This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, McMahan's 2017 FedAvg, and Bonawitz's 2017 aggregation to Zhao's 2018 non-IID, Kairouz's 2021 survey, and Zhu's 2019 leakage. This article presents a narrative review of that arc's canonical line: Dwork's 2006 ICALP, Shokri and Shmatikov's 2015 CCS, Konečný and colleagues's 2016 strategies, McMahan, Moore, Ramage, Hampson, and Arcas's 2017 FedAvg, Bonawitz and colleagues's 2017 secure aggregation, Zhao and colleagues's 2018 non-IID, Hard and colleagues's 2018 keyboard, Zhu, Liu, and Han's 2019 gradients, Yang and colleagues's 2019 concept, Li and colleagues's 2020 convergence, Li and colleagues's 2020 challenges, and Kairouz and colleagues's 2021 advances. The review is organized around three themes: the privacy's premise and the communication's bottleneck, in which the Dwork's noise and the Shokri-Shmatikov's gradients founded the distributed's training; the algorithm's and the deployment's era, in which the FedAvg's averaging, the secure's aggregation, and the keyboard's deployment gave the federation its engine; and the heterogeneity's and the frontier's era, in which the non-IID's data, the gradient's leakage, the convergence's proofs, and the open's problems carried the field into the privacy's science. It is concluded that federated learning is the machine learning's decentralization---and that its arc is the classroom's reading from the centralized's server to the privacy's frontier.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of federated learning and privacy as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Computer vision---making machines interpret images---traveled from blocks-world edge finders to deep convolutional networks matching human benchmarks, and its history is AI's most complete case of representation learning's triumph. This article presents a narrative review of the field's canonical line: Roberts's 1963 machine perception of solids, Marr's 1982 computational vision, Viola and Jones's 2001 face detection, Lowe's 2004 SIFT features, Dalal and Triggs's 2005 HOG descriptors, Felzenszwalb and colleagues' 2010 deformable part models, Szeliski's 2010 synthesis, Girshick's 2015 Fast R-CNN, Long, Shelhamer, and Darrell's 2015 fully convolutional nets, Simonyan and Zisserman's 2015 VGG, He and colleagues' 2016 ResNet, and Redmon and colleagues' 2016 YOLO. The synthesis is organized around three themes: representation, in which hand-engineered features gave way to learned hierarchies; architecture, in which convolution, regions, and residual depth solved recognition's geometry; and tasks, in which classification widened into detection, segmentation, and real-time video. It is concluded that vision's deep learning settlement reorganized the field around data and compute---and that its open problems, robustness and embodiment, define the current frontier.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Computer vision---making machines interpret images---traveled from blocks-world edge finders to deep convolutional networks matching human benchmarks, and its history is AI's most complete case of representation learning's triumph. This article presents a narrative review of the field's canonical line: Roberts's 1963 machine perception of solids, Marr's 1982 computational vision, Viola and Jones's 2001 face detection, Lowe's 2004 SIFT features, Dalal and Triggs's 2005 HOG descriptors, Felzenszwalb and colleagues' 2010 deformable part models, Szeliski's 2010 synthesis, Girshick's 2015 Fast R-CNN, Long, Shelhamer, and Darrell's 2015 fully convolutional nets, Simonyan and Zisserman's 2015 VGG, He and colleagues' 2016 ResNet, and Redmon and colleagues' 2016 YOLO. The synthesis is organized around three themes: representation, in which hand-engineered features gave way to learned hierarchies; architecture, in which convolution, regions, and residual depth solved recognition's geometry; and tasks, in which classification widened into detection, segmentation, and real-time video. It is concluded that vision's deep learning settlement reorganized the field around data and compute---and that its open problems, robustness and embodiment, define the current frontier.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical line: Dwork and colleagues' 2006 differential privacy, Dwork and Roth's 2014 foundations, Shokri and Shmatikov's 2015 privacy-preserving deep learning, Konecny and colleagues' 2016 communication strategies, McMahan and colleagues' 2017 FedAvg, Bonawitz and colleagues' 2017 secure aggregation, Zhao and colleagues' 2018 non-IID study, Bonawitz and colleagues' 2019 scale design, Yang and colleagues' 2019 concept paper, Zhu, Liu, and Han's 2019 gradient leakage, Li and colleagues' 2020 convergence analysis, and Kairouz and colleagues' 2021 open problems. The synthesis is organized around three themes: privacy, in which differential privacy's calculus and secure aggregation made learning without exposure precise; algorithm, in which FedAvg's weighted averaging met the heterogeneity of real devices and real data; and system, in which scale deployments faced stragglers, leakage, and the statistical reality of non-IID partitions. It is concluded that federated learning is privacy engineering's rare full-stack success---its limits as precisely mapped as its promise---and that its open problems are the field's charter: heterogeneity, security, and the economics of participation.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical line: Dwork and colleagues' 2006 differential privacy, Dwork and Roth's 2014 foundations, Shokri and Shmatikov's 2015 privacy-preserving deep learning, Konecny and colleagues' 2016 communication strategies, McMahan and colleagues' 2017 FedAvg, Bonawitz and colleagues' 2017 secure aggregation, Zhao and colleagues' 2018 non-IID study, Bonawitz and colleagues' 2019 scale design, Yang and colleagues' 2019 concept paper, Zhu, Liu, and Han's 2019 gradient leakage, Li and colleagues' 2020 convergence analysis, and Kairouz and colleagues' 2021 open problems. The synthesis is organized around three themes: privacy, in which differential privacy's calculus and secure aggregation made learning without exposure precise; algorithm, in which FedAvg's weighted averaging met the heterogeneity of real devices and real data; and system, in which scale deployments faced stragglers, leakage, and the statistical reality of non-IID partitions. It is concluded that federated learning is privacy engineering's rare full-stack success---its limits as precisely mapped as its promise---and that its open problems are the field's charter: heterogeneity, security, and the economics of participation.