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Found 27 papers, 12 papers with code
Interpretable and Fair Mechanisms for Abstaining Classifiers
In this setting, often fairness concerns arise when the abstention mechanism solely reduces errors for the majority groups of the data, resulting in increased performance differences across demographic groups.
Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts
However, when intervened on, CBMs assume the availability of humans that can identify the need to intervene and always provide correct interventions.
Mathematical Foundation of Interpretable Equivariant Surrogate Models
This paper introduces a rigorous mathematical framework for neural network explainability, and more broadly for the explainability of equivariant operators called Group Equivariant Operators (GEOs) based on Group Equivariant Non-Expansive Operators (GENEOs) transformations.
Embracing Diversity: A Multi-Perspective Approach with Soft Labels
Prior studies show that adopting the annotation diversity shaped by different backgrounds and life experiences and incorporating them into the model learning, i. e. multi-perspective approach, contribute to the development of more responsible models.
Multi-Perspective Stance Detection
Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences.
Characterizing Model Collapse in Large Language Models Using Semantic Networks and Next-Token Probability
As synthetic content increasingly infiltrates the web, generative AI models may experience an autophagy process, where they are fine-tuned using their own outputs.
A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions
Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests.
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions.
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
Handling Missing Values in Local Post-hoc Explainability
Missing data are quite common in real scenarios when using Artificial Intelligence (AI) systems for decision-making with tabular data and effectively handling them poses a significant challenge for such systems.
Human-AI Coevolution
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature.
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
However, typically, the variables of a dataset depend on one another, and these dependencies are not considered in data generation leading to the creation of implausible records.
Dense Hebbian neural networks: a replica symmetric picture of supervised learning
We consider dense, associative neural-networks trained by a teacher (i. e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations.
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations.
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs.
Explainable Deep Image Classifiers for Skin Lesion Diagnosis
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems.
Understanding peacefulness through the world news
Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers.
Benchmarking and Survey of Explanation Methods for Black Box Models
The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions.
Home and destination attachment: study of cultural integration on Twitter
In particular, the common language between home and destination countries corresponds to increased home attachment, as does low proficiency in the host language.
GLocalX -- From Local to Global Explanations of Black Box AI Models
Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other.
Predicting seasonal influenza using supermarket retail records
In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.
Local Rule-Based Explanations of Black Box Decision Systems
Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome.
PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach
The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e. g., tackles, passes, shots, etc.).
A Survey Of Methods For Explaining Black Box Models
The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation.
NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks
Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground.
Social and Information Networks 05C85, 60J60, 90C35 G.2.2; F.2.1
Human Perception of Performance
Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports.
