bias and fairness in natural language processing

In a random sample of Tweets from 2013, there were 31 different languages (Plank, 2016), but no treebanks for about two-thirds of them and even fewer semantically annotated resources like WordNets. These biases hold not just for word embeddings but also for the contextual representations of big pre-trained language models that are now widely used in different NLP systems. The top features were “heroin” and “substance abuse” across subgroups. A Survey on Bias and Fairness in Machine Learning. In Technically Wrong, Sara Wachter-Boettcher demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and to demand more from the companies ... These metrics can lead to fairer predictions across subgroups (Chouldechova, 2017; Corbett-Davies & Goel, 2018; Dixon et al., 2018), for example, if the metrics show that the performance for a specific group is much lower than for the rest. Work with researchers, engineers, and project managers to specify safety or other risk-related requirements for Applied AI projects. Mini-summary: With the recent boom in scholarship on Fairness and Bias in Machine Learning, several competing notions of bias and different approaches to mitigate their impact have emerged. For example debiasing embeddings for reducing gender bias in text classification (Prost et al., 2019), dialogue generation (Dinan et al., 2020; Liu et al., 2020), and machine translation (Font & Costa-jussà, 2019). The most salient is undoubtedly to pay more attention to how data is collected and clarify what went into the construction of the data set. If they believe the process is still ongoing, that is, the phrase is analytical, they will choose an ‘adjective plus noun’ construct. Such efforts are more conscious of the effects of debiasing on the target application. Post-hoc mitigation techniques mitigated bias in type II error rates without producing substantial type I error rates. (2020) found that machine translation systems changed the perceived user demographics to make samples sound older and more male in translation. Found inside“These Are Not the Stereotypes You Were Looking For: Bias and Fairness in Authorial Gender Attribution.” In Proceedings of the First Workshop on Ethics in Natural Language Processing, edited by Dirk Hovy, et al., 12–22/ Stroudsberg, ... Natural language processing (NLP) research has seen substantial progress on a variety of applications through the use of large pretrained language models 1,2,3,4.Although these increasingly . Bias can impact machine learning in every step of model development, testing, and implementation, leading to algorithmic bias and feedback loops. Found inside – Page 207... biases from text is a problem that has only recently attracted attention from the Natural Language Processing (NLP) ... dislike of cheating (fairness/cheating), group loyalty (loyalty/betrayal), respect of authority and tradition ... In their recent EMNLP-2020 article, Vargas & Cotterell show that within word embedding space, gender bias occupies a linear subspace. Whether I say ‘I am totally pumped’ or ‘I am very excited’ conveys information about me far beyond the actual meaning of the sentence. K. Lerman and A. Galstyan, "A Survey on Bias and Fairness in Machine Learning . Despite these methods being successful in various applications, they run the risk of exploiting and reinforcing the societal biases (e.g. One of the sources of bias overamplification is the choice of loss objective used in training the models. Social and personal perspectives shape the entire production cycle of news, the trends in communication on social media, and even the types of discourse commonly perceived as . Found inside – Page 240Fairness, Accountability, and Transparency. A conference about bias in machine learning, natural language processing, AI, and other computing processes. FTAA. Free Trade Area of the Americas. FTAA IMC. FTAA Independent Media Center. Employ ai Fairness 360 open source libraries to detect bias in models 4. . For example, Emily Bender has suggested making overexposure bias more apparent by stating explicitly which language we work on ‘even if it is English’ (Bender, 2019). Language Processing Technology vFairnessinNLP(tutorialatEMNLP19) vRobustRepresentations(tutorialatAAAI20) vRobustnessinNLP . ACL 2020 Tutorial: Integrating Ethics into the NLP Curriculum by Emily M. Bender, Dirk Hovy and Xanda Schofield. Researchers found that the Black FNR subgroup had the highest severity of disease and risk for poor outcomes. And language carries a lot of secondary information about the speaker, their self-identification, and membership in socio-demographic groups (Flek, 2020; Hovy & Spruit, 2016; Hovy & Yang, 2021). The consequences of these shortfalls range from an inconvenience to something much more insidious. Instead, they work on languages and tasks for which data is readily available, potentially generating more data in the process. Given that a large part of the world's population is currently under 30, such models will degrade even more over time and ultimately not meet their users' needs. This underexposure is a self-fulfilling prophecy: researchers are less likely to work on those languages for which there are not many resources. However, many syntactic analysis tools (taggers and parsers) are still trained on the newswire data from the 1980s and 1990s. In many cases, though, the effect is much less easy to notice: the performance degrades, producing sub-par output for some users. The meaning and measurement of bias: lessons from natural language processing. In real life, many of our reactions to everyday situations are biases that make our lives easier. Read our report here. The latter could be due to the test data point lying outside the training data distribution or the model's representation space. (2020) as well as the two workshops by