It is well known that AI-based language technology—large language models, machine translation systems, multilingual dictionaries, and corpora—is currently limited to three percent of the world’s most widely spoken, financially and politically backed languages. In response, recent efforts have sought to address the “digital language divide” by extending the reach of large language models to “underserved languages.” We show how some of these efforts tend to produce flawed solutions that adhere to a hard-wired representational preference for certain (...) languages, which we call language modeling bias. Language modeling bias is a specific and under-studied form of linguistic bias were language technology by design favors certain languages, dialects, or sociolects with respect to others. We show that language modeling bias can result in systems that, while being precise regarding languages and cultures of dominant powers, are limited in the expression of socio-culturally relevant notions of other communities. We further argue that at the root of this problem lies a systematic tendency of technology developer communities to apply a simplistic understanding of diversity which does not do justice to the more profound differences that languages, and ultimately the communities that speak them, embody. Drawing on the concept of epistemic injustice, we point to the broader ethico-political implications and show how it can lead not only to a disregard for valuable aspects of diversity but also to an under-representation of the needs of marginalized language communities. Finally, we present an alternative socio-technical approach that is designed to tackle some of the analyzed problems. (shrink)

Orienting biases for speech may provide a foundation for language development. Although human infants show a bias for listening to speech from birth, the relation of a speech bias to later language development has not been established. Here, we examine whether infants' attention to speech directly predicts expressive vocabulary. Infants listened to speech or non-speech in a preferential listening procedure. Results show that infants' attention to speech at 12 months significantly predicted expressive vocabulary at 18 months, (...) while indices of general development did not. No predictive relationships were found for infants' attention to non-speech, or overall attention to sounds, suggesting that the relationship between speech and expressive vocabulary was not a function of infants' general attentiveness. Potentially ancient evolutionary perceptual capacities such as biases for conspecific vocalizations may provide a foundation for proficiency in formal systems such language, much like the approximate number sense may provide a foundation for formal mathematics. (shrink)

There has been a longstanding bias in the study of spoken language towards using writing to analyse speech. This approach is problematic in that it assumes language to be derived from an autonomous mental capacity to assemble words into sentences, while failing to acknowledge culture-specific ideas linked to writing. Words and sentences are writing constructs that hardly capture the sound-making actions involved in spoken language. This book brings to light research that has long revealed structures present (...) in all languages but which do not match the writing-induced concepts of traditional linguistic analysis. It demonstrates that language processes are not physiologically autonomous, and that speech structures are structures of spoken language. It then illustrates how speech acts can be studied using instrumental records, and how multisensory experiences in semantic memory couple to these acts, offering a biologically-grounded understanding of how spoken language conveys meaning and why it develops only in humans. (shrink)

This paper defends strict invariantism against some philosophical and empirical data that have been taken to compromise it. The defence involves a combination of a priori philosophical arguments and empirically informed theorizing. The positive account of the data is an epistemic focal bias account that draws on cognitive psychology. It involves the assumption that, owing to limitations of the involved cognitive resources, intuitive judgments about knowledge ascriptions are generated by processing only a limited part of the available information—the part (...) that is in focus. According to the epistemic focal bias account, the intuitive judgments about knowledge ascriptions that constitute contrast effects amount to false positives, whereas the intuitive judgments that constitute salient alternatives effects amount to false negatives. I conclude by considering how the basic epistemic focal bias account may be developed further by reference to relevant alternatives theory in epistemology, pragmatics in the philosophy of language, and dual process theory in cognitive psychology. (shrink)

B a c k g r o u n d. The article is devoted to the study of confirmatory distortion as a cognitive bias within the framework of the modern theory of argumentation. In the context of this study, the effectiveness of the critical questioning technique as an argumentation strategy aimed at reducing the negative impact of confirmatory bias is considered. M e t h o d s. To achieve the goals of the research, the method of critical (...) questions is used, which is based on the traditional principles of logic, dialectics and rhetoric. Specific lists of critical questions are developed for each argumentative scheme. R e s u l t s. The article demonstrates that standard critical questions for schemes of practical reasoning, developed by eminent specialists, are able to minimize the negative impact of confirmation bias. This is achieved by taking into account information that may conflict with the agent's action plan. C o n c l u s i o n s. The study highlights the importance of using critical questions as a tool to minimize confirmation bias. Such an approach not only contributes to the substantiation of arguments, but also stimulates self-control and the search for information that may cause doubts about one's own views. The final part of the article examines propaganda as the deliberate use of confirmatory distortion and points to the need for attention to language patterns where it occurs rather unintentionally. In connection with the specificity of the studied phenomenon, the term "confirmation bias" is proposed as a translation of the English "confirmation bias". (shrink)

