{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T02:07:34Z","timestamp":1781921254623,"version":"3.54.5"},"reference-count":95,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Phase 6 of the Sophimatics framework represents the culmination of a comprehensive research program integrating philosophical wisdom with computational sophistication to address fundamental challenges in artificial intelligence systems. Building upon the Complex-Time Recursive Model established in Phase 5, this phase introduces a human-in-the-loop iterative refinement methodology specifically designed for security-critical applications. Through systematic validation across real-world cybersecurity datasets, including NSL-KDD and CICIDS2017, alongside healthcare privacy scenarios using MIMIC-III derived data, we demonstrate that collaborative human\u2013AI co-creation significantly enhances system performance across multiple dimensions, including interpretive accuracy, contextual fidelity, and ethical consistency. The proposed architecture implements three complementary feedback mechanisms: symbolic knowledge base refinement through expert-provided ontological corrections, neural parameter optimization guided by human evaluation of ethical alignment, and dynamic weight adjustment for value-system integration. Experimental results show substantial improvements over baseline approaches, with intrusion detection accuracy reaching 98.7% on NSL-KDD while maintaining 94.3% privacy preservation scores as measured by differential privacy guarantees. The healthcare privacy experiments demonstrate 97.2% sensitive attribute protection with only 2.1% utility loss compared to non-private baselines. Critical analysis reveals that human oversight mechanisms reduce false positive rates in ethical constraint violations by 67% compared to purely automated systems, while convergence analysis indicates stable performance after approximately 12\u201315 iterations across diverse application domains. These findings establish Phase 6 as an essential bridge between theoretical Sophimatics foundations and practical deployment in privacy-sensitive contexts, demonstrating that philosophically grounded AI architectures can achieve superior performance when augmented with structured human feedback loops. The work contributes both methodological innovations in human\u2013AI collaboration and empirical validation, demonstrating the viability of Sophimatics principles for addressing contemporary challenges in data protection and cybersecurity.<\/jats:p>","DOI":"10.3390\/a19030175","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:58:03Z","timestamp":1772114283000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Co-Creation by Human\u2013AI Sophimatics Framework and Applications"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-4608","authenticated-orcid":false,"given":"Gerardo","family":"Iovane","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Salerno, 84084 Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Iovane","sequence":"additional","affiliation":[{"name":"Liceo Scientifico Statale Francesco Severi, 84100 Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","unstructured":"Iovane, G., and Iovane, G. (2025). Sophimatics: A New Bridge Between Philosophical Thought and Logic for an Emerging Post-Generative Artificial Intelligence, Aracne Editore."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Iovane, G., and Iovane, G. (2025). Bridging computational structures with philosophical categories in Sophimatics and data protection policy with AI reasoning. Appl. Sci., 15.","DOI":"10.3390\/app152010879"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Iovane, G., and Iovane, G. (2025). Super Time-Cognitive Neural Networks (Phase 3 of Sophimatics): Temporal-philosophical reasoning for security-critical AI applications. Appl. Sci., 15.","DOI":"10.3390\/app152211876"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Iovane, G., and Iovane, G. (2025). Sophimatics: A two-dimensional Temporal Cognitive Architecture for Paradox-Resilient Artificial Intelligence. Big Data Cogn. Comput., 9.","DOI":"10.3390\/bdcc9120314"},{"key":"ref_5","unstructured":"Iovane, G., and Iovane, G. (Algorithms, 2026). From Complexity Theory to Computational Wisdom: Enhancing EEG\u2013Neurotransmitter Models Through Sophimatics for Brain Data Analysis, Algorithms, under final review."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Voigt, P., and Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide, Springer International Publishing.","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref_7","first-page":"671","article-title":"Big Data\u2019s Disparate Impact","volume":"104","author":"Barocas","year":"2016","journal-title":"Calif. Law Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006). Calibrating Noise to Sensitivity in Private Data Analysis. Proceedings of the Theory of Cryptography Conference, Springer.","DOI":"10.1007\/11681878_14"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The Algorithmic Foundations of Differential Privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends\u00ae Theor. Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ACM.","DOI":"10.1145\/2976749.2978318"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and Open Problems in Federated Learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_12","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR."