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Journal articles on the topic 'Diverse explanations'
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Ley, Dan, Umang Bhatt, and Adrian Weller. "Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7390–98. http://dx.doi.org/10.1609/aaai.v36i7.20702.
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To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain. We broaden the exploration to examine δ-CLUE, the set of potential CLUEs within a δ ball of the original input in latent space. We study the diversity of such sets and find that many CLUEs are redundant; as such, we propose DIVerse CLUE (∇-CLUE), a set of CLUEs which each propose a distinct explanation as to how one can decrease the uncertainty associated with an input. We then further propose GLobal AMortised CLUE (GLAM-CLUE), a distinct, novel method which learns amortised mappings that apply to specific groups of uncertain inputs, taking them and efficiently transforming them in a single function call into inputs for which a model will be certain. Our experiments show that δ-CLUE, ∇-CLUE, and GLAM-CLUE all address shortcomings of CLUE and provide beneficial explanations of uncertainty estimates to practitioners.
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Yang, Mengyuan, Mengying Zhu, Yan Wang, Linxun Chen, Yilei Zhao, Xiuyuan Wang, Bing Han, Xiaolin Zheng, and Jianwei Yin. "Fine-Tuning Large Language Model Based Explainable Recommendation with Explainable Quality Reward." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 9250–59. http://dx.doi.org/10.1609/aaai.v38i8.28777.
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Large language model-based explainable recommendation (LLM-based ER) systems can provide remarkable human-like explanations and have widely received attention from researchers. However, the original LLM-based ER systems face three low-quality problems in their generated explanations, i.e., lack of personalization, inconsistency, and questionable explanation data. To address these problems, we propose a novel LLM-based ER model denoted as LLM2ER to serve as a backbone and devise two innovative explainable quality reward models for fine-tuning such a backbone in a reinforcement learning paradigm, ultimately yielding a fine-tuned model denoted as LLM2ER-EQR, which can provide high-quality explanations. LLM2ER-EQR can generate personalized, informative, and consistent high-quality explanations learned from questionable-quality explanation datasets. Extensive experiments conducted on three real-world datasets demonstrate that our model can generate fluent, diverse, informative, and highly personalized explanations.
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Cho, Hyeoncheol, Youngrock Oh, and Eunjoo Jeon. "SEEN: Seen: Sharpening Explanations for Graph Neural Networks Using Explanations From Neighborhoods." Advances in Artificial Intelligence and Machine Learning 03, no. 02 (2023): 1165–79. http://dx.doi.org/10.54364/aaiml.2023.1168.
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Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth of GNN applications, recent progress in explaining predictions from GNNs, such as sensitivity analysis, perturbation methods, and attribution methods, showed great opportunities and possibilities for explaining GNN predictions. In this study, we propose a method to improve the explanation quality of node classification tasks that can be applied in a post hoc manner through aggregation of auxiliary explanations from important neighboring nodes, named SEEN. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques due to its independent mechanism. Experiments on matching motifparticipating nodes from a given graph show great improvement in explanation accuracy of up to 12.71% and demonstrate the correlation between the auxiliary explanations and the enhanced explanation accuracy through leveraging their contributions. SEEN provides a simple but effective method to enhance the explanation quality of GNN model outputs, and this method is applicable in combination with most explainability techniques.
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Sanneman, Lindsay. "Understanding Our Robots With the Help of Human-Centered Explainable AI." XRDS: Crossroads, The ACM Magazine for Students 30, no. 1 (September 2023): 52–57. http://dx.doi.org/10.1145/3611686.
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Insights from the field of human factors can help us design human-centered explanations that enable effective human-robot interaction. Studying explanation techniques according to these human factors will be critical in understanding their efficacy across diverse contexts.
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Sorto, M. Alejandra, Carlos A. Mejía Colindres, and Aaron T. Wilson. "Informing Practice: Uncovering and Eliciting Perceptions in Linguistically Diverse Classrooms." Mathematics Teaching in the Middle School 20, no. 2 (September 2014): 72–77. http://dx.doi.org/10.5951/mathteacmiddscho.20.2.0072.
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One of the many challenges that teachers face in mathematics classrooms is determining how much of the verbal and written explanations help students accomplish instructional goals. The challenge is greater in linguistically diverse classrooms because the explanations and multiple representations are not perceived uniformly by all students.
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Clark, Stephen R. L. "Supernatural Explanations and Inspirations." European Journal for Philosophy of Religion 9, no. 3 (September 21, 2017): 49–63. http://dx.doi.org/10.24204/ejpr.v9i3.1990.
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I propose, in partial response to the rich essays by Millican & Thornhill-Miller and Salamon that religious traditions are too diverse to be represented either by a cosmological core or even (though this is more plausible) an ethical. Religious sensibility is more often inspirational than explanatory, does not always require a transcendent origin of all things (however reasonable that thesis may be in the abstract), and does not always support the sort of humanistic values preferred in the European Enlightenment. A widely shared global religion is more likely to be eclectic than carefully ‘rational’, and is likely to be opposed by a more overtly ‘supernatural’ project founded in revelation.
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Wang, Linlin, Zefeng Cai, Gerard De Melo, Zhu Cao, and Liang He. "Disentangled CVAEs with Contrastive Learning for Explainable Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13691–99. http://dx.doi.org/10.1609/aaai.v37i11.26604.
