The new settlement is with the DFEH, the California Division of Labor Standards Enforcement (DLSE) and certain individual claimants. All current and former California employees and contractors who identify as women and worked at Riot between November 2014 and present day qualify for a payout. At least 2,300 workers are eligible for part of the $80 million settlement, with those who started earlier or worked at the company longer receiving a larger allocation of the funds. Riot will pay into a settlement fund that will then be distributed to claimants following a court's approval. McCracken settled out of the suit for an undisclosed figure.

As artificial intelligence (A.I.) continues to rapidly integrate within everyday life, there are a few ethical dilemmas that have arisen synchronously and their impact on use cases have become the subject of much debate (Kilbertus et al., 2017; Hardt et al., 2016; Pazzanese, 2020). One such predicament that this paper hinges on has to do with inclusivity and marginalization (Bender et al., 2021). How are notions of participation affected by training data that reinforce hegemonic power in the formation of algorithmic models? Accordingly, this article will seek to spotlight ethical challenges within A.I. via a grounded interpretivist viewpoint gained by qualitatively investigating the literature in order to discuss bias amplifications. As outlined by Bender et al., (2021), there are several juristic and social dilemmas regarding the growth and utilization of language models.

Description:   *** Cannot provide sponsorship upon conversion. What is the specific title of the position? Lead Data and AI Engineer Work location? Preferred Locations - MA or MN (Client facilities). 100% telecommute is also considered. Work hours (ex. 9am-5pm day/night shifts rotating shifts etc)? 9-5 Please provide a summary of the project/initiative that this candidate will be working on? We are establishing Agile Data Warehouse in cloud and many new AI practices to enable personalization in various capabilities to improve employee experience. Please describe the team the candidate will be working with - how many members? 10 – 12 team members What is the break-down of the teams skill sets (ex: 1 PM 4 Developers etc.)? 1 PM, 3 Product owners 2 Sprint teams consisting - 1 Scrum Masters and 12 developers What are the top 5-10 responsibilities for this position (please be detailed as to what the candidate is expected to do or complete on a daily basis)? • Identify opportunities for Data Engineering and AI to enhance the core product platform, select the best machine learning techniques to the specific business problem and then build the models that solve the problem. • Architect and design AI/ML and Analytics solutions and cloud services • Own the end-end process, from recognizing the problem to implementing the solution. • Establish DataOps and MLOps principles and best practices What does the ideal candidate background look like (ex: healthcare specific background specific industry experience etc.)? a. Hands on experience with modern application – microservices, Cloud and CI/CD b. 5-7 years of hands on Data and AI engineering work c. Good communication with developing architecture and design documentation What skills/attributes are required (please be detailed as to number of years of experience for each skill)? • Bachelor's Degree or master's degree in Computer Science. • 5+ years of hands-on software engineering experience. • Demonstrated AI/ML solution design experience • Proven work experience in Spark, Python, SQL, Any RDBMS. • Familiarity with Azure Data Lake, Synapse, ADF, Power BI. • Experience building, deploying and maintaining ML models in production • Experience with MLOps tools such as ModelDB, MLFlow and Kubeflow. • Familiar with best practices in the data engineering and MLOps community. • Ability to convey complex concepts and ideas in a clear and concise manner to a wide range of audience internal business stakeholders, outside partners and technology teams. • To be able to work in a fast-paced agile development environment. • Proven track record in working with diverse teams to achieve goals • Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. What skills/attributes are preferred (what will set a candidate apart)? • Experience with AzureML • Expert in Azure Synapse, Azure Container Registry, Azure App services Of the required skills listed, which would you consider the top 3? Please list your expectations regarding years of experience for each requirement. a. AI/ ML Solution design b. Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. c. MLOps What will the interview process look like? (Video phone or in person? How many rounds? How technical will the interviews be?) a. How many rounds? 2-3 b. Video vs. phone? Video c. How technical will the interviews be? Mostly technical

Don Fancher is a Deloitte Risk & Financial Advisory Principal with Deloitte Financial Advisory Services LLP where he serves as the Global Leader of Deloitte Forensic as well as the Co-Leader of Deloitte's Legal Business Services practice. Mr. Fancher has over 30 years of experience assisting clients and leading practices in forensic, dispute consulting and legal transformation. He currently leads over 4,500 Deloitte professionals around the world serving clients in areas such as financial crime, disputes and investigations, business insurance, discovery, data governance, legal transformation, and contract lifecycle management. Mr. Fancher has significant experience assisting clients and counsel in performing forensic investigations and special reviews for matters regarding financial crime, misappropriation of assets, breach of fiduciary duty, and FCPA violations. These have included both individual employee and institution-wide schemes for misappropriating funds and/or improperly reporting asset values and financial performance.

Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (AI) still relies on the assumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on NLP and CV tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments potentially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed {\em FairRec} algorithm guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of {\em FairRec} in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

Over the last couple of decades, there has been a considerable effort devoted to the problem of updating logic programs under the stable model semantics (a.k.a. answer-set programs) or, in other words, the problem of characterising the result of bringing up-to-date a logic program when the world it describes changes. Whereas the state-of-the-art approaches are guided by the same basic intuitions and aspirations as belief updates in the context of classical logic, they build upon fundamentally different principles and methods, which have prevented a unifying framework that could embrace both belief and rule updates. In this paper, we will overview some of the main approaches and results related to answer-set programming updates, while pointing out some of the main challenges that research in this topic has faced.

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: 1) how to define a quantitative land-use configuration? 2) how to automate configuration planning? 3) how to evaluate the quality of a generated configuration? In this paper, we systematically address the three challenges. Specifically, 1) We define a land-use configuration as a longitude-latitude-channel tensor. 2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. 3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.

Acronyms and long-forms are commonly found in research documents, more so in documents from scientific and legal domains. Many acronyms used in such documents are domain-specific and are very rarely found in normal text corpora. Owing to this, transformer-based NLP models often detect OOV (Out of Vocabulary) for acronym tokens, especially for non-English languages, and their performance suffers while linking acronyms to their long forms during extraction. Moreover, pretrained transformer models like BERT are not specialized to handle scientific and legal documents. With these points being the overarching motivation behind this work, we propose a novel framework CABACE: Character-Aware BERT for ACronym Extraction, which takes into account character sequences in text and is adapted to scientific and legal domains by masked language modelling. We further use an objective with an augmented loss function, adding the max loss and mask loss terms to the standard cross-entropy loss for training CABACE. We further leverage pseudo labelling and adversarial data generation to improve the generalizability of the framework. Experimental results prove the superiority of the proposed framework in comparison to various baselines. Additionally, we show that the proposed framework is better suited than baseline models for zero-shot generalization to non-English languages, thus reinforcing the effectiveness of our approach. Our team BacKGProp secured the highest scores on the French dataset, second-highest on Danish and Vietnamese, and third-highest in the English-Legal dataset on the global leaderboard for the acronym extraction (AE) shared task at SDU AAAI-22.

With AI scaling at a tremendous rate in the last few years and no sign of slowing down, there is an increasing need for an open discussion around the ethics and trustworthiness of AI, including regulatory and legal risks. From Deepfakes which have facilitated million-pound fraudulent bank transfers to harmful gender stereotyping and racial bias, it is clear that we need regulations in place to create a safer, fairer world for everyone. To progress, we need to heighten our awareness around the changes that AI demands in our thinking, especially as, according to Gartner, by 2022, 85% of AI projects could deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them. If we don't, AI may trigger embarrassing situations, erode reputations, and damage businesses. However, there are also tremendous grey areas with regulation, with little consensus on how it should be done and most importantly, who should make the rules.