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is (...) described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance. (shrink)

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves — rather than with their mathematical representations (as in symbolic (...) models). This paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Mäs & Flache 2013; Singer et al. 2019): First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from symbolic models. However, once ADAs start to actively generate new contributions, the evolution of a conversation is dominated by properties of the agents as authors. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research. (shrink)

Across languages of the world, some grammatical patterns have been argued to be more common than expected by chance. These are sometimes referred to as language universals. One such universal is the correlation between constituent order freedom and the presence of a case system in a language. Here, we explore whether this correlation can be explained by a bias to balance production effort and informativity of cues to grammatical function. Two groups of learners were presented with miniature (...) artificial languages containing optional case marking and either flexible or fixed constituent order. Learners of the flexible order language used case marking significantly more often. This result parallels the typological correlation between constituent order flexibility and the presence of case marking in a language and provides a possible explanation for the historical development of Old English to Modern English, from flexible constituent order with case marking to relatively fixed order without case marking. In addition, learners of the flexible order language conditioned case marking on constituent order, using more case marking with the cross-linguistically less frequent order, again mirroring typological data. These results suggest that some cross-linguistic generalizations originate in functionally motivated biases operating during language learning. (shrink)

Patriarchy's timeline showing how the Lords of patriarchy developed language in its own self-interest. Exposes the need patriarchy had to make and use language as a political tool to achieve its political ends, control, power and domination.

Our paper contributes to the rapidly expanding body of experimental research on transformative decision making, and in the process, marks out a novel empirical interpretation for assessments of subjective value in transformative contexts. We start with a discussion of the role of subjective value in transformative decisions, and then critique extant experimental work that explores this role, with special attention to Reuter and Messerli (2018). We argue that current empirical treatments miss a crucial feature of practical deliberation manifesting across a (...) variety of everyday decisions: often, people attach more weight to decision criteria that they can know—a phenomenon known as the “evaluability bias.” In transformative contexts, if people cannot know the subjective value of an option, they are unlikely to attach it much weight. Despite this, people may care very much about such value. We then use this point to develop and present new empirical results that, in addition to supporting our concerns about evaluability bias, support the hypothesis that people care about subjective value. Our work enriches the current philosophical understanding of transformative decisions and helps to frame the emerging experimental paradigm for the empirical dimensions of the debate. (shrink)

I show that speaker bias in _polarity focus questions_ (PFQs) is context sensitive, while speaker bias in _high negation questions_ (HNQs) is context insensitive. This leads me to develop separate accounts of speaker bias in each of these kinds of polar questions. I argue that PFQ bias derives from the fact that they are frequently used in conversational contexts in which an answer to the question has already been asserted by an interlocutor, thus expressing doubt about (...) the prior assertion. This derivation explains their context sensitivity, and the fact that similar bias arises from polar questions that lack polarity focus. I also provide novel evidence that the prejacents of HNQs lack negation, and thus only have an outer negation reading (see, e.g., Ladd in Papers from the seventeenth regional meeting of the Chicago Linguistic Society, vol. 17, pp. 164–171, 1981 ; Romero and Han in Linguistics and Philosophy 27(5):609–658, 2004 ; Krifka in Contrastiveness in information structure, alternatives and scalar implicatures, pp. 359–398, 2017 ; AnderBois in Questions in discourse, pp. 118–171, 2019 ; Frana and Rawlins in Semantics and Pragmatics 12(16):1–48, 2019 ; Jeong in Journal of Semantics 38(1):49–94, 2020 ). Based on a treatment of HNQs as denoting unbalanced partitions (Romero and Han in Linguistics and Philosophy 27(5):609–658, 2004 ), and competition with their positive polar question alternatives, I propose a novel derivation of speaker bias in HNQs as a conversational implicature. Roughly, if the speaker is ignorant, then a positive polar question will be more useful because it is more informative, so the use of an HNQ conveys that the speaker is not ignorant. The denotation of the HNQ then makes clear which way the speaker is biased. The result separates high negation from verum focus, and I argue that it is more parsimonious and has better empirical coverage than prior accounts. (shrink)

Describes my book from the first bias creating belief in male superiority, to developing language with a massive number of names embedding male bias to guarantee patriarchy's survival, creating belief supplanting knowledge. Suggests changing language with evidence-based, neutral, inclusive and specific names reducing, and perhaps, eliminating male dominance.