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Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. Extensive experiments demonstrate that our method generates high-quality explanations and achieves new state-of-the-art results in diverse domains.
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Li, Dingbang, Wenzhou Chen, and Xin Lin. "MSGeN: Multimodal Selective Generation Network for Grounded Explanations." Electronics 13, no. 1 (December 29, 2023): 152. http://dx.doi.org/10.3390/electronics13010152.
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Modern models have shown impressive capabilities in visual reasoning tasks. However, the interpretability of their decision-making processes remains a challenge, causing uncertainty in their reliability. In response, we present the Multimodal Selective Generation Network (MSGeN), a novel approach to enhancing interpretability and transparency in visual reasoning. MSGeN can generate explanations that seamlessly integrate diverse modal information, providing a comprehensive and intuitive understanding of its decisions. The model consists of five collaborative components: (1) the Multimodal Encoder, which encodes and fuses input data; (2) the Reasoner, which is responsible for generating stepwise inference states; (3) the Selector, which is utilized for selecting the modality for each step’s explanation; (4) the Speaker, which generates natural language descriptions; and (5) the Pointer, which produces visual cues. These components work harmoniously to generate explanations enriched with natural language context and visual cues. Our extensive experimentation demonstrates that MSGeN surpasses existing multimodal explanation generation models across various metrics, including BLEU, METEOR, ROUGE, CIDEr, SPICE, and Grounding. We also show detailed visual examples highlighting MSGeN’s ability to generate comprehensive and coherent explanations, showcasing its effectiveness through practical case studies.
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Santos, Daniel, José Augusto Baranauskas, and Renato Tinós. "Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing." Learning and Nonlinear Models 21, no. 2 (September 7, 2023): 4–15. http://dx.doi.org/10.21528/lnlm-vol21-no2-art1.
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The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree – DT). The DT is trained with an artificial dataset generated by Genetic Algorithms (GAs). The primary objective of this approach is to replicate the decision boundaries of the black-box model in proximity to the instance under explanation. We show that the artificial examples generated by the GAs in LORE are not necessarily diverse. Consequently, we propose the integration of GAs with fitness sharing in LORE to generate a more diversified subset of artificial examples. The underlying motivation is to ensure that the local decision boundaries of the DT more closely resemble those of the black-box classifier. Experimental results with two classifiers (Multilayer Perceptron and Random Forests), and four classification problems, indicate that LORE with fitness sharing yields more diverse GA populations, consequently leading to improved local explanations. These findings underscore the effectiveness of incorporating fitness sharing into the LORE methodology for enhancing the explainability of black-box classifiers.
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Rožanec, Jože, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Blaž Fortuna, and Dunja Mladenić. "Enriching Artificial Intelligence Explanations with Knowledge Fragments." Future Internet 14, no. 5 (April 29, 2022): 134. http://dx.doi.org/10.3390/fi14050134.
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Artificial intelligence models are increasingly used in manufacturing to inform decision making. Responsible decision making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into the models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google knowledge graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting. The embeddings-based approach measures the similarity between relevant concepts and retrieved media news entries and datasets’ metadata based on the word movers’ distance between embeddings. The semantic-based approach recourses to wikification and measures the Jaccard distance instead. The semantic-based approach leads to more diverse entries when displaying media events and more precise and diverse results regarding recommended datasets. We conclude that the explanations provided can be further improved with information regarding the purpose of potential actions that can be taken to influence demand and to provide “what-if” analysis capabilities.
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Chang, Shuo, F. Harper, Lingfei He, and Loren Terveen. "CrowdLens: Experimenting with Crowd-Powered Recommendation and Explanation." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 52–61. http://dx.doi.org/10.1609/icwsm.v10i1.14743.
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Recommender systems face several challenges, e.g., recommending novel and diverse items and generating helpful explanations. Where algorithms struggle, people may excel. We therefore designed CrowdLens to explore different workflows for incorporating people into the recommendation process. We did an online experiment, finding that: compared to a state-of-the-art algorithm, crowdsourcing workflows produced more diverse and novel recommendations favored by human judges;some crowdworkers produced high-quality explanations for their recommendations, and we created an accurate model for identifying high-quality explanations;volunteers from an online community generally performed better than paid crowdworkers, but appropriate algorithmic support erased this gap. We conclude by reflecting on lessons of our work for those considering a crowdsourcing approach and identifying several fundamental issues for future work.
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Menés Fernández, Laura, Isabel Salvat, and Cristina Adillón. "Expressed Beliefs about the Cause of Pain in a Pediatric Population: A Qualitative Study." Children 10, no. 6 (June 2, 2023): 1007. http://dx.doi.org/10.3390/children10061007.
