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1. Detecting and understanding moral biases in news

   https://doi.org/10.18653/v1/2020.nuse-1.15  

   Shahid, Usman; Di Eugenio, Barbara; Rojecki, Andrew; Zheleva, Elena (January 2020, Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events)

   null (Ed.)

   We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area. 

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2. Cognitive biases in software development

   https://doi.org/10.1145/3517217  

   Chattopadhyay, Souti; Nelson, Nicholas; Au, Audrey; Morales, Natalia; Sanchez, Christopher; Pandita, Rahul; Sarma, Anita (April 2022, Communications of the ACM)

   Cognitive biases are hardwired behaviors that influence developer actions and can set them on an incorrect course of action, necessitating backtracking. Although researchers have found that cognitive biases occur in development tasks in controlled lab studies, we still do not know how these biases affect developers' everyday behavior. Without such an understanding, development tools and practices remain inadequate. To close this gap, we conducted a two-part field study to examine the extent to which cognitive biases occur, the consequences of these biases on developer behavior, and the practices and tools that developers use to deal with these biases. We found about 70% of observed actions were associated with at least one cognitive bias. Even though developers recognized that biases frequently occur, they are forced to deal with such issues with ad hoc processes and suboptimal tool support. As one participant (IP12) lamented: There is no salvation! 

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3. Benchmarking Intersectional Biases in NLP

   https://doi.org/10.18653/v1/2022.naacl-main.263  

   Lalor, John; Yang, Yi; Smith, Kendall; Forsgren, Nicole; Abbasi, Ahmed (July 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)
4. Understanding and Evaluating Racial Biases in Image Captioning

   Zhao, Dora; Wang, Angelina; Russakovsky, Olga (October 2021, Proceedings)
5. On Measures of Biases and Harms in NLP

   Sunipa Dev; Emily Sheng; Jieyu Zhao; Aubrie Amstutz; Jiao Sun; Yu Hou; Mattie Sanseverino; Jiin Kim; Akihiro Nishi; Nanyun Peng; et al (November 2022, Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022)

   Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. To create interventions and mitigate these biases and associated harms, it is vital to be able to detect and measure such biases. While existing works propose bias evaluation and mitigation methods for various tasks, there remains a need to cohesively understand the biases and the specific harms they measure, and how different measures compare with each other. To address this gap, this work presents a practical framework of harms and a series of questions that practitioners can answer to guide the development of bias measures. As a validation of our framework and documentation questions, we also present several case studies of how existing bias measures in NLP—both intrinsic measures of bias in representations and extrinsic measures of bias of downstream applications—can be aligned with different harms and how our proposed documentation questions facilitates more holistic understanding of what bias measures are measuring. 

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