Rationalization for explainable NLP: a survey.

Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. However, this improvement comes at the expense of model explainability. Black-box models make it difficult to understand the internals of a system and the process it takes to arrive at an output. Numerical (LIME, Shapley) and visualization (saliency heatmap) explainability techniques are helpful; however, they are insufficient because they require specialized knowledge. These factors led rationalization to emerge as a more accessible explainable technique in NLP. Rationalization justifies a model's output by providing a natural language explanation (rationale). Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users. Since rationalization is a relatively new field, it is disorganized. As the first survey, rationalization literature in NLP from 2007 to 2022 is analyzed. This survey presents available methods, explainable evaluations, code, and datasets used across various NLP tasks that use rationalization. Further, a new subfield in Explainable AI (XAI), namely, Rational AI (RAI), is introduced to advance the current state of rationalization. A discussion on observed insights, challenges, and future directions is provided to point to promising research opportunities.

Identifying False Human Papillomavirus (HPV) Vaccine Information and Corresponding Risk Perceptions From Twitter: Advanced Predictive Models.

BACKGROUND: The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy. OBJECTIVE: Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media. METHODS: This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine-related information on Twitter. RESULTS: We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine-related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary. CONCLUSIONS: Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.

Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist's dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

Biosystems Design by Machine Learning.

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Who is Saying What on Twitter: An Analysis of Messages with References to HIV and HIV Risk Behavior.

This research aimed to determine the nature of social media discussions about HIV. With the goal of conducting a descriptive analysis, we collected almost 1,000 tweets posted February to September 2015. The sample of tweets included keywords related to HIV or behavioral risk factors (e.g., sex, drug use) and was coded for content (e.g., HIV), behavior change strategies, and message source. Seven percent of tweets concerned HIV/AIDS, which were often referred to as jokes or insults. The majority of tweets coded as behavior change attempts involved attitude change strategies. The majority of the tweets (80%) came from private users (vs. organizations). Different types of sources employed different types of behavior change strategies: For instance, private users, compared to experts or organizations, included more strategies to decrease detrimental attitudes (29% versus 6%, p < .001), and also more strategies to counter myths and misinformation (6% versus 1%, p = .008). In summary, tweets related to HIV/AIDS and associated risk factors frequently use the terms in jokes and insults, come largely from private users, and entail attitudinal and informational strategies. Online health campaigns with clear calls to action and corrections of misinformation may make important contributions to social media conversations about HIV/AIDS.

Esta investigación tuvo el objectivo de caracterizar las discusiones sobre VIH en los medios sociales. Con el objetivo de realizar un análisis descriptivo, recogimos alrededor de mil tweets entre febrero y septiembre del 2015. Estos tweets fueron seleccionados si incluían palabras claves relacionadas con el VIH o con factores de riesgo conductual tales como sexo o uso de drogas. Cuatro codificadores clasificaron los tweets en función del contenido (e.g., el VIH como enfermedad, referido a un product o servicio), la estrategia de cambio conductual (cambio conductual, llamada a la acción, o corrección de mitos), y la fuente del mensaje (e.g., usuarios privados, expertos, empresas comerciales). La mayoría de los tweets (80%) provenía de usuarios privados en lugar de institucionales. El 7% de los tweets se refería estrictamente al VIH u otras infecciones de transmisión sexual, frecuentemente utilizando esos términos como bromas o insultos, tales como escribir que una experiencia displacentera "me dio SIDA". La mayoría de los intentos de cambio conductual incluía estrategias de reducción de actitudes negativas. Fuentes de distintos tipos empleaban estrategias de cambio conductual de distintos tipos. Por ejemplo, usuarios privados (comparados con expertos, organizaciones comerciales, y otras organizaciones, tal como periódicos y ONGs), publicaban más mesajes clasificados como estrategias de promoción de actitudes negativas (29% versus 6%, p < .001), y tenían más correcciones de mitos (6% versus 1%, p = .008). En resumen, los tweets que mencionan el VIH o factores de riesgo de VIH utilizan los términos en bromas e insultos con gran frecuencia, provienen mayormente de usuarios privados, e incluyen estrategias de cambio de actitud. Las campañas de Internet con llamadas claras a la acción y con correcciones de mitos pueden hacer contribuciones importantes a las conversaciones sobre VIH en los medios sociales.

Who is saying what on Twitter: An analysis of messages with references to HIV and HIV risk behavior / Quién dice qué en Twitter: Mensajes con referencia a VIH y conducta de riesgo de VIH

Abstract: This research aimed to determine the nature of social media discussions about HIV. With the goal of conducting a descriptive analysis, we collected almost 1,000 tweets posted February to September 2015. The sample of tweets included keywords related to HIV or behavioral risk factors (e.g., sex, drug use) and was coded for content (e.g., HIV), behavior change strategies, and message source. Seven percent of tweets concerned HIV/AIDS, which were often referred to as jokes or insults. The majority of tweets coded as behavior change attempts involved attitude change strategies. The majority of the tweets (80%) came from private users (vs. organizations). Different types of sources employed different types of behavior change strategies: For instance, private users, compared to experts or organizations, included more strategies to decrease detrimental attitudes (29% versus 6%, p < .001), and also more strategies to counter myths and misinformation (6% versus 1%, p = .008). In summary, tweets related to HIV/AIDS and associated risk factors frequently use the terms in jokes and insults, come largely from private users, and entail attitudinal and informational strategies. Online health campaigns with clear calls to action and corrections of misinformation may make important contributions to social media conversations about HIV/AIDS.

Resumen: Esta investigación tuvo el objetivo de caracterizar las discusiones sobre VIH en los medios sociales. Con el objetivo de realizar un análisis descriptivo, recogimos alrededor de mil tweets entre febrero y septiembre del 2015. Estos tweets fueron seleccionados si incluían palabras claves relacionadas con el VIH o con factores de riesgo conductual tales como sexo o uso de drogas. Cuatro codificadores clasificaron los tweets en función del contenido (e.g., el VIH como enfermedad, referido a un producto o servicio), la estrategia de cambio conductual (cambio conductual, llamada a la acción, o corrección de mitos), y la fuente del mensaje (e.g., usuarios privados, expertos, empresas comerciales). La mayoría de los tweets (80%) provenía de usuarios privados en lugar de institucionales. El 7% de los tweets se refería estrictamente al VIH u otras infecciones de transmisión sexual, frecuentemente utilizando esos términos como bromas o insultos, tales como escribir que una experiencia displacentera "me dio SIDA". La mayoría de los intentos de cambio conductual incluía estrategias de reducción de actitudes negativas. Fuentes de distintos tipos empleaban estrategias de cambio conductual de distintos tipos. Por ejemplo, usuarios privados (comparados con expertos, organizaciones comerciales, y otras organizaciones, tal como periódicos y ONGs), publicaban más mensajes clasificados como estrategias de promoción de actitudes negativas (29% versus 6%, p < .001), y tenían más correcciones de mitos (6% versus 1%, p = .008). En resumen, los tweets que mencionan el VIH o factores de riesgo de VIH utilizan los términos en bromas e insultos con gran frecuencia, provienen mayormente de usuarios privados, e incluyen estrategias de cambio de actitud. Las campañas de Internet con llamadas claras a la acción y con correcciones de mitos pueden hacer contribuciones importantes a las conversaciones sobre VIH en los medios sociales.