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(1) Background: The aims of this study are to explore what beliefs children and adolescents manifest about the cause of the pain they describe, to compare whether there are differences between beliefs by age and the persistence of pain, and to relate the explanations of the cause of pain with current scientific evidence. (2) Methods: a cross-sectional qualitative study was used. The primary endpoint of the study was obtaining explanations of the cause of pain recorded by means of an open-ended question. The participants were school-age children attending a charted school in the province of Barcelona. (3) Results: The children and adolescents proposed a diverse range of explanations for the cause of pain that they reported in their responses. The most frequent explanation for the cause of pain were pathologies and injuries (45.95%), ergonomic issues (22.60%) and psychological issues (15.95%). (4) Conclusions: There is a lot of variety in the explanations that young people give about the cause of their pain in schoolchildren aged between 10 and 16 years old. There exists a high prevalence of explanations non-associated with tissue damage (ENAD) concerning the causes of pain described. It is necessary that future health prevention programs dedicated to early ages consider which beliefs about the cause of pain are the most frequent in the pediatric population.
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Kaufman, Robert L. "Assessing Alternative Perspectives on Race and Sex Employment Segregation." American Sociological Review 67, no. 4 (August 2002): 547–72. http://dx.doi.org/10.1177/000312240206700404.
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Four major explanations for employment segregation—skill deficits, worker preferences, economic and organizational structure, and stereotyping/queuing—are assessed using a diverse and overlapping set of predictors: general skills and training, product market structure, race- and sex-typed tasks and conditions, desirable employment and growth rates, and links to other labor market actors. A two-stage measurement and analytic strategy controls for relevant worker-level factors. Data from the 1990 census PUMs are analyzed to measure the employment segregation of black women, black men, and white women in relation to white men across 1,917 labor market positions, net of human capital, family structure, geographic residence and labor supply. Archival data provide measures of variables characterizing labor market positions. Stereotyping and queuing explanations are broadly consistent with nearly all results, while a worker preference approach applies to somewhat fewer predictors and is largely but not wholly compatible with their effects. A skill deficits explanation applies to, and is supported by, a narrow set of findings, while the economic and organizational structure explanations are restricted in their relevance and receive limited support.
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Louw, L. "Acquired cholesteatoma pathogenesis: stepwise explanations." Journal of Laryngology & Otology 124, no. 6 (February 16, 2010): 587–93. http://dx.doi.org/10.1017/s0022215109992763.
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AbstractBackground:Since Virchow's first, 1855 publication on cholesteatoma, this disease has been the subject of extensive debate. The pathogenesis of acquired cholesteatoma is repeatedly explained on the premises of the migration, hyperplasia and metaplasia theories, but proof for the latter theory remains limited. In retrospect, there is progress toward better understanding of all the pathological mechanisms involved, as expounded in this review.Discussion:The triggers for cholesteatoma onset are diverse, and may involve tympanic membrane trauma (i.e. perforation, displacement, retraction or invagination), tympanic membrane disease, and/or tympanic cavity mucosa disease. Research has revealed that cell migration is replaced under inflammatory conditions by hyperplasia, which triggers the onset of cholesteatoma. Lately, the hyperplasia theory gained prominence and circumscription of the papillary cone formation concept provided insight into cholesteatoma progression (growth and expansion). Diseased mucosa can contribute to the development of retraction pockets and cholesteatoma. The type of cholesteatoma trigger and the role of chronic inflammation during disease progression and recurrence are important in guiding clinical intervention.
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Coles, Steven, and Sarah Fairbank. "Doubly damned in the mental health system: Diagnosed and deemed diverse." Clinical Psychology Forum 1, no. 197 (May 2009): 18–21. http://dx.doi.org/10.53841/bpscpf.2009.1.197.18.
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The article discusses how the psychiatric system and biogenetic explanations of distress restrict a full appreciation of diversity. It discusses the social and political nature of diversity. Changes to clinical training are advocated as a first step towards addressing diversity.
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Au, Kathryn H. "Social Constructivism and the School Literacy Learning of Students of Diverse Backgrounds." Journal of Literacy Research 30, no. 2 (June 1998): 297–319. http://dx.doi.org/10.1080/10862969809548000.
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This theoretical review builds on the idea that social constructivism offers implications for reshaping schooling in ways that may correct the gap between the literacy achievement of students of diverse backgrounds and that of mainstream students. A diverse social con-structivist perspective may encourage literacy educators to progress from a mainstream orientation toward a serious consideration of the significance of students' ethnicity, primary language, and social class to literacy learning. From a social constructivistperspective, 5 explanations for the literacy achievement gap appear plausible: linguistic differences, cultural differences, discrimination, inferior education, and rationales for schooling. Incorporating these 5 explanations and building on the work of Cummins (1986, 1994), a conceptual framework for addressing the literacy achievement gap is proposed. This framework suggests that the school literacy learning of students of diverse backgrounds will be improved as educators address the goal of instruction, the role of the home language, instructional materials, classroom management and interaction with students, relationships with the community, instructional methods, and assessment.
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Bynner, Claire. "Intergroup relations in a super-diverse neighbourhood: The dynamics of population composition, context and community." Urban Studies 56, no. 2 (November 30, 2017): 335–51. http://dx.doi.org/10.1177/0042098017740287.