the Association for Computational Linguistics on Ethics in NLP (Alfano et al., 2018; Hovy et al., 2017). For example, suppose we ask crowd workers to annotate concepts like dogmatism, hate speech, or microaggressions. More generally, methods designed to probe and analyse the model can help us understand how it reached decisions. Any examination of bias in AI needs to recognize the fact that these biases mainly stem from . This approach is equivalent to expecting everyone to speak like the octogenarian from above: it leads to problems when encountering the teenager. Hey! This is reflected in the way people write and speak about events and entities in the world as well as in how they live through their personal experiences. Please check your email for instructions on resetting your password. The first step in the process was to formulate a research question to define the aim of the study and map answers from the literature. This degradation is much harder to see but often systematic for a particular demographic group and creates a demographic bias in NLP applications. [20 min] Introduce & Motivation v[40 min] Societal Bias in Language Representations v[10 min] Bias Detection v[10 min] Break v[30min]Bias Amplification . Hence, marginalized communities or speaker groups do not have their voice represented proportionally. Recent work by Bird (2020) suggests new ways of collaborating with Indigenous communities in the form of open discussions and proposes a postcolonial approach to computational methods for supporting language vitality. Maybe you have dipped your toe in the waters of natural language processing by auditing Stanford's From Languages to Information course. Comput. A way forward is to use various evaluation settings and metrics (Ribeiro et al., 2020). Valencia, Spain: Association for Computational Linguistics. This work proposes to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference to reduce the magnitude of bias amplification in multilabel object classification and visual semantic role labeling. For example, the language model GPT-3, of OpenAI fame, can generate racist rants when given the right prompt. Computers look beyond individual words or phrases and understand the . (2021) discuss this briefly in a case study for machine translation systems. Moreover, evaluation of bias has been inconsistent in previous work, in terms of dataset balance and evaluation methods. For example, by merely changing the gender of a pronoun, the systems classified the sentence differently. Found inside – Page 3825th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Saarbrücken, Germany, June 24-26, ... informativeness and fairness & ethics) and property (transparency and post-hoc interpretability). This incisive meta-review from Blodgett et al dissects 146 papers on Bias in Natural Language Processing (NLP) and identifies critical discrepancies in motivation, normative reasoning and suggested approaches. Bias can significantly impact population health and create disparities in disadvantaged groups. Pages 706. . Website: https://www.cs.cmu.edu/∼sprabhum/. Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. Found inside – Page 139For example, at Microsoft, our researchers are working on techniques to identify bias in word embeddings and correct it, enabling natural language processing scenarios built on top of these models to be fair. Gender bias can be removed ... Certain works focus on bias and unfairness identification and mitigation methods for specific applications such as text analysis—e.g. Kai-Wei Chang, Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning.She is most well-known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use. But the question is: whose standard (Eisenstein, 2013)? Found inside – Page 269Improving fairness in machine learning systems: what do industry practitioners need? In: ACM CHI Conference on Human ... Mitigating gender bias in natural language processing: a literature review. In: 57th Proceeding of Association for ... Specifically, these constraints ensure that the proportion of predicted labels should be the same or approximately the same for each user group. One of the reasons for the linguistic and cultural skew in research is the makeup of research groups themselves. In general, we can use measures to address overfitting or imbalanced data to correct for demographic bias in data. However, a recent paper (Joshi et al., 2020) found that most conferences still focus on the well-resourced languages and are less inclusive of less-resourced ones. Another issue with the design of machine learning models is that they always make a prediction, even when they are unsure or when they cannot know the answer. A Survey on Bias and Fairness . For example, these models are not directly applicable to data sets that contain scientific articles or medical terminologies. This dual nature of intended use and unintended consequences is common to all new technologies. Natural language processing techniques play important roles in our daily life. Because humans may be biased, so may be Machine Learning (ML) models trained on data that reflects human biases. Mexican mathematician interested in Natural Language Processing and bias and fairness in AI. Systems that explicitly model user demographics will help produce both more personalized and less biased translations (Font & Costa-jussà, 2019; Mirkin et al., 2015; Mirkin & Meunier, 2015; Saunders & Byrne, 2020; Stanovsky et al., 2019). With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. and you may need to create a new Wiley Online Library account. Bias was then examined through testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95 percent confidence intervals. You may have learned from one of these many other freely-available top-notch natural language processing .

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