Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens

The rapid growth in the usage and applications of Natural Language Processing (NLP) in various sociotechnical solutions has highlighted the need for a comprehensive understanding of bias and its impact on society. While research on bias in NLP has expanded, several challenges persist that require attention. These include the limited focus on sociodemographic biases beyond race and gender, the narrow scope of analysis predominantly centered on models, and the technocentric implementation approaches. This paper addresses these challenges and advocates for a more interdisciplinary approach to understanding bias in NLP. The work is structured into three facets, each exploring a specific aspect of bias in NLP

Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models

We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the \textit{Bias Identification Test in Sentiment} (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.Comment: TrustNLP at ACL 202

Survey on Sociodemographic Bias in Natural Language Processing

Deep neural networks often learn unintended biases during training, which might have harmful effects when deployed in real-world settings. This paper surveys 209 papers on bias in NLP models, most of which address sociodemographic bias. To better understand the distinction between bias and real-world harm, we turn to ideas from psychology and behavioral economics to propose a definition for sociodemographic bias. We identify three main categories of NLP bias research: types of bias, quantifying bias, and debiasing. We conclude that current approaches on quantifying bias face reliability issues, that many of the bias metrics do not relate to real-world biases, and that current debiasing techniques are superficial and hide bias rather than removing it. Finally, we provide recommendations for future work.Comment: 23 pages, 1 figur

Nationality Bias in Text Generation

Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper examines how a text generation model, GPT-2, accentuates pre-existing societal biases about country-based demonyms. We generate stories using GPT-2 for various nationalities and use sensitivity analysis to explore how the number of internet users and the country's economic status impacts the sentiment of the stories. To reduce the propagation of biases through large language models (LLM), we explore the debiasing method of adversarial triggering. Our results show that GPT-2 demonstrates significant bias against countries with lower internet users, and adversarial triggering effectively reduces the same.Comment: Paper accepted in the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL2023

Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles

We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lead to algorithmic discrimination, posing a significant challenge to the fairness and justice of AI systems. Our study employs a two-step mixed-methods approach that includes both quantitative and qualitative analysis to identify and understand the impact of nationality bias in a text generation model. Through our human-centered quantitative analysis, we measure the extent of nationality bias in articles generated by AI sources. We then conduct open-ended interviews with participants, performing qualitative coding and thematic analysis to understand the implications of these biases on human readers. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting. The qualitative analysis from our interviews offers insights into the experience readers have when encountering such articles, highlighting the potential to shift a reader's perception of a country. These findings emphasize the critical role of public perception in shaping AI's impact on society and the need to correct biases in AI systems

The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets. Our investigation stems from the recognition that SA has become an integral component of diverse sociotechnical systems, exerting influence on both social and technical users. By delving into sociological and technological literature on sentiment, we unveil distinct conceptualizations of this term in domains such as finance, government, and medicine. Our study exposes a lack of explicit definitions and frameworks for characterizing sentiment, resulting in potential challenges and biases. To tackle this issue, we propose an ethics sheet encompassing critical inquiries to guide practitioners in ensuring equitable utilization of SA. Our findings underscore the significance of adopting an interdisciplinary approach to defining sentiment in SA and offer a pragmatic solution for its implementation.Comment: This paper has been accepted and will appear at the EMNLP 2023 Main Conferenc

MiR-543 regulates the epigenetic landscape of myelofibrosis by targeting TET1 and TET2

Myelofibros is (MF) is a myeloproliferative neoplasm characterized by cytopenia and extramedullary hematopoiesis, resulting in splenomegaly. Multiple pathological mechanisms (e.g., circulating cytokines and genetic alterations, such as JAK(V617F) mutation) have been implicated in the etiology of MF, but the molecular mechanism causing resistance to JAK(V617F) inhibitor therapy remains unknown. Among MF patients who were treated with the JAK inhibitor ruxolitinib, we compared noncoding RNA profiles of ruxolitinib therapy responders versus nonresponders and found miR-S43 was significantly upregulated in non responders. We validated these findings by reverse transcription-quantitative PCR. in this same cohort, in 2 additional independent MF patient cohorts from the United States and Romania, and in a JAK2(V617F) mouse model of MF. Both in vitro and in vivo models were used to determine the underlying molecular mechanism of miR-543 in MF. Here, we demonstrate that miR-543 targets the dioxygenases ten-eleven translocation 1 (TET1) and 2 (TET2) in patients and in vitro, causing increased levels of global 5-methylcytosine, while decreasing the acetylation of histone 3, STAT3, and tumor protein p53. Mechanistically, we found that activation of STAT3 by JAKs epigenetically controls miR-543 expression via binding the promoter region of miR-543. Furthermore, miR-543 upregulation promotes the expression of genes related to drug metabolism, including CYP3A4, which is involved in ruxolitinib metabolism. Our findings suggest miR-543 as a potentially novel biomarker for the prognosis of MF patients with a high risk of treatment resistance and as a potentially new target for the development of new treatment options

Session 1E: Role of Pyocyanin, a Secreted Virulence Factor of Pseudomonas aeurignosa, in Respiratory Epithelial Cell Functions

Pseudomonas aeruginosa (PA) is a rod-shaped gram negative bacterium that is associated with lung infection of humans with compromised host-defense such as cystic fibrosis. PA secretes proteins, lipopolysaccharides and virulence factors such as pyocyanin (PCN) that modulate host cell signal transduction and immune responses. PCN mediates its cellular effects via reactive oxygen species (ROS) production in host cells. However, molecular mechanism(s) of PCN-induced ROS production are not well understood. In this study, we will address PCN mediated regulation of ROS via MAPK signaling and activation of NADPH Oxidase (NOX) proteins in bronchial epithelium. Specifically, the role of PCN on ROS generated by mitochondria, NOX2 and NOX4 and endoplasmic stress will be determined using cellular and molecular approaches. In addition, the role of PCN-induced ROS on epithelial barrier integrity and secretion of pro-inflammatory cytokines will be investigated using bronchial epithelial cells in culture. These planned studies will provide new and novel information on PCN-mediated cellular responses in host immune responses in the bronchial epithelium

SELF MEDICATION PRACTICE AMONG URBAN SLUM DWELLERS IN UDUPI TALUK, KARNATAKA, INDIA

Objective: To estimate the prevalence and associated factors of self-medication among slum dwellers, and to explore the perception of community pharmacists' on self-medication practices in Udupi taluk of Karnataka state in India.Methods: A quantitative survey among 300 randomly selected slum dwellers and in-depth interviews with community pharmacists and pharmacy practice experts were conducted during January-April 2016. Descriptive and analytical methods were used to estimate the prevalence and to identify associated factors. Thematic analysis was carried out on qualitative data.Results: The prevalence of self-medication practice was 47%. Factors such as gender, recent experience of an illness, and stocking medicines at home were significantly associated with self-medication practice. Self-medication practices were high for common ailments and for the illnesses they perceived as ‘mild' (66%). Community pharmacists (87%) were the main source of information on medication. The majority (76%) of participants were ignorant about the expiry date of the medicines. The qualitative data highlighted pharmacist's' role to promote consultation with a physician, and educating patients on completion of treatment course and possible drug reactions.Conclusion: Self-medication practices found to be common among slum-dwellers due to reasons such as lesser awareness, easy availability of over-the-counter medications, and limitations related to universal access to health. There is a need to improve the awareness of dangers of self-medication to the general public and strengthen the mechanism to monitor dispensing of medicines without prescriptions