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There is now an extensive literature demonstrating that experiences of migration and diversity differ significantly between and across local geographies. Three broad explanations for differences in local outcomes have been put forward (Robinson, 2010): first, population composition – the characteristics of individuals living in the neighbourhood; second, context – the social and physical environment; and third, community – socio-cultural histories and collective identities. Few studies examine the linkages between all three explanations and their relative importance. This article applies all three explanations to intergroup relations in a super-diverse context. It draws on data from a mixed methods case study of a neighbourhood in Glasgow, Scotland where long-term white and ethnic minority communities reside alongside Central and Eastern European migrants, refugees and other recent arrivals. The evidence comprises local statistics and documentary evidence, participant observation and qualitative and walk-along interviews with residents and local organisations. The findings highlight the different ways in which people respond to super-diversity, and the importance of the neighbourhood context and the material conditions for intergroup relations. The article thus demonstrates the ambiguities that arise from applying the dynamics of population composition, context and community to neighbourhood analysis, with implications for the study of neighbourhoods more widely.
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Wagstaff, Kiri, Nina Lanza, David Thompson, Thomas Dietterich, and Martha Gilmore. "Guiding Scientific Discovery with Explanations Using DEMUD." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 905–11. http://dx.doi.org/10.1609/aaai.v27i1.8561.
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In the era of large scientific data sets, there is an urgent need for methods to automatically prioritize data for review. At the same time, for any automated method to be adopted by scientists, it must make decisions that they can understand and trust. In this paper, we propose Discovery through Eigenbasis Modeling of Uninteresting Data (DEMUD), which uses principal components modeling and reconstruction error to prioritize data. DEMUD’s major advance is to offer domain-specific explanations for its prioritizations. We evaluated DEMUD’s ability to quickly identify diverse items of interest and the value of the explanations it provides. We found that DEMUD performs as well or better than existing class discovery methods and provides, uniquely, the first explanations for why those items are of interest. Further, in collaborations with planetary scientists, we found that DEMUD (1) quickly identifies very rare items of scientific value, (2) maintains high diversity in its selections, and (3) provides explanations that greatly improve human classification accuracy.
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Bates, Vincent C., Jason B. Gossett, and Travis Stimeling. "Country Music Education for Diverse and Inclusive Music Classrooms." Music Educators Journal 107, no. 2 (December 2020): 28–34. http://dx.doi.org/10.1177/0027432120956386.
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Despite its rich heritage and enduring popularity, country music has historically been marginalized in American music education, usually in favor of more “high-brow” musical practices. This article explores potential explanations for this imbalance within the context of a general overview of cultural and social considerations and implications related to this important American art form. Finally, we outline practical steps that music teachers can take toward more inclusive and diverse approaches to music teaching and learning to include country music critically and as appropriate to meet students’ needs and interests. These steps include applications within current approaches to band, orchestra, choir, general music, songwriting, and guitar.
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Weinrich, M. L., and V. Talanquer. "Mapping students' modes of reasoning when thinking about chemical reactions used to make a desired product." Chemistry Education Research and Practice 17, no. 2 (2016): 394–406. http://dx.doi.org/10.1039/c5rp00208g.
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The central goal of this study was to analyze the complexity of students' explanations about how and why chemical reactions happen in terms of the types of causal connections students built between expressed concepts and ideas. We were particularly interested in characterizing differences in the types of reasoning applied by students with different levels of training in the chemistry, from college to graduate school. Using a qualitative research approach, we identified diverse modes of reasoning expressed by students when engaged in the analysis of different sets of chemical reactions selected to produce a targeted compound. Main findings indicate that dominant modes of reasoning varied with educational level and the nature of the task. Although participants applied diverse modes of reasoning, linear causal reasoning was prevalent across educational levels and types of tasks. Many students tended to generate explanations based on the identification of a single agent that caused a sequential chain of events. Advanced undergraduate students in our sample generated the most complex explanations. The results of our study have important implications for the development of causal mechanistic reasoning in chemistry.
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Kawakura, Shinji, Masayuki Hirafuji, Seishi Ninomiya, and Ryosuke Shibasaki. "Analyses of Diverse Agricultural Worker Data with Explainable Artificial Intelligence: XAI based on SHAP, LIME, and LightGBM." European Journal of Agriculture and Food Sciences 4, no. 6 (November 8, 2022): 11–19. http://dx.doi.org/10.24018/ejfood.2022.4.6.348.
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We use recent explainable artificial intelligence (XAI) based on SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light Gradient Boosting Machine (LightGBM) to analyze diverse physical agricultural (agri-) worker datasets. We have developed various promising body-sensing systems to enhance agri-technical advancement, training and worker development, and security. However, existing methods and systems are not sufficient for in-depth analysis of human motion. Thus, we have also developed wearable sensing systems (WS) that can capture real-time three-axis acceleration and angular velocity data related to agri-worker motion by analyzing human dynamics and statistics in different agri-fields, meadows, and gardens. After investigating the obtained time-series data using a novel program written in Python, we discuss our findings and recommendations with real agri-workers and managers. In this study, we use XAI and visualization to analyze diverse data of experienced and inexperienced agri-workers to develop an applied method for agri-directors to train agri-workers.
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Metz, S. Emlen, Deena Skolnick Weisberg, and Michael Weisberg. "A Case of Sustained Internal Contradiction: Unresolved Ambivalence between Evolution and Creationism." Journal of Cognition and Culture 20, no. 3-4 (August 26, 2020): 338–54. http://dx.doi.org/10.1163/15685373-12340088.
