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Collaborating Authors

Mohammad, Saif M.


Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

arXiv.org Artificial Intelligence

The importance and pervasiveness of emotions in our lives makes affective computing a tremendously important and vibrant line of work. Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for suppressing dissidents and manipulating voters). Thus, it is imperative that the affective computing community actively engage with the ethical ramifications of their creations. In this paper, I have synthesized and organized information from AI Ethics and Emotion Recognition literature to present fifty ethical considerations relevant to AER. Notably, the sheet fleshes out assumptions hidden in how AER is commonly framed, and in the choices often made regarding the data, method, and evaluation. Special attention is paid to the implications of AER on privacy and social groups. The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems. Additionally, the sheet acts as a useful introductory document on emotion recognition (complementing survey articles).


Ethics Sheets for AI Tasks

arXiv.org Artificial Intelligence

Several high-profile events, such as the use of biased recidivism systems and mass testing of emotion recognition systems on vulnerable sub-populations, have highlighted how technology will often lead to more adverse outcomes for those that are already marginalized. In this paper, I will make a case for thinking about ethical considerations not just at the level of individual models and datasets, but also at the level of AI tasks. I will present a new form of such an effort, Ethics Sheets for AI Tasks, dedicated to fleshing out the assumptions and ethical considerations hidden in how a task is commonly framed and in the choices we make regarding the data, method, and evaluation. Finally, I will provide an example ethics sheet for automatic emotion recognition. Together with Data Sheets for datasets and Model Cards for AI systems, Ethics Sheets aid in the development and deployment of responsible AI systems.


How Translation Alters Sentiment

Journal of Artificial Intelligence Research

Sentiment analysis research has predominantly been on English texts. Thus there exist many sentiment resources for English, but less so for other languages. Approaches to improve sentiment analysis in a resource-poor focus language include: (a) translate the focus language text into a resource-rich language such as English, and apply a powerful English sentiment analysis system on the text, and (b) translate resources such as sentiment labeled corpora and sentiment lexicons from English into the focus language, and use them as additional resources in the focus-language sentiment analysis system. In this paper we systematically examine both options. We use Arabic social media posts as stand-in for the focus language text. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We show that Arabic sentiment analysis systems benefit from the use of automatically translated English sentiment lexicons. We also conduct manual annotation studies to examine why the sentiment of a translation is different from the sentiment of the source word or text. This is especially relevant for building better automatic translation systems. In the process, we create a state-of-the-art Arabic sentiment analysis system, a new dialectal Arabic sentiment lexicon, and the first Arabic-English parallel corpus that is independently annotated for sentiment by Arabic and English speakers.