Drawing on empirical findings, a number of philosophers have recently argued that people who use English as a foreign language may face a linguistic bias in academia in that they or their contributions may be perceived more negatively than warranted because of their English. I take a critical look at this argument. I first distinguish different phenomena that may be conceptualized as linguistic bias but that should be kept separate to avoid overgeneralizations. I then examine a range (...) of empirical studies that philosophers have cited to argue that people who use English as a foreign language are subject to linguistic bias in academia. I contend that many of these studies do not sufficiently support key claims that philosophers have made about linguistic bias, are challenged by counterevidence, and lack generalizability. I end by introducing methodological recommendations that may help philosophers develop more convincing empirically informed arguments regarding linguistic bias. (shrink)

In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...) qualified applicants from minority groups from applying. The existing works are limited to analyzing, categorizing and highlighting the occurrence of bias in the recruitment process. In this paper, we go beyond this and develop machine learning models for identifying and classifying biased and discriminatory language in job descriptions. We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements, i.e., masculine-coded, feminine-coded, exclusive, LGBTQ-coded, demographic and racial language. We utilized the combination of linguistic features with recent state-of-the-art word embeddings representations as input features for various machine learning classifiers. Our results show that the machine learning classifiers were able to identify all the five categories of biased and discriminatory language with a decent accuracy. The Random Forest classifier with FastText word embeddings achieved the best performance with tenfolds cross-validation. Our system directly addresses the bias in the attraction phase of hiring by identifying and classifying biased and discriminatory language and thus encouraging recruiters to write more inclusive job advertisements. (shrink)

On the basis of internet forum and press media data, this article studies the expression of hostile attitudes towards multilingualism and multiculturalism in the context of debates about immigration. The forum data are drawn from the BBC’s Have Your Say website, which is a moderated forum that excludes polemical and abusive postings. Nevertheless, it still seems to provide its users ample opportunity for airing strongly anti-immigrant attitudes. The narratives in which these attitudes are being expressed are exemplary stories of the (...) posters’ supposed encounters with the use of foreign languages in the street, in the workplace or at school. This presence of foreign languages in the British public sphere is evaluated as being problematic and is “explained” as a result of mass immigration, which serves to reinforce the scenario of a culture mix that will destroy British “home” culture. Media coverage of immigration partly supports such vilification of multilingualism and multiculturalism, and the reports and comments often seem to be drawn from similar narrative-argumentative templates as those of the discussions on Have Your Say. In conclusion, we argue that counterspeech informed by Critical Discourse Analysis has to develop alternative narratives and figurative scenarios that question the bias against linguistic and cultural diversity. (shrink)

The objective of this article was to review existing research to assess the evidence for predictive processing in sign language, the conditions under which it occurs, and the effects of language mastery on the neural bases of PP. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. We searched peer-reviewed electronic databases and gray literature. We also searched the reference lists of records selected for the review and forward citations to identify all relevant publications. (...) We searched for records based on five criteria. To reduce the risk of bias, the remaining two authors with expertise in sign language processing and a variety of research methods reviewed the results. Disagreements were resolved through extensive discussion. In the final review, 7 records were included, of which 5 were published articles and 2 were dissertations. The reviewed records provide evidence for PP in signing populations, although the underlying mechanism in the visual modality is not clear. The reviewed studies addressed the motor simulation proposals, neural basis of PP, as well as the development of PP. All studies used dynamic sign stimuli. Most of the studies focused on semantic prediction. The question of the mechanism for the interaction between one’s sign language competence and PP in the manual-visual modality remains unclear, primarily due to the scarcity of participants with varying degrees of language dominance. There is a paucity of evidence for PP in sign languages, especially for frequency-based, phonetic, and syntactic prediction. However, studies published to date indicate that Deaf native/native-like L1 signers predict linguistic information during sign language processing, suggesting that PP is an amodal property of language processing.Systematic Review Registration[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238911], identifier [CRD42021238911]. (shrink)

