Deep Learning (DL) models have become the go-to method for addressing numerous Computer Vision (CV) tasks, such as image classification. Unlike traditional approaches that require manual feature extraction, DL streamlines the development of end-to-end pipelines that seamlessly integrate images as inputs to the learning process, thereby automating feature extraction and enhancing overall efficiency. This automation enables the training of DL models over extensive image datasets, which subsequently leads to enhanced model accuracy. However, the "black-box" nature of DL models presents challenges, as Machine Learning (ML) practitioners often struggle to understand the chain of transformations that a DL model adopts to map an image into the final prediction. This lack of transparency is considered to be hampering the adoption of DL models in real-world scenarios, due to a plethora of reasons: lack of trust from domain experts, impossibility of thorough debugging from practitioners, lack of compliance to legal requirements regarding explainability, and potential systemic bias in the trained model [18]. The research field of eXplainable Artificial Intelligence (XAI) tackles this problem by trying to provide more insights about the inner decision process of ML models [11]. However, most XAI techniques for CV are post-hoc: they are applied on trained ML models, and typically try to correlated portions of the image to the resulting label by means of input perturbation or maskings [29, 31, 33]. A few other approaches try to modify the training procedure itself, hoping to gain more control over the model's internals, while at the same time maintaining competitive classification performances. With this in mind, we remark how the standard DL pipeline for image classification trains the model to learn a mapping from images to labels.

The increasing usage of new data sources and machine learning (ML) technology in credit modeling raises concerns with regards to potentially unfair decision-making that rely on protected characteristics (e.g., race, sex, age) or other socio-economic and demographic data. The authors demonstrate the impact of such algorithmic bias in the microfinance context. Difficulties in assessing credit are disproportionately experienced among vulnerable groups, however, very little is known about inequities in credit allocation between groups defined, not only by single, but by multiple and intersecting social categories. Drawing from the intersectionality paradigm, the study examines intersectional horizontal inequities in credit access by gender, age, marital status, single parent status and number of children. This paper utilizes data from the Spanish microfinance market as its context to demonstrate how pluralistic realities and intersectional identities can shape patterns of credit allocation when using automated decision-making systems. With ML technology being oblivious to societal good or bad, we find that a more thorough examination of intersectionality can enhance the algorithmic fairness lens to more authentically empower action for equitable outcomes and present a fairer path forward. We demonstrate that while on a high-level, fairness may exist superficially, unfairness can exacerbate at lower levels given combinatorial effects; in other words, the core fairness problem may be more complicated than current literature demonstrates. We find that in addition to legally protected characteristics, sensitive attributes such as single parent status and number of children can result in imbalanced harm. We discuss the implications of these findings for the financial services industry.

Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longer-range correlations in connections and activity. In spite of this, there are few generative graph models that consider higher-order network structures and even fewer that focus on using motifs in models of dynamic graphs. Most existing generative models for temporal graphs strictly grow the networks via edge addition, and the models are evaluated using static graph structure metrics -- which do not adequately capture the temporal behavior of the network. To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND) -- a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). We compare DYMOND to three dynamic graph generative model baselines on real-world networks and show that DYMOND performs better at generating graph structure and node behavior similar to the observed network. We also propose a new methodology to adapt graph structure metrics to better evaluate the temporal aspect of the network. These metrics take into account the changes in overall graph structure and the individual nodes' behavior over time.

We propose a novel data-driven framework for algorithmic recourse that offers users interventions to change their predicted outcome. Existing approaches to compute recourse find a set of points that satisfy some desiderata -- e.g. an intervention in the underlying causal graph, or minimizing a cost function. Satisfying these criteria, however, requires extensive knowledge of the underlying model structure, often an unrealistic amount of information in several domains. We propose a data-driven, computationally efficient approach to computing algorithmic recourse. We do so by suggesting directions in the data manifold that users can take to change their predicted outcome. We present Stepwise Explainable Paths (StEP), an axiomatically justified framework to compute direction-based algorithmic recourse. We offer a thorough empirical and theoretical investigation of StEP. StEP offers provable privacy and robustness guarantees, and outperforms the state-of-the-art on several established recourse desiderata.

