Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

MacAvaney, Sean; Mittu, Anjali; Coppersmith, Glen; Leintz, Jeff; Resnik, Philip

doi:10.18653/v1/2021.clpsych-1.7

Cited by 30 publications

(25 citation statements)

References 31 publications

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“…Public Health: Assist public health research projects, including those on loneliness (Guntuku et al 2019;Kiritchenko et al 2020), depression (De Choudhury et al 2013;Resnik et al 2015), suicidality prediction (MacAvaney et al 2021), bipolar disorder (Karam et al 2014), stress (Eichstaedt et al 2015), and well-being (Schwartz et al 2013).…”

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confidence: 99%

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Mohammad

2022

Computational Linguistics

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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. Along the way, key recommendations are made for responsible AER. 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).

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confidence: 99%

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Mohammad

2022

Computational Linguistics

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“…For instance, language models and statistical parsers were used to explain language processing difficulty (Sarti et al, 2021;Rathi, 2021;Meister et al, 2022) and incrementality (Merkx & Frank, 2021;Stanojević et al, 2021), syntactic agreement processes (Ryu & Lewis, 2021), brain representations of abstract and concrete concepts (Anderson et al, 2017;Ramakrishnan & Deniz, 2021), prediction of gaze behaviour (Hollenstein et al, 2021), among others. In clinical psychology, Transformer-based language models are being used to formulate cognitive models that better explain human emotions (Guo & Choi, 2021), comprehension deficit in aphasia subjects (Guo & Choi, 2021), and even improve suicidal prevention systems (MacAvaney et al, 2021).…”

Section: Cognitive Models For Nlp Tasksmentioning

confidence: 99%

On the Trade-off between Redundancy and Local Coherence in Summarization

Cardenas¹,

Gallé²,

Cohen³

2022

Preprint

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Extractive summarization systems are known to produce poorly coherent and, if not accounted for, highly redundant text. In this work, we tackle the problem of summary redundancy in unsupervised extractive summarization of long, highly-redundant documents. For this, we leverage a psycholinguistic theory of human reading comprehension which directly models local coherence and redundancy. Implementing this theory, our system operates at the proposition level and exploits properties of human memory representations to rank similarly content units that are coherent and non-redundant, hence encouraging the extraction of less redundant final summaries. Because of the impact of the summary length on automatic measures, we control for it by formulating content selection as an optimization problem with soft constraints in the budget of information retrieved. Using summarization of scientific articles as a case study, extensive experiments demonstrate that the proposed systems extract consistently less redundant summaries across increasing levels of document redundancy, whilst maintaining comparable performance (in terms of relevancy and local coherence) against strong unsupervised baselines according to automated evaluations.

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“…This posits a heavy burden on the capability of the research community to develop wide-coverage models (see also Section 5). To overcome this obstacle, an emerging ethical approach suggests that fellow researchers will "come to the data set" instead of the data set being sent to them (MacAvaney et al, 2021). In this approach, the sensitive data are kept in a secured environment online in which researchers could conduct analyses but cannot export the raw information itself.…”

Section: Privacy Considerations Versus the Need For High-quality Pred...mentioning

confidence: 99%

“…Indeed, general guidelines for AI applications are published from time to time by research institutions and public organizations ( Jobin et al, 2019); however, it is essential that a consensual "best practice" protocol be developed specifically for resource sharing in suicide research. This future protocol, for example, may support the above described initiative to upload data sets and models to designated servers (MacAvaney et al, 2021). A password-protected access to these servers may be decided to be given only to researchers from recognized academic institutions after they sign a nondisclosure form.…”

Section: Privacy Considerations Versus the Need For High-quality Pred...mentioning

confidence: 99%

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

Ophir

Tikochinski

Klomek

et al. 2021

Clinical Psychological Science

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Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art deep neural network models, in this “travel guide” article, we describe, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk. Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening that theoretical understanding of suicide behaviors and promoting the personalized approach in psychological assessment are presented as well. We also discuss principal ethical and methodological obstacles in CL suicide prevention, such as the difficulty to maintain people’s privacy/safety or interpret the “black box” of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles are provided for future researchers and clinicians.

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Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

Cited by 30 publications

References 31 publications

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

On the Trade-off between Redundancy and Local Coherence in Summarization

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

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