},{"key":"ref_13","first-page":"50","article-title":"Federated Learning: Challenges, Methods, and Future Directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017). Membership Inference Attacks Against Machine Learning Models. Proceedings of the 2017 IEEE Symposium on Security and Privacy, IEEE.","DOI":"10.1109\/SP.2017.41"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3214303","article-title":"A Survey on Homomorphic Encryption Schemes: Theory and Implementation","volume":"51","author":"Acar","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gentry, C. (2009). Fully Homomorphic Encryption Using Ideal Lattices. Proceedings of the 41st Annual ACM Symposium on Theory of Computing, ACM.","DOI":"10.1145\/1536414.1536440"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1137\/120868669","article-title":"Efficient Fully Homomorphic Encryption from (Standard) LWE","volume":"43","author":"Brakerski","year":"2014","journal-title":"SIAM J. Comput."},{"key":"ref_18","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. (2016). CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. Proceedings of the 33rd International Conference on Machine Learning, PMLR."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yao, A.C. (1982). Protocols for Secure Computations. Proceedings of the 23rd Annual Symposium on Foundations of Computer Science, IEEE.","DOI":"10.1109\/SFCS.1982.38"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cramer, R., Damg\u00e5rd, I.B., and Nielsen, J.B. (2015). Secure Multiparty Computation and Secret Sharing, Cambridge University Press.","DOI":"10.1017\/CBO9781107337756"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mohassel, P., and Zhang, Y. (2017). SecureML: A System for Scalable Privacy-Preserving Machine Learning. Proceedings of the 2017 IEEE Symposium on Security and Privacy, IEEE.","DOI":"10.1109\/SP.2017.12"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mohassel, P., and Rindal, P. (2018). ABY3: A Mixed Protocol Framework for Machine Learning. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, ACM.","DOI":"10.1145\/3243734.3243760"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/popets-2019-0035","article-title":"SecureNN: 3-Party Secure Computation for Neural Network Training","volume":"2019","author":"Wagh","year":"2019","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40708-016-0042-6","article-title":"Interactive Machine Learning for Health Informatics: When Do We Need the Human-in-the-Loop?","volume":"3","author":"Holzinger","year":"2016","journal-title":"Brain Inform."},{"key":"ref_25","unstructured":"Settles, B. (2009). Active Learning Literature Survey, University of Wisconsin-Madison."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fails, J.A., and Olsen, D.R. (2003). Interactive Machine Learning. Proceedings of the 8th International Conference on Intelligent User Interfaces, ACM.","DOI":"10.1145\/604050.604056"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s12599-019-00595-2","article-title":"Hybrid Intelligence","volume":"61","author":"Dellermann","year":"2019","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s10462-022-10246-w","article-title":"Human-in-the-Loop Machine Learning: A State of the Art","volume":"56","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u2018Why Should I Trust You?\u2019: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_31","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_33","first-page":"841","article-title":"Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR","volume":"31","author":"Wachter","year":"2017","journal-title":"Harv. J. Law Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3236386.3241340","article-title":"The Mythos of Model Interpretability","volume":"16","author":"Lipton","year":"2018","journal-title":"Queue"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way, Springer International Publishing.","DOI":"10.1007\/978-3-030-30371-6"},{"key":"ref_36","unstructured":"Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_37","unstructured":"Ziegler, D.M., Stiennon, N., Wu, J., Brown, T.B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019). Fine-Tuning Language Models from Human Preferences. arXiv."},{"key":"ref_38","unstructured":"Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control, Viking Press."},{"key":"ref_39","unstructured":"Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities, MIT Press."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s11023-020-09517-8","article-title":"The Ethics of AI Ethics: An Evaluation of Guidelines","volume":"30","author":"Hagendorff","year":"2020","journal-title":"Minds Mach."},{"key":"ref_41","unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., and De Raedt, L. (2018, January 3\u20138). DeepProbLog: Neural Probabilistic Logic Programming. Proceedings of the NeurIPS 2018, Montreal, QC Canada."},{"key":"ref_42","unstructured":"Serafini, L., and d\u2019Avila Garcez, A. (2016, January 16\u201317). Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. Proceedings of the NeSy 2016, New York, NY, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009). A Detailed Analysis of the KDD CUP 99 Data Set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_44","first-page":"446","article-title":"A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms","volume":"4","author":"Dhanabal","year":"2015","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., and Ghorbani, A.A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy, SCITEPRESS.","DOI":"10.5220\/0006639801080116"},{"key":"ref_46","first-page":"479","article-title":"A Detailed Analysis of CICIDS2017 Dataset for Designing Intrusion Detection Systems","volume":"7","author":"Panigrahi","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lashkari, A.H., Draper Gil, G., Mamun, M.S.I., and Ghorbani, A.A. (2017). Characterization of Tor Traffic Using Time Based Features. Proceedings of the 3rd International Conference on Information Systems Security and Privacy, SCITEPRESS.","DOI":"10.5220\/0006105602530262"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a Freely Accessible Critical Care Database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e005122","DOI":"10.1161\/CIRCOUTCOMES.118.005122","article-title":"Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing","volume":"12","author":"Wu","year":"2019","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S. (2018). Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. Proceedings of the 2018 IEEE 31st Computer Security Foundations Symposium, IEEE.","DOI":"10.1109\/CSF.2018.00027"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Carvalho, D.V., Pereira, E.M., and Cardoso, J.S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8.","DOI":"10.3390\/electronics8080832"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s007790170019","article-title":"Understanding and Using Context","volume":"5","author":"Dey","year":"2001","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"McNamara, A., Smith, J., and Murphy-Hill, E. (2018). Does ACM\u2019s Code of Ethics Change Ethical Decision Making in Software Development?. Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM.","DOI":"10.1145\/3236024.3264833"},{"key":"ref_54","first-page":"795","article-title":"Sustainable AI: Environmental Implications, Challenges and Opportunities","volume":"Volume 4","author":"Wu","year":"2022","journal-title":"Proceedings of the Machine Learning and Systems 2022"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.cose.2019.06.005","article-title":"A Survey of Network-based Intrusion Detection Data Sets","volume":"86","author":"Ring","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_56","first-page":"102419","article-title":"Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study","volume":"50","author":"Ferrag","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., and Marchetti, M. (2018). On the Effectiveness of Machine and Deep Learning for Cyber Security. Proceedings of the 2018 10th International Conference on Cyber Conflict (CyCon), IEEE.","DOI":"10.23919\/CYCON.2018.8405026"},{"key":"ref_58","unstructured":"Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., and Talwar, K. (2017). Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. Proceedings of the 5th International Conference on Learning Representations, OpenReview.net."},{"key":"ref_59","unstructured":"Tram\u00e8r, F., and Boneh, D. (2021). Differentially Private Learning Needs Better Features (or Much More Data). Proceedings of the 9th International Conference on Learning Representations, OpenReview.net."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M. (2019). ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. Proceedings of the 2019 Network and Distributed System Security Symposium, Internet Society.","DOI":"10.14722\/ndss.2019.23119"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s43856-024-00462-6","article-title":"Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging","volume":"4","author":"Ziller","year":"2024","journal-title":"Commun. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring Fairness in Machine Learning to Advance Health Equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3236009","article-title":"A Survey of Methods for Explaining Black Box Models","volume":"51","author":"Guidotti","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","article-title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","volume":"267","author":"Miller","year":"2019","journal-title":"Artif. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1145\/2701413","article-title":"Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence","volume":"58","author":"Davis","year":"2015","journal-title":"Commun. ACM"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1137\/16M1080173","article-title":"Optimization Methods for Large-Scale Machine Learning","volume":"60","author":"Bottou","year":"2018","journal-title":"SIAM Rev."},{"key":"ref_68","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (2017). Understanding Deep Learning Requires Rethinking Generalization. Proceedings of the 5th International Conference on Learning Representations, OpenReview.net."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P.N., and Inkpen, K. (2019). Guidelines for Human-AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3290605.3300233"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/10447318.2020.1741118","article-title":"Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy","volume":"36","author":"Shneiderman","year":"2020","journal-title":"Int. J. Hum.\u2013Comput. Interact."