and engineering,
Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Zowghi, A. Jacquet, · 2024
Closest in time.
From cobit to iso 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models,
T. R. McIntosh, T. Susnjak, T. Liu, P. Watters, D. Xu, D. Liu, R. Nowrozy, M. N. Halgamuge, · 2024
Closest in time.
An approach to measure the effectiveness of the mitre atlas framework in safeguarding machine learning systems against data poisoning attack,
C. Wymberry, H. Jahankhani, · 2024
Closest in time.
The need for user-centred assessment of ai fairness and correctness,
S. Stumpf, E. Taka, Y. Nakao, L. Luo, R. Sonoda, T. Yokota, · 2024
Closest in time.