Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

Pre-trained Transformers currently dominate most NLP tasks. They impose, however, limits on the maximum input length (512 sub-words in BERT), which are too restrictive in the legal domain. Even sparse-attention models, such as Longformer and BigBird, which increase the maximum input length to 4,096 sub-words, severely truncate texts in three of the six datasets of LexGLUE. Simpler linear classifiers with TF-IDF features can handle texts of any length, require far less resources to train and deploy, but are usually outperformed by pre-trained Transformers. We explore two directions to cope with long legal texts: (i) modifying a Longformer warm-started from LegalBERT to handle even longer texts (up to 8,192 sub-words), and (ii) modifying LegalBERT to use TF-IDF representations. The first approach is the best in terms of performance, surpassing a hierarchical version of LegalBERT, which was the previous state of the art in LexGLUE. The second approach leads to computationally more efficient models at the expense of lower performance, but the resulting models still outperform overall a linear SVM with TF-IDF features in long legal document classification.

In 2021 the U.S. Patent and Trademark Office (USPTO) developed an Artificial Intelligence (AI) based prototype search system for use by examiners during examination of patent applications. As previously discussed by Mintz, the AI search system aimed to help identify relevant documents and provide suggestions to examiners for additional areas to search. The USPTO found searching success with the prototype, for the USPTO just launched an AI-based "Similarity Search" in the Patents End-to-End (PE2E) prior art search suite for patents examiners. As explained by the USPTO, a patent examiner provides input, including a patent specification, to the "Similarity Search" feature. The feature then uses AI models to identify and, within seconds, output U.S. and foreign patent references similar to the patent application being examined.

A class-action lawsuit has been launched against OpenAI and Microsoft over GitHub Copilot. GitHub Copilot uses technology from OpenAI to help generate code and speed up software development. Microsoft says that it is trained on "billions of lines of public code … written by others." Last month, developer and lawyer Matthew Butterick announced that he'd partnered with the Joseph Saveri Law Firm to investigate whether Copilot infringed on the rights of developers by scraping their code and not providing due attribution. This could unwittingly cause serious legal problems for GitHub Copilot users. "Copilot leaves copyleft compliance as an exercise for the user.

In 2021 the U.S. Patent and Trademark Office (USPTO) developed an Artificial Intelligence (AI) based prototype search system for use by examiners during examination of patent applications. As previously discussed by Mintz, the AI search system aimed to help identify relevant documents and provide suggestions to examiners for additional areas to search. The USPTO found searching success with the prototype, for the USPTO just launched an AI-based "Similarity Search" in the Patents End-to-End (PE2E) prior art search suite for patents examiners. As explained by the USPTO, a patent examiner provides input, including a patent specification, to the "Similarity Search" feature. The feature then uses AI models to identify and, within seconds, output U.S. and foreign patent references similar to the patent application being examined.

Artificial intelligence will someday know you better than you know yourself. That day may be sooner than we realize with the amount of data collected on all humans and their environments increasing exponentially. So where are the rules, and what are our rights? Over the past few centuries, data has been collected at high levels: primarily on companies, countries, societies, cultures, religions and other high-level aggregations. With the data age in full swing, we are delving into the frontier of individual data--a level previously unreached in terms of deeply knowing and connecting humans.

The Data Engineer requires strong technical background, hands-on experience in Data Modeling, XML's and other Azure technologies along with data analysis skills and communication abilities. Reporting to the COG IPL and Distribution, the candidate will participate and execute multiple project teams for the delivery of data solutions, including new development, maintenance and enhancements as well as assist with the daily operations. If you apply for this opportunity we will get you resume and its contain personal data whose treatment has been authorized by its owner for Digital OnUs, S. de RL de CV (the "Company"). If you are not the owner of this information or have no relation whatsoever with the subjects treated in it, you are requested in the most attentive way not to make copies of it and / or its attached files and delete it immediately, under the risk of being considered as responsible for the unauthorized treatment of personal data in accordance with the Federal Law on Protection of Personal Data Held by Private Parties, its Regulations, and other applicable regulations.

A major concern of Machine Learning (ML) models is their opacity. They are deployed in an increasing number of applications where they often operate as black boxes that do not provide explanations for their predictions. Among others, the potential harms associated with the lack of understanding of the models' rationales include privacy violations, adversarial manipulations, and unfair discrimination. As a result, the accountability and transparency of ML models have been posed as critical desiderata by works in policy and law, philosophy, and computer science. In computer science, the decision-making process of ML models has been studied by developing accountability and transparency methods. Accountability methods, such as adversarial attacks and diagnostic datasets, expose vulnerabilities of ML models that could lead to malicious manipulations or systematic faults in their predictions. Transparency methods explain the rationales behind models' predictions gaining the trust of relevant stakeholders and potentially uncovering mistakes and unfairness in models' decisions. To this end, transparency methods have to meet accountability requirements as well, e.g., being robust and faithful to the underlying rationales of a model. This thesis presents my research that expands our collective knowledge in the areas of accountability and transparency of ML models developed for complex reasoning tasks over text.

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built on physical measurement data collected from sensors, they may not be suitable for predicting public health effects experienced from pollution exposure. This study aims to develop and validate models to nowcast the observed pollution levels using Web search data, which is publicly available in near real-time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level, by using generally available meteorological data and aggregate Web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting three critical air pollutants (ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)), across ten major U.S. metropolitan statistical areas (MSAs) in 2017 and 2018.

Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.