This paper presents a novel methodology for integrating indigenous knowledge systems into AI development to counter cognitive imperialism and foster inclusivity. By critiquing the dominance of Western epistemologies and highlighting the risks of bias, the authors argue for incorporating diverse epistemologies. The proposed framework outlines a participatory approach that includes indigenous perspectives, ensuring AI benefits all. The methodology draws from AI ethics, indigenous studies, and postcolonial theory, emphasizing co-creation with indigenous communities, ethical protocols for indigenous data governance, and adaptation (...) of AI algorithms. Case studies in natural language processing, content moderation, and healthcare demonstrate the methodology’s effectiveness and importance. By offering a concrete methodology for decolonizing AI, this paper contributes significantly to AI ethics and social justice, providing a roadmap for equitable, culturally respectful AI. (shrink)

In this paper I discuss and develop the risk account of scientific objectivity, which I have recently introduced, contrasting it to some alternatives. I then use the account in order to analyse a practice that is relatively common in anthropology, in the history of science, and in the sociology of scientific knowledge: withholding epistemic judgement. I argue that withholding epistemic judgement on the beliefs one is studying can be a relatively efficient strategy against collective bias in these fields. However, (...) taking into account the criticisms presented against the strategy, I also argue that it is a usable strategy only when the distance between the researchers and their ideas, and the people and ideas being studied, is significant enough. (shrink)

German nouns may alternate from singular to plural in two different ways. Some singular forms that end in a voiceless obstruent have a plural in which this obstruent is voiced. Another alternation concerns the vowel. Some singular forms with a back vowel have a plural form in which this back vowel is front. For each noun it has to be established individually whether it alternates or not. The voicing alternation is phonetically grounded, but the vowel alternation is not. Knowledge about (...) such alternations involves two things. First, it involves knowledge of which words alternate and which words do not and second, it involves the ability to extend the alternations to novel words. We studied the knowledge of which words alternate and the proportion to which they alternate in two corpus studies. We studied the knowledge of speakers concerning which words alternate and what generalizations can be based upon these words by means of a production study. The production study involved words and nonces. We asked twenty 5 year-olds, twenty 7 year-olds and tenth adults to produce the plural for a given singular word and a plural for a given singular nonce. In the corpus study we found that both alternations occur with the same frequency. In the production of alternations in words we found that participants in all age groups make few mistakes. With respect to the production of alternations in nonce words, we found that the proportion of voicing alternations decreases with age, while the proportion of vowel alternations increases. We explain this change in the ability to generalize the alternations to nonces on the basis of the confidence speakers can have in a generalization. Young children have a small lexicon and they can form relatively unreliable generalizations on lexical distributions, but they can have more confidence in their knowledge of phonetics. Adults have a large lexicon and can therefore confidently form based on their lexicon. (shrink)

Evidence suggests a limited contribution to the total research output in leading obstetrics and gynaecology journals by researchers from the developing world. Editorial bias, quality of scientific research produced and language barriers have been attributed as possible causes for this phenomenon. The aim of this study was to understand the prevalence of editorial board members based out of low and lower-middle income countries in leading journals in the field of obstetrics and gynaecology. The top 21 journals in the (...) field of obstetrics and gynaecology were selected based on their impact factor, SCImago ranking and literature search. The composition of the editorial boards of these journals was studied based on World Bank Income Criteria to understand the representation status of researchers from low and lower-middle income countries. A total of 1315 board members make up the editorial composition of leading obstetrics and gynaecology journals. The majority of these editors belong to high-income countries (n = 1148; 87.3%). Low (n = 6; 0.45%) and lower-middle income (n = 55; 4.18%) countries make up for a very minuscule proportion of editorial board members. Only a meagre 9 out of 21 journals have editorial board members from these countries (42.85%). Low and low-middle countries have poor representation in the editorial boards of leading obstetrics and gynaecology journals. Poor representation in research from these countries has grave consequences for a large proportion of the global population and multidisciplinary collaborative efforts must be taken to rapidly change this statistic with immediate effect. (shrink)

This study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German-LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and nonword-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed according to the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of (...) language impairment across language combinations. Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German-LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accurracy. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM), for deriving a clinical category for the children as ±language-impaired based on performance scores on SRT and/or NWRT while withholding information about their clinical status based on standardized assessment in L1/L2. Subsequently, we calculated diagnostic accuracy and investigated which background variables (age of onset, length of exposure, degree of language-dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on NWRT and/or SRT scores. Results show that although language-dominance influences the performance of bilingual typically-developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically-developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive for risk factors for SLI and not to dominance levels or SES. (shrink)

