Papers

How Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity

Algorithmic recourse, or enabling individuals to reverse a negative outcome, has gained attention as a means of supporting human agency in interactions with artificial intelligence (AI) systems. However, recent work has shown that even if a decision-making classifi

SAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs

Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model, highlighting the critical need to safeguard widely-deployed models. Safeguarding approaches, which include f

SHAP-based Explanations are Sensitive to Feature Representation

Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an “interpretable” feature representation. In tabular data, feature values themselves are often considered interpretable. This

ShaRP: Explaining Rankings and Preferences with Shapley Values

Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizations, and population groups, it is essential to understand them—to help individuals i

The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness

Algorithmic recourse, or providing recommendations to individuals who receive an unfavorable outcome from an algorithmic system on how they can take action and change that outcome, is an important tool for giving individuals agency against algorithmic decision syst

Geometric SMOTE for imbalanced datasets with nominal and continuous features

Imbalanced learning can be addressed in 3 different ways: Resampling, algorithmic modifications and cost-sensitive solutions. Resampling, and specifically oversampling, are more general approaches when opposed to algorithmic and cost-sensitive methods. Since the pr

Improving Active Learning Performance through the Use of Data Augmentation

Active learning (AL) is a well-known technique to optimize data usage in training, through the interactive selection of unlabeled observations, out of a large pool of unlabeled data, to be labeled by a supervisor. Its focus is to find the unlabeled observations tha

Setting the Right Expectations: Algorithmic Recourse Over Time

Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take action against an undesirable outcome made by an algorithmic system, is receiving gr

Tabular and latent space synthetic data generation: a literature review

The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generati

The Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classification

Research trends and applications of data augmentation algorithms

In the Machine Learning research community, there is a consensus regarding the relationship between model complexity and the required amount of data and computation power. In real world applications, these computational requirements are not always available, motiva

Improving Imbalanced Land Cover Classification with K-Means SMOTE: Detecting and Oversampling Distinctive Minority Spectral Signatures

Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for

Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification

In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both

Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

the automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. the ability to build robust automatic classifiers and produce accurate maps can

Harnessing Big Data to Inform Tourism Destination Management Organizations