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1609\/aaai.v33i01.33012429","article-title":"Updates in Human-AI Teams: Understanding and Addressing the Performance\/Compatibility Tradeoff","volume":"Volume 33","author":"Bansal","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence 2019"},{"key":"ref_72","unstructured":"Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect, Basic Books."},{"key":"ref_73","unstructured":"Marcus, G. (2020). The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e253","DOI":"10.1017\/S0140525X16001837","article-title":"Building Machines That Learn and Think Like People","volume":"40","author":"Lake","year":"2017","journal-title":"Behav. Brain Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.cobeha.2018.12.010","article-title":"Reconciling Deep Learning with Symbolic Artificial Intelligence","volume":"29","author":"Garnelo","year":"2019","journal-title":"Curr. Opin. Behav. Sci."},{"key":"ref_76","unstructured":"Jayaraman, B., and Evans, D. (2019, January 14\u201316). Evaluating Differentially Private Machine Learning in Practice. Proceedings of the USENIX Security 2019, Santa Clara, CA, USA."},{"key":"ref_77","unstructured":"Bagdasaryan, E., Poursaeed, O., and Shmatikov, V. (2019, January 8\u201314). Differential Privacy Has Disparate Impact on Model Accuracy. Proceedings of the NeurIPS 2019, Vancouver, BC, Canada."},{"key":"ref_78","unstructured":"Carlini, N., Liu, C., Erlingsson, \u00da., Kos, J., and Song, D. (2019, January 14\u201316). The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. Proceedings of the USENIX Security 2019, Santa Clara, CA, USA."},{"key":"ref_79","first-page":"1069","article-title":"Differentially Private Empirical Risk Minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Schilit, B., Adams, N., and Want, R. (1994). Context-Aware Computing Applications. Proceedings of the Workshop on Mobile Computing Systems, IEEE.","DOI":"10.1109\/WMCSA.1994.16"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s00779-003-0253-8","article-title":"What We Talk About When We Talk About Context","volume":"8","author":"Dourish","year":"2004","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1504\/IJAHUC.2007.014070","article-title":"A Survey on Context-Aware Systems","volume":"2","author":"Baldauf","year":"2007","journal-title":"Int. J. Ad Hoc Ubiquitous Comput."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Selbst, A.D., Boyd, D., Friedler, S.A., Venkatasubramanian, S., and Vertesi, J. (2019, January 29\u201331). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the FAT* 2019, Atlanta, GA, USA.","DOI":"10.1145\/3287560.3287598"},{"key":"ref_84","unstructured":"European Commission (2021). Proposal for a Regulation on Artificial Intelligence (AI Act), European Commission. COM(2021) 206 Final."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop Explaining Black Box Machine Learning Models and Use Interpretable Models Instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable AI","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_87","unstructured":"Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., and Dennison, D. (2015, January 7\u201312). Hidden Technical Debt in Machine Learning Systems. Proceedings of the NeurIPS 2015, Montreal, QC, Canada."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Rader, E., Cotter, K., and Cho, J. (2018, January 21\u201326). Explanations as Mechanisms for Supporting Algorithmic Transparency. Proceedings of the CHI 2018, Montreal, QC, Canada.","DOI":"10.1145\/3173574.3173677"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1007\/s10208-021-09536-6","article-title":"Convergence Rates of First- and Higher-Order Dynamics for Solving Linear Ill-Posed Problems","volume":"22","author":"Dong","year":"2022","journal-title":"Found. Comput. Math."},{"key":"ref_90","unstructured":"Hadfield-Menell, D., Russell, S.J., Abbeel, P., and Dragan, A. (2016, January 5\u201310). Cooperative Inverse Reinforcement Learning. Proceedings of the NeurIPS 2016, Barcelona, Spain."},{"key":"ref_91","unstructured":"Wang, Z., Qin, Y., Zhou, W., Yan, J., Ye, Q., Neves, L., Liu, Z., and Ren, X. (2020, January 30). Learning from Explanations with Neural Execution Tree. Proceedings of the ICLR 2020, Addis Ababa, Ethiopia."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1023\/A:1022673506211","article-title":"Improving Generalization with Active Learning","volume":"15","author":"Cohn","year":"1994","journal-title":"Mach. Learn."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K. (November, January 30). Practical Secure Aggregation for Privacy-Preserving Machine Learning. Proceedings of the CCS 2017, Dallas, TX, USA.","DOI":"10.1145\/3133956.3133982"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., and Kochenderfer, M.J. (2017, January 24\u201328). Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks. Proceedings of the CAV 2017, Heidelberg, Germany.","DOI":"10.1007\/978-3-319-63387-9_5"},{"key":"ref_95","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press. [2nd ed.]."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/3\/175\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:03:38Z","timestamp":1772114618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/3\/175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":95,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["a19030175"],"URL":"https:\/\/doi.org\/10.3390\/a19030175","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,26]]}}}