{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":32,"n_cols":5,"rows_kept":32,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[25,2,25,3],[26,2,26,3],[28,2,28,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910983303703321"]
[null,null,"Autore",null,"Meo Rosa"]
[null,null,"Titolo",null,"Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part I / / edited by Rosa Meo, Fabrizio Silvestri"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031746307"]
[{"$nulls":4},"3031746309"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (770 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2133"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Data structures (Computer science)"]
[{"$nulls":4},"Information theory"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Data Structures and Information Theory"]
[{"$nulls":4},"Computers and Education"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial.  -- Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces.  -- Predicate-based explanation of a Reinforcement Learning agent via action importance evaluation.  -- Local interpretability of random forests for multi-target regression.  -- On "]
[{"$nulls":4},"the Adaptability of Attention-Based Interpretability in Different Transformer Architectures for Multi-Class Classification Tasks.  -- Analyzing the Explanation and Interpretation Potential of Matrix Capsule Networks.  -- An Efficient Shapley Value Computation for the Naive Bayes Classifier.  -- An Experimental Investigation into the Evaluation of Explainability Methods for Computer Vision.  -- Natively Interpretable t-SNE.  -- BIAS 2023 - 3rd Workshop on Bias and Fairness in AI.  -- Automated discovery of trade-off between utility, privacy and fairness in machine learning models.  -- Facial Analysis Systems and Down Syndrome.  -- Mitigating Discrimination in Insurance with Wasserstein Barycenters.  -- Counterfactual Explanations for Recommendation Bias.  -- Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias.  -- Towards Fair Face Verification: An In-depth Analysis of Demographic Biases.  -- How Different Is Stereotypical Bias Across Languages?.  -- Sampling strategies for mitigating bias in face synthesis methods.  -- Towards Inclusive Fairness Evaluation via Eliciting Disagreement Feedback from Non-Expert Stakeholders.  -- Beliefs, Relationships, and Equality: An Alternative Source of Discrimination in a Symmetric Hiring Market via Threats.  -- Biased Data in Conversational Agents.  -- Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias.  -- How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses.  -- Explainable Artificial Intelligence: From Static to Dynamic.  -- Shapley-Based Feature Selection for Online Algorithm Selection.  -- Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes.  -- Dynamic Interpretability for Model Comparison via Decision Rules.  -- Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning.  -- A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning.  -- ML, Law and Society.  -- The AI Act Is Coming: Are E-Health Manufacturers Ready?.  -- Techniques to achieve anonymisation of health data: When are they sufficient to be considered as legally complaint?.  -- Mental state classification using EEG signals: ethics, law and challenges.  -- Trustworthy AI Development in Education.  -- Variants analysis in judicial trials: Challenges and initial results.  -- FLAIRS: Federated Learning AI Regulatory Sandbox.  -- Towards a Process View of Algorithmic Fairness.  -- A practical application of Artificial Intelligence techniques for legal context analysis.  -- Why Fair Automated Hiring Systems Breach EU Non-Discrimination Law.  -- Enhancing e-Justice: Assessing the effectiveness of specialized LLMs' applications with Cicero.  -- Process mining on a public procurement dataset: a case study.  -- Google Topics as a way out of the cookie dilemma?."]
[null,null,null,"Sommario/riassunto","The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets "]
[{"$nulls":4},"explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning. ."]