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Abstract Many people feel the pull of both creationism and evolution as explanations for the origin of species, despite the direct contradiction. Some respond by endorsing theistic evolution, integrating the scientific and religious explanations by positing that God initiated or guided the process of evolution. Others, however, simultaneously endorse both evolution and creationism despite the contradiction. Here, we illustrate this puzzling phenomenon with interviews with a diverse sample. This qualitative data reveals several approaches to coping with simultaneous inconsistent explanations. For example, some people seem to manage this contradiction by separating out ideological claims, which prioritize identity expression, from fact claims, which prioritize truth. Fitting with this interpretation, ambivalent individuals tended to call explanations “beliefs” (not knowledge), avoid mention of truth or falsity, and ground one or both beliefs in identity and personal history. We conclude with a brief discussion of the affordances of this distinction.
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Crystal, David S., Hirozumi Watanabe, and Ru San Chen. "Preference for diversity in competitive and cooperative contexts: A study of American and Japanese children and adolescents." International Journal of Behavioral Development 24, no. 3 (September 2000): 348–55. http://dx.doi.org/10.1080/01650250050118330.
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This study examined preferences and explanations for wanting to be in a same-versus mixedachievement classroom, and in a group of peers with similar versus dissimilar interests and hobbies on a school trip among 5th, 8th, and 11th graders in Detroit and Matsuyama, Japan. Choice of the diverse environment varied by context, location, and grade level. Across contexts, preference for diversity tended to decrease among Detroit students, and increase among Matsuyama students with age. Additionally, students in Detroit were more likely than their peers in Matsuyama to give abilitybased and activity-related explanations; students in Matsuyama were more likely than students in Detroit to give mastery-based and cognitive-related explanations. Results are discussed in terms of social comparison theory.
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Renshon, Stanley A. "The Polls: The Public's Response to the Clinton Scandals, Part 2: Diverse Explanations, Clearer Consequences." Presidential Studies Quarterly 32, no. 2 (June 2002): 412–27. http://dx.doi.org/10.1111/j.0360-4918.2002.00228.x.
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BANERJEE, MUKULIKA. "Money and Meaning in Elections: Towards a theory of the vote." Modern Asian Studies 54, no. 1 (July 8, 2019): 286–313. http://dx.doi.org/10.1017/s0026749x17000798.
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AbstractThis article offers a comprehensive set of explanations for why people vote. Based on evidence from Indian elections, where voter turnouts remain consistently high—and rising—despite voting not being compulsory, the article shows that two broad sets of reasons exist. First, a set of transactional factors, labelled ‘money’ here, encompass within it the instrumental and coercive reasons that propel people to vote. Secondly, evidence shows that people also attribute ‘meaning’ to the act of voting itself so they vote for the sake of performing the act itself. Drawing from the wider literature and the author's own ethnographic work, including comparative ethnographic research conducted by a team across India, this article brings together these diverse set of reasons to propose a holistic explanation for why people vote.
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Sokol, Kacper, and Peter Flach. "One Explanation Does Not Fit All." KI - Künstliche Intelligenz 34, no. 2 (February 4, 2020): 235–50. http://dx.doi.org/10.1007/s13218-020-00637-y.
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Abstract The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system’s operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations—a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up “What if?” questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats. Furthermore, we discuss the challenges of mirroring the explainee’s mental model, which is the main building block of intelligible human–machine interactions. We also deliberate on the risks of allowing the explainee to freely manipulate the explanations and thereby extracting information about the underlying predictive model, which might be leveraged by malicious actors to steal or game the model. Finally, building an end-to-end interactive explainability system is a challenging engineering task; unless the main goal is its deployment, we recommend “Wizard of Oz” studies as a proxy for testing and evaluating standalone interactive explainability algorithms.
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Jones, Adam M., Gozde Sahin, Zachary W. Murdock, Yunhao Ge, Ao Xu, Yuecheng Li, Di Wu, et al. "USC-DCT: A Collection of Diverse Classification Tasks." Data 8, no. 10 (October 12, 2023): 153. http://dx.doi.org/10.3390/data8100153.
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Machine learning is a crucial tool for both academic and real-world applications. Classification problems are often used as the preferred showcase in this space, which has led to a wide variety of datasets being collected and utilized for a myriad of applications. Unfortunately, there is very little standardization in how these datasets are collected, processed, and disseminated. As new learning paradigms like lifelong or meta-learning become more popular, the demand for merging tasks for at-scale evaluation of algorithms has also increased. This paper provides a methodology for processing and cleaning datasets that can be applied to existing or new classification tasks as well as implements these practices in a collection of diverse classification tasks called USC-DCT. Constructed using 107 classification tasks collected from the internet, this collection provides a transparent and standardized pipeline that can be useful for many different applications and frameworks. While there are currently 107 tasks, USC-DCT is designed to enable future growth. Additional discussion provides explanations of applications in machine learning paradigms such as transfer, lifelong, or meta-learning, how revisions to the collection will be handled, and further tips for curating and using classification tasks at this scale.
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Taylor, Claire. "Elite Reform and Popular Heresy inc.1000: ‘Revitalization Movements’ as a Model for Understanding Religious Dissidence Historically." Studies in Church History 42 (2006): 41–53. http://dx.doi.org/10.1017/s042420840000382x.