In artificial intelligence literature, “delusions” are characterized as the generation of unfaithful output from reliable source content. There is an extensive literature on computer-generated delusions, ranging from visual hallucinations, like the production of nonsensical images in Computer Vision, to nonsensical text generated by (natural) language models, but this literature is predominantly taxonomic. In a recent research paper, however, a group of scientists from DeepMind successfully presented a formal treatment of an entire class of delusions in generative AI models (i.e., (...) models based on a transformer architecture, both with and without RLHF—reinforcement learning with human feedback, such as BERT, GPT-3 or the more recent GPT-3.5), referred to as auto-suggestive delusions. Auto-suggestive delusions are not mere unfaithful output, but are self-induced by the transformer models themselves. Typically, these delusions have been subsumed under the concept of exposure bias, but exposure bias alone does not elucidate their nature. In order to address their nature, I will introduce a formal framework that clarifies the probabilistic delusions capable of explaining exposure bias in a broad manner. This will serve as the foundation for exploring auto-suggestive delusions in language models. Next, an examination of self- or auto-suggestive delusions will be undertaken, by drawing an analogy with the rule-following problematic from the philosophy of mind and language. Finally, I will argue that this comprehensive approach leads to the suggestion that transformers, large language models in particular, may develop in a manner that touches upon solipsism and the emergence of a private language, in a weak sense. (shrink)

Previous studies have shown a robust bias to express the goal path over the source path when describing events. Motivated by linguistic theory, this study manipulated the causal structure of events and measured the extent to which adults and 3.5- to 4-year-old English-speaking children included the goal and source in their descriptions. We found that both children's and adults’ encoding of the source increased for events in which the source caused the motion of the figure compared to nearly identical (...) events in which the source played no such causal role. However, a goal bias persisted overall for both causal and noncausal motion events. These findings suggest that although the goal bias in language is highly robust, properties of the source influence its likelihood of being encoded in language, thus shedding light on how properties of an event can influence the mapping of event components into language. (shrink)

In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English (...) as the average length of their utterances increases. These results suggest that it is possible to explain the key features of the OI phenomenon in both Dutch and English in terms of the interaction between an utterance-final bias in learning and the distributional characteristics of child-directed speech in the 2 languages. They also show how computational modeling techniques can be used to investigate the extent to which cross-linguistic similarities in the developmental data can be explained in terms of common processing constraints as opposed to innate knowledge of universal grammar. (shrink)

The rapid rise of generative AI, including LLMs, has prompted a great deal of concern, both within and beyond academia. One of these concerns is that generative models embed, reproduce, and therein potentially perpetuate all manner of bias. The present study offers an alternative perspective: exploring the potential of LLMs to detect bias in human generated text. Our target is genetic essentialism in obesity discourse in Australian print media. We develop and deploy an LLM-based classification model to evaluate (...) a large sample of relevant articles (n=26,163). We show that our model detects genetic essentialist biases as reliably as human experts; and find that, while genes figure less prominently in popular discussions of obesity than previous work might suggest, when genetic information is invoked, it is often presented in a biased way. Implications for future work are discussed. (shrink)

This paper ties some less well-known names to the bigger categories of embedded male-bias in names (see other papers) that patriarchy made in its development of language, making the masses believe in phallic-based fantasy, the male as superior and mind of the species.

Looks at the development of the sacred after the Sumerians' named the phallus Supreme Creator in 9000 B.C.E. Lists names invented creating belief in Sacred Phallus and names the part male genitals played in supporting the phallus as sacred.

During the 11,000 years patriarchy ruled it embedded male-bias in over 20,000 names thus making language suit their ideology of eternal males rule. Every male-biased name stops our species from seeing reality-as-it-is. Every bias hides a truth creating ignorance of what actually exists to be seen and known.

How do young children learn how to talk about ethical concepts, such as fairness and equity? The current research assesses the effects of philosophy-based anti-bias lessons aimed at three to five-year-olds in a laboratory preschool and explores the relationship between socioemotional skills, language development, and understanding of ethical concepts. The constructivist curriculum was grounded in storybook lessons with corresponding activities. In a pre-posttest design, we interviewed children before and after lessons in fairness education that included book reading, related (...) games, and philosophical discussions. We found that children showed a significant increase in comprehension of equity-related terms and skin color awareness, and we found a positive relationship between children’s observed levels of community membership skills and their receptive vocabulary. In addition, receptive vocabulary skills predicted participant’s skin color awareness and their ability to tell a fairness narrative involving skin color following the lessons. The current study provides a framework for teachers to integrate ethical reasoning into a preschool curriculum and identifies some abilities occurring earlier than predicted by Jean Piaget. Given the divisive political climate surrounding anti-bias education, we hope that the present study encourages educators to put social emotional learning at the forefront of preschool pedagogy. (shrink)

