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Found 9 papers, 5 papers with code
DTOR: Decision Tree Outlier Regressor to explain anomalies
Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains.
Evaluative Item-Contrastive Explanations in Rankings
The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry.
Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms
In the recent years, the raise in the usage and efficiency of Artificial Intelligence and, more in general, of Automated Decision-Making systems has brought with it an increasing and welcome awareness of the risks associated with such systems.
Disambiguation of Company names via Deep Recurrent Networks
Moreover, we show that Active Learning prioritisation is indeed helpful when labelling resources are limited, and let the learning models reach the out-of-sample performance saturation with less labelled data with respect to standard (random) data labelling approaches.
Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation
Machine learning applications are becoming increasingly pervasive in our society.
Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
In order to create reliable, safe and trustworthy systems through human-centred artificial intelligence (HCAI) design, recent efforts have produced user interfaces (UIs) for AI experts to investigate the fairness of AI models.
Counterfactual Explanations as Interventions in Latent Space
Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems.
A Clarification of the Nuances in the Fairness Metrics Landscape
In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing with Artificial Intelligence.
BeFair: Addressing Fairness in the Banking Sector
Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts.