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Amongst various features still being evaluated as characteristic of West Frankish society inc.1000 AD, one of the most striking, is that into the middle of the century a range of essentially unrelated and geographically widespread sources speak of ‘heresy’ newly affecting the populace. But how should we interpret these apparently diverse phenomena? Of the models which heresiologists explore, too often overlooked is that proposed by Janet Nelson in this very forum in 1971. Her thesis, that a ‘crisis in theodicy’ produced a cognitive need for new explanations which ‘heresy’ answered, whilst not explicitly anthropological, focused on understanding phenomena within societal wholes. This explanation was challenged by the anthropologist Talal Asad, who argued that heretical activity simply indicated urban movements over which clerics were unable to extend their authority.
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Levshina, Natalia. "Finding the best fit for direct and indirect causation: a typological study." Lingua Posnaniensis 58, no. 2 (December 20, 2016): 65–82. http://dx.doi.org/10.1515/linpo-2016-0010.
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AbstractThe contrast between direct and indirect causation is the most widely discussed semantic distinction in the literature on causative constructions. This distinction has been claimed to correlate with a number of formal parameters, such as formal distance, productivity and length, which are linked to different functional and diachronic explanations based on the principles of iconicity and economy. The present study tests these claims on a typologically representative sample of languages from 46 diverse families, examining four formal variables and their association with (in)directness of causation. According to the data, formal length displays the most pervasive association with the semantic distinction in question, which supports the economy-based explanation. In addition, the relative prominence of the other formal parameters depends on the type of causatives and their stage of grammaticalization.
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Endres, Anthony M., and David A. Harper. "Capital in the history of economic thought: charting the ontological underworld." Cambridge Journal of Economics 44, no. 5 (August 27, 2020): 1069–91. http://dx.doi.org/10.1093/cje/beaa026.
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Abstract We undertake a comprehensive descriptive and comparative ontology of capital in the history of economic thought post-1870. Beginning with the pioneering contributions of Menger, Böhm-Bawerk, Clark and Knight, we reassess the familiar dualistic ontology of capital that contrasts ‘materialist’ and ‘fundist’ approaches. Advancing beyond this dualism, we find that the ontology of capital is an evolving mosaic presenting many nuances and overlapping with other ontologies concerning notions of time and atomism. There is no substitute for examining the diverse theories, causal explanations and conceptual systems in which capital is embedded. In episodic capital controversies, economists have employed distinctive metaphors of capital revealing hidden presuppositions that imply specific functional and dispositional properties of capital. Ontological comparison can uncover implicit ideas about capital, as evidenced in the metaphors used by Böhm-Bawerk, Hayek and Robinson. The benefits of a descriptive and comparative approach are further illustrated in our critical appraisal of the modern monetary ontology of capital associated with Piketty, business finance and growth accounting. Differentiated by their specific ontologies, each explanation of capital in market economies should be regarded as at best a very partial account, though our assessment shows that some explanations are relatively more fragmentary and impoverished than others.
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Hassan, Ali, Riza Sulaiman, Mansoor Abdullateef Abdulgabber, and Hasan Kahtan. "TOWARDS USER-CENTRIC EXPLANATIONS FOR EXPLAINABLE MODELS: A REVIEW." Journal of Information System and Technology Management 6, no. 22 (September 1, 2021): 36–50. http://dx.doi.org/10.35631/jistm.622004.
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Recent advances in artificial intelligence, particularly in the field of machine learning (ML), have shown that these models can be incredibly successful, producing encouraging results and leading to diverse applications. Despite the promise of artificial intelligence, without transparency of machine learning models, it is difficult for stakeholders to trust the results of such models, which can hinder successful adoption. This concern has sparked scientific interest and led to the development of transparency-supporting algorithms. Although studies have raised awareness of the need for explainable AI, the question of how to meet real users' needs for understanding AI remains unresolved. This study provides a review of the literature on human-centric Machine Learning and new approaches to user-centric explanations for deep learning models. We highlight the challenges and opportunities facing this area of research. The goal is for this review to serve as a resource for both researchers and practitioners. The study found that one of the most difficult aspects of implementing machine learning models is gaining the trust of end-users.
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Fábián, Katalin. "Postmortem: Explanations for the Consolidation of the Hungarian Authoritarian Turn after 2010." Hungarian Studies Review 50, no. 1-2 (November 1, 2023): 172–89. http://dx.doi.org/10.5325/hungarianstud.50.1-2.0172.
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Abstract This book review essay builds on previous analysis that appeared in Hungarian Studies Review (vol. 48, no. 2, Spring 2021), as it recognizes the past three years and compares a new set of six recently published scholarly monographs that provide explanations of the causes and consequences of the consolidation of authoritarian rule in Hungary since 2010. Together, the otherwise diverse thematic and disciplinary foci of these books converge in establishing the interlocking historical, economic, and political factors that have allowed for the emergence and deepened the hegemony of thus far four consecutive Orbán governments.
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Anand Reddy, S. Tharun. "Human-Computer Interaction Techniques for Explainable Artificial Intelligence Systems." Research & Review: Machine Learning and Cloud Computing 3, no. 1 (March 26, 2024): 1–7. http://dx.doi.org/10.46610/rtaia.2024.v03i01.001.