Pregnant people experience moral judgment in healthcare settings that may be coded into clinical documentation. Stigmatizing language in medical records transmits bias between clinicians, potentially exacerbating disparities in maternal morbidity and mortality. We examined obstetrical records from 100 randomly selected patients who received prenatal and delivery care in an academic hospital system. Qualitative analysis sought to identify linguistic features conveying negative attitudes or moral judgment, revealing themes of epistemic injustice: (1) discrediting patient testimony as incompetent, unreliable, and hysterical; (...) (2) unnecessary details that are objectifying, stigmatizing, or unprofessional; and (3) judgments of maternal fitness, where women are labeled “bad mothers” by emphasizing neglectful, selfish, and debauched characteristics. We conclude by advocating for further validation of our findings, revisiting medical education paradigms, and supporting the development of natural language processing (NLP) technologies to detect and intercept stigma. (shrink)

Recently, I had a conversation with a colleague which brought back memories of similar conversations in the past. This individual was required to teach a course in informal logic for the first time. His background was in formal logic and he did not understand the nature of this course, the curriculum, nor the skills to be taught. This lack caused him to disparage the course as not worthy of his attention, not consisting of any recognized area of expertise and of (...) little value to students. This situation would not be a problem were it atypical. I believe this bias against an understanding of argumentation is, in part, due to a lack of awareness of the theories, issues, and approaches that inform the dynamics of critical thinking. Like most other areas in philosophy, there is controversy; there are different approaches and arguments. Those who have an understanding of ongoing research in this area rarely disparage the field of informal logic, or fail to acknowledge the developing area of expertise and its history. (shrink)

Our objectives in this article are to bring some theoretical order into developmental sequences and simultaneities in children’s ability to appreciate multiple labels for single objects, to reason with identity statements, to reason hypothetically, counterfactually, and with beliefs and desires, and to explain why an ‘implicit’ understanding of belief occurs before an ‘explicit’ understanding. The central idea behind our explanation is the emerging grasp of how objects of thought and desire relate to real objects and to each other. To capture (...) this idea we make use of the notion of discourse referents, as did Perner and Brandl (2005), to explain the developmental link between understanding beliefs and alternative naming. We present confirming evidence of the prediction from this analysis that children should have comparable problems with understanding identity statements. We explain the precociously correct answers in ‘implicit’ false belief tests based on indirect measures in the following way: From infancy children are able to keep track of other people’s experiences, to reason about counterfactual circumstances, and to reason about goal-directed (rational) action depending on given circumstances. Indirect tasks reduce the bias to use actual circumstances for reasoning about goal directed action compared to the traditional task, which leads to more correct answers. An emerging metarepresentational understanding helps overcome these biases and enables not only correct action prediction but also the explanation of erroneous actions. The common metarepresentational element explains why false belief tasks and the alternative naming task are mastered at the same time as children understand identity statements. (shrink)

Humans alone acquire language. According to one influen- tial school of thought, we do this because we possess a uniquely human ability to act with and attribute “Gricean” communicative intentions. A challenge for this view is that attributing communicative intent seems to require cognitive abilities that infant language learners lack. After considering a range of responses to this challenge, I argue that infant language development can be explained, because Gricean communication is cognitively less demanding than many suppose. (...) However, a consequence of this is that abilities for Gricean communication are unlikely to be uniquely human. (shrink)

I develop one partial explanation of the origins of our fallibilist intuitions about knowledge in ordinary language fallibilism and argue that this explanation indicates that our epistemic methodology should be more impartial and theory-neutral. First, I explain why the so-called Moorean constraint (cf. Hawthorne 2005, 111) that encapsulates fallibilist intuitions is fallibilism’s cornerstone. Second, I describe a pattern of fallibilist reasoning in light of the influential dual processing and heuristics and biases approach to cognition (cf. Kahneman 2011; Thaler and (...) Sunstein 2008; Evans 2017). I suggest that this pattern of reasoning involves the question-substitution heuristic, the availability and representativeness heuristics, the focusing bias as well as framing effects, priming and the anchoring and adjusting heuristic. Third, I argue that this fallibilist pattern of reasoning is methodologically dubious because it involves a vicious circularity and briefly outline an alternative, more impartial and theory-neutral abductive methodology for the theory of knowledge. Finally, I briefly explain how this analysis sheds light on the ordinary language fallibilism of Moore (1939), Austin (1961), Wittgenstein (1969) and Chisholm (1982). (shrink)