Sentences that present a complex syntax act as a major stumbling block for downstream Natural Language Processing applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easier to process, using a set of rewriting operations, such as reordering, deletion, or splitting. State-of-the-art syntactic TS approaches suffer from two major drawbacks: first, they follow a very conservative approach in that they tend to retain the input rather than transforming it, and second, they ignore the cohesive nature of texts, where context spread across clauses or sentences is needed to infer the true meaning of a statement. To address these problems, we present a discourse-aware TS approach that splits and rephrases complex English sentences within the semantic context in which they occur. Based on a linguistically grounded transformation stage that uses clausal and phrasal disembedding mechanisms, complex sentences are transformed into shorter utterances with a simple canonical structure that can be easily analyzed by downstream applications. With sentence splitting, we thus address a TS task that has hardly been explored so far. Moreover, we introduce the notion of minimality in this context, as we aim to decompose source sentences into a set of self-contained minimal semantic units. To avoid breaking down the input into a disjointed sequence of statements that is difficult to interpret because important contextual information is missing, we incorporate the semantic context between the split propositions in the form of hierarchical structures and semantic relationships. In that way, we generate a semantic hierarchy of minimal propositions that leads to a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences.

Summarization is the process of extracting the most important information from a text and presenting it in a condensed form. With vast amounts of information produced at an unprecedented rate, organizations and individuals alike face unique challenges, heightening the demand for effective summarization systems. For researchers of many fields, it is challenging to keep up with the latest developments in their field including Artificial Intelligence itself as vicariously indicated by the number of journal publications per year which has almost tripled since 2015 (D.

In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb their data on their devices before sending it out for analysis. However, as the collection of multiple sensitive information becomes more prevalent across various industries, collecting a single sensitive attribute under LDP may not be sufficient. Correlated attributes in the data may still lead to inferences about the sensitive attribute. This paper empirically studies the impact of collecting multiple sensitive attributes under LDP on fairness. We propose a novel privacy budget allocation scheme that considers the varying domain size of sensitive attributes. This generally led to a better privacy-utility-fairness trade-off in our experiments than the state-of-art solution. Our results show that LDP leads to slightly improved fairness in learning problems without significantly affecting the performance of the models. We conduct extensive experiments evaluating three benchmark datasets using several group fairness metrics and seven state-of-the-art LDP protocols. Overall, this study challenges the common belief that differential privacy necessarily leads to worsened fairness in machine learning.

Waymo starts limited trial of self-driving taxi service. The Uber driver behind the wheel during the first reported fatal collision involving a fully autonomous vehicle pleaded guilty to endangerment on Friday, and was sentenced to three years of supervised probation. In March of 2018, Elaine Herzberg was killed while walking her bike outside the lines of a crosswalk in suburban Phoenix. Driver Rafaela Vasquez, 49, was streaming a show on her phone and not watching the road at the moment of the fatal accident, authorities said. Video released by the Tempe Police Department from inside the Vasquez's Volvo XC90 SUV shows her looking down at the moment of the crash, during which the vehicle was moving at 40 miles per hour.

Jason Hong finds many similarities between FATE (Fairness, Accountability, Transparency, Ethics) and privacy.

The Uber safety driver at the wheel during the first known fatal self-driving car crash involving a pedestrian has pleaded guilty to and been sentenced for an endangerment charge. Rafaela Vasquez will serve three years of probation for her role in the 2018 Tempe, Arizona collision that killed Elaine Herzberg while she was jaywalking at night. The sentence honors the prosecutors' demands and is stiffer than the six months the defense team requested. The prosecution maintained that Vasquez was ultimately responsible. While an autonomous car was involved, Vasquez was supposed to concentrate on the road and take over if necessary.