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As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdisciplinary collaboration between HCI and AI. The recommendations in this article provide a starting point for designing such systems. In essence, XAI presents a significant opportunity to improve the transparency of AI systems, but it requires careful design and implementation to be effective.
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Loranty, Krzysztof, and Janusz Ropski. "Pedagogical implications of evolutionary explanations concerning interpersonal and intergroup relations." Studia Administracji i Bezpieczeństwa 10, no. 10 (June 30, 2021): 169–78. http://dx.doi.org/10.5604/01.3001.0015.6250.
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Making the world a safe place has been the aim of mankind since time immemorial. Representatives of various sciences have made an appreciable effort to help this dream come true. They have constructed theoretical systems and found practical solutions to problems which are the most significant for people. Unfortunately, in many cases research carried out by representatives of humanistic and social sciences has turned out to be unfruitful. Efforts which improve relations between people are particularly crucial for educationalists, that is they try to constrain or eliminate negative relationships with the view of benefits stemming from positive relations. In spite of considerable successes both in theory as well as in practical activities, we have to constantly face the necessity to answer the question: what should be done to bring together people who are willing to cooperate and love, not to perform diverse forms of aggression and rivalry?
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Laliyo, Lukman Abdul Rauf, Rahmat Utina, Rustam Husain, Masri Kudrat Umar, Muhammad Rifai Katili, and Citra Panigoro. "Evaluating students’ ability in constructing scientific explanations on chemical phenomena." Eurasia Journal of Mathematics, Science and Technology Education 19, no. 9 (September 1, 2023): em2328. http://dx.doi.org/10.29333/ejmste/13524.
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Evaluation of students’ ability in constructing scientific explanations on scientific phenomena is essential as an effort to obtain information and feedback for innovation in learning process and curriculum development. Unfortunately, this issue is still left unexplored by researchers in chemistry education. Such is presumed to occur due to validated instruments, measurements, analysis techniques, and diverse epistemological values that leave much space to be investigated. Employing a Rasch model, we intended to validate test of ability in constructing scientific explanations on chemical phenomena, examine data fit with the Rasch model, evaluate difference in the students’ ability in constructing scientific explanations, investigate items with different functions, and diagnose causes for difference in item difficulty level. The respondents were 550 students from seven senior high schools in three regencies/cities and 153 university students in Sulawesi, Indonesia. Data were collected by 30 test items; each item consisted of three questions measuring students’ ability in proposing their knowledge (Q1), evidence (Q2), and reasoning (Q3). Their responses were assessed on criteria and analyzed using the Rasch partial credit model. This model applies an individual-centered statistical approach allowing researchers to measure up to item and individual level. Results suggested that data fit the Rasch model measurement. Also, students’ ability in constructing scientific explanations varied significantly. We found no items with different functions, signifying that sex and hometown do not influence students’ ability. However, based on item logit value grouping, it was discovered that item difficulty level also varied among students. This was particularly due to students’ lack of chemistry concepts mastery that lowered their ability and accuracy in constructing scientific explanation. This shows lack of epistemological engagement of students in learning process. In conclusion, this study provides valuable insights into students’ ability to construct scientific explanations and sheds light on factors that influence their performance in this area. Findings highlight need for targeted interventions that address students’ conceptual understanding and engagement with chemistry concepts, as well as promote critical thinking and scientific reasoning skills. This has important implications for science education and can inform curriculum development and evaluation policies.
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THOMSON, WILLIAM. "From Sounds To Music: The Contextualizations of Pitch." Music Perception 21, no. 3 (2004): 431–56. http://dx.doi.org/10.1525/mp.2004.21.3.431.
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Current musicology demands opposing explanations of pitch structure, a styles-driven approach that ignores both the kinetics common to diverse musics and the broadly shared bases of human perception. To establish a unified theory, a ““from-bit-to-whole”” path is argued, noting sensory, neural, and cognitive domains as they are relevant and empirically confirmed. After preliminary discussion of the most elemental dynamics of tone bunching into wholes, an extensive examination of melodic pitch framing ensues. The tonality frame is observed in melodies from a broad sampling of eras and cultures; thus is confirmed a conception of organization that ties the spectral content of the single tone to the chord and to melody, both bearing in common the properties of sonance and root. Such an explanation helps link current musical practice with conceptualizations and practices of both ancient and exotic cultures.
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MYERS-SCOTTON, CAROL, and AGNES BOLONYAI. "Calculating speakers: Codeswitching in a rational choice model." Language in Society 30, no. 1 (January 2001): 1–28. http://dx.doi.org/10.1017/s0047404501001014.
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Although the methodologies for describing many types of linguistic variation have been well developed, satisfactory theoretical links between data and explanation – especially links that include causal mechanisms – remain lacking. This article argues, somewhat paradoxically, that even though most choices reflect some societal pattern, speakers make linguistic choices as individuals. That is, choices ultimately lie with the individual and are rationally based. Rational Choice Models (e.g. Elster 1979, 1989, 1997) provide explanatory mechanisms for the ways actors in society select from alternative structures and available options. The Rational Choice approach taken here is enhanced by diverse theories of human action (e.g. Damasio 1996, Klein 1998, Lessig 1995). Analysis of codeswitching examples within a recasting of the Markedness Model (Myers-Scotton, e.g. 1993, 1998) suggests how a rationally based model offers better explanations for linguistic variation than do other approaches.