Given the current context of the experience of migration on schools in England and Europe, and the competing policies and approaches to social integration in schools, there is a need to understand the connection between language development and social integration as a basis for promoting appropriate policies and practices. This volume explores the complex relationship between language, education and the social integration of newcomer migrant children in England, through an in-depth analysis of case studies from schools in the (...) East of England. The authors set this evidence against the background of policy debates in the wider international setting, including a critical discussion of assumptions underlying national narratives of mainstreaming and assimilation. In the light of an absence of national guidelines for appropriate practice in schools, the authors outline a model of inclusive pedagogy for English as an additional language and a framework of home-school communication to promote effective EAL parental engagement in schools. (shrink)

Language development is one of the major battle grounds within the humanities and sciences. This book presents, for the first time, an impartial account of the three dominant theories of language development. Written to be accessible for those within developmental psychology, philosophy, and linguistics, the book provides the reader with the information they need in order make up their own mind about this much debated issue.

LANGUAGE NOTE | Document text in Chinese; abstract also in English. 近些年，全球抑鬱症患者顯著增加，傳統線下心理治療面臨可及性、便利性和社會污名化等挑戰，已成為全球公共術生領域的關注焦點。在此背景下，人工智慧(AI)聊天機器人的快速發展為其提供了新的解決方案，但AI聊 天機器人仍面臨患者隱私信息保護、算法偏見、人機過度依賴及責任歸屬等倫理問題。因此，本文借用道家哲學思想對AI醫學倫理進行反思，並引用心理健康、人工智慧和機器學習等多領域知識。通過探討AI聊天機器人治療 抑鬱症可能出現的人機過度依賴和責任歸屬的倫理問題，以期AI技術發展的同時，亦能維護人的自由與本真狀態，實現技術創新與人文闋懷的和諧統一。 The current rapid increase in the number of people suffering from depression globally has presented traditional offline psychotherapies with various challenges, such as regarding accessibility, convenience of treatment, and social stigma, which have become focal areas in the global public health domain. In this context, the rapid development of artificial intelligence (AI) chatbots has provided new solutions. However, AI chatbots still face ethical issues regarding patient privacy information (...) protection, algorithmic bias, human–machine overdependence, and responsibility attribution. Therefore, this paper draws on the philosophical ideas of Daoism to reflect on the ethics of AI medicine and references the knowledge of multiple fields such as mental health, AI, and machine learning. We explore the ethical issues of human–machine overdependence and responsibility attribution that may arise in the use of AI chatbots to treat depression. The goal should be to ensure that human freedom and authenticity are protected alongside the development of AI technology, thus achieving harmony between technological innovation and humanistic care. (shrink)

BackgroundKnowledge development depends on an unbiased representation of the available evidence. Selective citation may distort this representation. Recently, some controversy emerged regarding the possible impact of swimming on childhood asthma, raising the question about the role of selective citation in this field. Our objective was to assess the occurrence and determinants of selective citation in scientific publications on the relationship between swimming in chlorinated pools and childhood asthma.MethodsWe identified scientific journal articles on this relationship via a systematic literature search. The (...) following factors were taken into account: study outcome (authors’ conclusion, data-based conclusion), other content-related article characteristics (article type, sample size, research quality, specificity), content-unrelated article characteristics (language, publication title, funding source, number of authors, number of affiliations, number of references, journal impact factor), author characteristics (gender, country, affiliation), and citation characteristics (time to citation, authority, self-citation). To assess the impact of these factors on citation, we performed a series of univariate and adjusted random-effects logistic regressions, with potential citation path as unit of analysis.ResultsThirty-six articles were identified in this network, consisting of 570 potential citation paths of which 191 (34%) were realized. There was strong evidence that articles with at least one author in common, cited each other more often than articles that had no common authors (odds ratio (OR) 5.2, 95% confidence interval (CI) 3.1–8.8). Similarly, the chance of being cited was higher for articles that were empirical rather than narrative (OR 4.2, CI 2.6–6.7), that reported a large sample size (OR 5.8, CI 2.9–11.6), and that were written by authors with a high authority within the network (OR 4.1, CI 2.1–8.0). Further, there was some evidence for citation bias: articles that confirmed the relation between swimming and asthma were cited more often (OR 1.8, CI 1.1–2.9), but this finding was not robust.ConclusionsThere is clear evidence of selective citation in this research field, but the evidence for citation bias is not very strong. (shrink)

