We are increasingly turning to machine learning systems to support human decisions. While decision makers may be subject to many forms of prejudice and bias, the promise and hope is that machines would be able to make more equitable decisions. Unfortunately, whether because they are fitted on already biased data or otherwise, there are concerns that some of these data driven recommendation systems treat members of different classes differently, perpetrating biases, providing different degrees of utilities, and inducing disparities. The examples that have emerged are quite varied: 1. Criminal justice: courts in the United States use COMP AS--a commercially available algorithm to assess a criminal defendant's likelihood of becoming a recidivist--to help them decide who should receive parole, based on records collected through the criminal justice system. In 2016 ProPublica analyzed COMP AS and "found that black defendants were far more likely than white defendants to be incorrectly judged to be at a higher risk of recidivism, while white defendants were more likely than black defendants to be incorrectly flagged as low risk" [1].

In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested term extraction and does not crucially need extra (extracted) features. Results show that it can achieve high recall and a comparable precision on term extraction task with inputting segmented raw text.

We present a hierarchical reinforcement learning (HRL) or options framework for identifying decision states. Informally speaking, these are states considered important by the agent's policy e.g. , for navigation, decision states would be crossroads or doors where an agent needs to make strategic decisions. While previous work (most notably Goyal et. al., 2019) discovers decision states in a task/goal specific (or 'supervised') manner, we do so in a goal-independent (or 'unsupervised') manner, i.e. entirely without any goal or extrinsic rewards. Our approach combines two hitherto disparate ideas - 1) \emph{intrinsic control} (Gregor et. al., 2016, Eysenbach et. al., 2018): learning a set of options that allow an agent to reliably reach a diverse set of states, and 2) \emph{information bottleneck} (Tishby et. al., 2000): penalizing mutual information between the option $\Omega$ and the states $s_t$ visited in the trajectory. The former encourages an agent to reliably explore the environment; the latter allows identification of decision states as the ones with high mutual information $I(\Omega; a_t | s_t)$ despite the bottleneck. Our results demonstrate that 1) our model learns interpretable decision states in an unsupervised manner, and 2) these learned decision states transfer to goal-driven tasks in new environments, effectively guide exploration, and improve performance.

Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Further efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.

Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the learning rate or step size). To begin to address this challenge, we examine the use of online step-size adaptation using a sensor-rich robotic arm. Our method of choice, Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step sizes on a feature level; importantly, TIDBD allows step-size tuning and representation learning to occur at the same time. We show that TIDBD is a practical alternative for classic Temporal-Difference (TD) learning via an extensive parameter search. Both approaches perform comparably in terms of predicting future aspects of a robotic data stream. Furthermore, the use of a step-size adaptation method like TIDBD appears to allow a system to automatically detect and characterize common sensor failures in a robotic application. Together, these results promise to improve the ability of robotic devices to learn from interactions with their environments in a robust way, providing key capabilities for autonomous agents and robots.

Semantically non-compositional phrases constitute an intriguing research topic in Natural Language Processing. Semantic non-compositionality --the situation when the meaning of a phrase cannot be derived from the meaning of its components, is the main characteristic of such phrases, however, they bear other characteristics such as high statistical association and non-substitutability. In this work, we present a model for identifying non-compositional phrases that takes into account all of these characteristics. We show that the presented model remarkably outperforms the existing models of identifying non-compositional phrases that mostly focus only on one of these characteristics.

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a method that combines a knowledge-based approach and a reinforcement learning (RL) technique into one method for intelligent problem solving. The proposed approach focuses on multi-expert knowledge acquisition, with the reinforcement learning being applied as a conflict resolution strategy aimed at integrating the knowledge of multiple exerts into one knowledge base. The article describes the KB-RL approach in detail and applies the reported method to one of the most challenging problems of current Artificial Intelligence (AI) research, namely playing a strategy game. The results show that the KB-RL system is able to play and complete the full FreeCiv game, and to win against the computer players in various game settings. Moreover, with more games played, the system improves the gameplay by shortening the number of rounds that it takes to win the game. Overall, the reported experiment supports the idea that, based on human knowledge and empowered by reinforcement learning, the KB-RL system can deliver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience.

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. A video of President Donald Trump singing "America the Beautiful" received over 50,000 views on Instagram when it was posted just before the July 4th holiday. Did the president really record a video of himself singing and post it to social media? But with the growing prevalence of altered videos and intentional mischief, the question "Is it real?" is one we will need to ask with more regularity.

Facebook Inc. has been paying hundreds of outside contractors to transcribe clips of audio from users of its services, according to people with knowledge of the work. Facebook has reportedly been paying contractors to transcribe the audio chats of users on its various platforms. According to Bloomberg, which cites people with knowledge of the work, contractors were given audio clips with no context of where they were obtained and told to transcribe them. Facebook told Bloomberg the users affected by this agreed to have voice chats transcribed through its Messenger app. "Much like Apple and Google, we paused human review of audio more than a week ago," said Facebook in a statement to USA TODAY.