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Deep generative models---neural networks that learn data's distribution and sample from it---matured from variational autoencoders' latent geometry to diffusion models' state-of-the-art images. This article presents a narrative review of that arc's canonical line: Kingma and Welling's 2013 auto-encoding variational Bayes, Goodfellow and colleagues' 2014 generative adversarial networks, Mirza and Osindero's 2014 conditional GANs, Rezende and Mohamed's 2015 normalizing flows, Sohl-Dickstein and colleagues' 2015 nonequilibrium thermodynamics, Arjovsky and colleagues' 2017 Wasserstein GANs, Karras and colleagues' 2019 style-based generator, Ho and colleagues' 2020 denoising diffusion, Ramesh and colleagues' 2021 text-to-image generation, Dhariwal and Nichol's 2021 diffusion-beats-GANs result, Nichol and Dhariwal's 2021 improved diffusion, and Rombach and colleagues' 2022 latent diffusion. The synthesis is organized around three themes: latent foundations, in which autoencoders and flows made sampling principled; adversarial training, in which games between generator and discriminator produced realism; and diffusion's rise, in which denoising trajectories conquered synthesis. It is concluded that generative modeling's decade ran from likelihood's compromise to sampling's triumph---and that latent diffusion is the field's new foundation.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Medical geography---the study of where disease occurs and why place matters to health---began with a map: John Snow's 1855 tracing of cholera's deaths to the Broad Street pump, the field's founding demonstration that contagion has an address. This article presents a narrative review of that arc's canonical line: Snow's 1855 On the Mode of Communication of Cholera, May's 1958 The Ecology of Human Disease, Learmonth's 1988 Disease Ecology, Cliff and Haggett's 1988 Atlas of Disease Distributions, Gould's 1993 The Slow Plague, Smallman-Raynor and Cliff's 2004 War Epidemics, Haggett's 2000 The Geographical Structure of Epidemics, Meade and Earickson's 2000 Medical Geography, Mayer's 2000 essay on emerging infections, Ostfeld, Keesing, and Eviner's 2008 Infectious Disease Ecology, Keeling and Rohani's 2008 Modeling Infectious Diseases, and Brockmann and Helbing's 2013 hidden geometry of networked contagion. The review is organized around three themes: origin, in which Snow's mapping established place's causal role; diffusion, in which the waves' and hierarchies' and war's structures of epidemic spread were mapped; and synthesis, in which ecology's, modeling's, and network science's frameworks unified the field. It is concluded that medical geography is epidemiology's spatial conscience---the discipline that remembers every epidemic is also a map---and that its methods now run the world's outbreak responses.
Zen Revista, 10 GEOGRAPHY· Zenodo (CERN European Organi...· 0 citations
Deep generative models---neural networks that learn data's distribution and sample from it---matured from variational autoencoders' latent geometry to diffusion models' state-of-the-art images. This article presents a narrative review of that arc's canonical line: Kingma and Welling's 2013 auto-encoding variational Bayes, Goodfellow and colleagues' 2014 generative adversarial networks, Mirza and Osindero's 2014 conditional GANs, Rezende and Mohamed's 2015 normalizing flows, Sohl-Dickstein and colleagues' 2015 nonequilibrium thermodynamics, Arjovsky and colleagues' 2017 Wasserstein GANs, Karras and colleagues' 2019 style-based generator, Ho and colleagues' 2020 denoising diffusion, Ramesh and colleagues' 2021 text-to-image generation, Dhariwal and Nichol's 2021 diffusion-beats-GANs result, Nichol and Dhariwal's 2021 improved diffusion, and Rombach and colleagues' 2022 latent diffusion. The synthesis is organized around three themes: latent foundations, in which autoencoders and flows made sampling principled; adversarial training, in which games between generator and discriminator produced realism; and diffusion's rise, in which denoising trajectories conquered synthesis. It is concluded that generative modeling's decade ran from likelihood's compromise to sampling's triumph---and that latent diffusion is the field's new foundation.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Graph neural networks---learning over relational, irregular structure by passing messages between nodes---generalized deep learning's grids to the graph: molecules, social networks, knowledge bases, and the web. This article presents a narrative review of that arc's canonical line: Sperduti and Starita's 1997 structure classification, Gori, Monfardini, and Scarselli's 2005 graph-domain learning, Scarselli and colleagues' 2009 GNN model, Bruna and colleagues' 2014 spectral networks, Defferrard and colleagues' 2016 localized filtering, Kipf and Welling's 2017 graph convolutions, Gilmer and colleagues' 2017 message passing, Hamilton, Ying, and Leskovec's 2017 GraphSAGE, Velickovic and colleagues' 2018 attention, Ying and colleagues' 2019 GNNExplainer, Wu and colleagues' 2021 comprehensive survey, and Bronstein and colleagues' 2021 geometric deep learning. The synthesis is organized around three themes: recursion, in which state propagation over nodes founded learning on graphs; convolution, in which spectral theory and message passing gave the graph a deep architecture; and geometry, in which attention, pooling, explainability, and symmetry made the network general. It is concluded that the GNN is deep learning's relational settlement---convolution's invariance learned from graph geometry rather than grid regularity---and that its message-passing abstraction is one of machine learning's cleanest unifications.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