Children acquiring languages with noun classes have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the hypothesis that children are classifying nouns optimally with respect to a distribution that does not match the surface distribution of statistical features in their input. We propose three ways in which children's apparent statistical (...) insensitivity might arise, and find that all three provide ways to account for the difference between children's behavior and the optimal classifier. A fourth model combines two of these proposals and finds that children's insensitivity is best modeled as a bias to ignore certain features during classification, rather than an inability to encode those features during learning. These results provide insight into children's developing knowledge of noun classes and highlight the complex ways in which statistical information from the input interacts with children's learning processes. (shrink)

The question of language development and origin is a subject that is vital to our understanding of what it means to be human. This is reflected in the large range of academic disciplines that are dedicated to the subject. Language development has in particular been related to studies in cognitive capacity and the ability for mind reading, often termed a theory of mind. The Social Brain Hypothesis has been the only attempt to correlate a cognitive scale of complexity (...) incorporating a theory of mind and intentionality orders to the archaeological record and hominin phylogeny. However, a method is still lacking that allows a correlation of the orders of intentionality (and by inference a theory of mind and language development) to the archaeological signatures that represent the physical expression of hominin behaviour. This paper is primarily concerned with introducing a new theoretical perspective – termed the identity model – which facilitates such a correlation between a scale of cognitive acuity, hominin behaviour through the archaeological record and subsequently language development within an evolutionary context. (shrink)

The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses of child‐directed speech showing that there is indirect statistical information useful for correct auxiliary fronting in polar interrogatives and that such information is sufficient for distinguishing between grammatical and (...) ungrammatical generalizations, even in the absence of direct evidence. We further show that there are simple learning devices, such as neural networks, capable of exploiting such statistical cues, producing a bias toward correct AUX questions when compared to their ungrammatical counterparts. The results suggest that the basic assumptions of the poverty of stimulus argument may need to be reappraised. (shrink)

Recent work on the application of neural networks to language modeling has shown that models based on certain neural architectures can capture syntactic information from utterances and sentences even when not given an explicitly syntactic objective. We examine whether a fully data-driven model of language development that uses a recurrent neural network encoder for utterances can track how child language utterances change over the course of language development in a way that is comparable to what is (...) achieved using established language assessment metrics that use language-specific information carefully designed by experts. Given only transcripts of child language utterances from the CHILDES Database and no pre-specified information about language, our model captures not just the structural characteristics of child language utterances, but how these structures reflect language development over time. We establish an evaluation methodology with which we can examine how well our model tracks language development compared to three known approaches: Mean Length of Utterance, the Developmental Sentence Score, and the Index of Productive Syntax. We discuss the applicability of our model to data-driven assessment of child language development, including how a fully data-driven approach supports the possibility of increased research in multilingual and cross-lingual issues. (shrink)

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental (...) trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination. (shrink)

The present study attempts to investigate and analyze the relationship between the language used by the Hui nationality, its social situation, and identity construction from a sociosemiotic perspective, and makes a further discussion on the process of identity construction via language convergence, divergence, and maintenance. It goes further to put forward the distinction between social identity/ethnic identity and group identity/personal identity as well as the roles that language convergence and divergence have played within these identity constructions, proposes (...) that language convergence and divergence are the two crucial language strategies utilized by people in code switching, therefrom constructing a dynamic balanced identity system recursively. (shrink)

On the possibilities and limits of the ethics of artificial intelligence. An overview of current developments and debates with a focus on language processing systems. -/- Driven by the success of artificial intelligence (AI), the ethics of AI is currently enjoying a boom. Advice from ethics experts is increasingly being sought by policymakers and industry to proactively identify the risks associated with new AI technologies and to propose solutions. But how realistic are the expectations placed on AI ethics to (...) make technological development more equitable and acceptable? Doesn't AI ethics itself run the risk of becoming a fig leaf in the service of pre-determined economic and political goals? Based on these questions, this article presents and discusses some of the most current debates and proposals for dealing with the opportunities and risks of AI technologies, with a particular focus on language technologies. Although the danger of instrumentalization is present, the article concludes, the ethics of AI currently represents an important opportunity to identify problematic developments at an early stage and to initiate societal debates, political processes, and legal regulations that can make future AI developments more equitable and environmentally sound. (shrink)