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Xie, Zhouhang, Sameer Singh, Julian McAuley, and Bodhisattwa Prasad Majumder. "Factual and Informative Review Generation for Explainable Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13816–24. http://dx.doi.org/10.1609/aaai.v37i11.26618.
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Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language ‘rationales’ for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that could generate both informative and factually grounded explanations. Inspired by recent success in using retrieved content in addition to parametric knowledge for generation, we propose to augment the generator with a personalized retriever, where the retriever's output serves as external knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and Amazon Movie Reviews dataset show our model could generate explanations that more reliably entail existing reviews, are more diverse, and are rated more informative by human evaluators.
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Watson, David S., Limor Gultchin, Ankur Taly, and Luciano Floridi. "Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice." Minds and Machines 32, no. 1 (March 2022): 185–218. http://dx.doi.org/10.1007/s11023-022-09598-7.
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AbstractNecessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method’s competitive performance against state of the art XAI tools on a diverse array of tasks.
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Geng, Zhuangzhuang, and Zhonghua Gao. "Mammalian PRC1 Complexes: Compositional Complexity and Diverse Molecular Mechanisms." International Journal of Molecular Sciences 21, no. 22 (November 14, 2020): 8594. http://dx.doi.org/10.3390/ijms21228594.
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Polycomb group (PcG) proteins function as vital epigenetic regulators in various biological processes, including pluripotency, development, and carcinogenesis. PcG proteins form multicomponent complexes, and two major types of protein complexes have been identified in mammals to date, Polycomb Repressive Complexes 1 and 2 (PRC1 and PRC2). The PRC1 complexes are composed in a hierarchical manner in which the catalytic core, RING1A/B, exclusively interacts with one of six Polycomb group RING finger (PCGF) proteins. This association with specific PCGF proteins allows for PRC1 to be subdivided into six distinct groups, each with their own unique modes of action arising from the distinct set of associated proteins. Historically, PRC1 was considered to be a transcription repressor that deposited monoubiquitylation of histone H2A at lysine 119 (H2AK119ub1) and compacted local chromatin. More recently, there is increasing evidence that demonstrates the transcription activation role of PRC1. Moreover, studies on the higher-order chromatin structure have revealed a new function for PRC1 in mediating long-range interactions. This provides a different perspective regarding both the transcription activation and repression characteristics of PRC1. This review summarizes new advancements regarding the composition of mammalian PRC1 and accompanying explanations of how diverse PRC1-associated proteins participate in distinct transcription regulation mechanisms.
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Reed, Catherine L., Jefferson D. Grubb, and Piotr Winkielman. "Emulation theory offers conceptual gains but needs filters." Behavioral and Brain Sciences 27, no. 3 (June 2004): 411–12. http://dx.doi.org/10.1017/s0140525x04370093.
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Much can be gained by specifying the operation of the emulation process. A brief review of studies from diverse domains, including complex motor-skill representation, emotion perception, and face memory, highlights that emulation theory offers precise explanations of results and novel predictions. However, the neural instantiation of the emulation process requires development to move the theory from armchair to laboratory.
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Fadillah Rahmah, Hariana Restu, Neni Wahyuningtyas, Nurul Ratnawati, Refki Marsida, M. Khoirul Anna Waladul Mufid, and Mohd Hairy Ibrahim. "Development of “ARCIL” Media in Prehistoric Culture Materials for Junior High School Students." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 22 (November 29, 2022): 176–90. http://dx.doi.org/10.3991/ijim.v16i22.36155.
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the purpose of this research and development is to produce learning media that are real, concrete, and interesting using augmented reality (AR) technology on learning material that contain pre-literate cultural products. This research and development uses ADDIE instructional design from the RnD method with five stages namely analysis, design, development, implementation, and evaluation. The data obtained is quantitative and qualitative data. Research instruments in the form of closed questionnaires with a level scale of 1-4 to obtain quantitative data and open questionnaires to obtain qualitative data about product usage advice. The results of the study show that, media eligibility based on the assessment of linguists obtained a 90.625%, material experts obtained a 94.12%, media experts obtained a 97.62%. Media practicality tests based on user responses by education practitioners gained a 90.91% and by students obtained a 90.58% score. Based on the results of the study, ARCIL learning media is very valid and practical to use in the learning process. There are several suggestions for further research, namely, (1) Adding other types of prehistorical culture results that are more diverse, (2) Provide variations in the form of other learning materials that are more diverse, (3) The addition of audio explanations so that students better understand the explanation of the material presented.
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Ord, Terry J., and Joan Garcia-Porta. "Is sociality required for the evolution of communicative complexity? Evidence weighed against alternative hypotheses in diverse taxonomic groups." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1597 (July 5, 2012): 1811–28. http://dx.doi.org/10.1098/rstb.2011.0215.
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Complex social communication is expected to evolve whenever animals engage in many and varied social interactions; that is, sociality should promote communicative complexity. Yet, informal comparisons among phylogenetically independent taxonomic groups seem to cast doubt on the putative role of social factors in the evolution of complex communication. Here, we provide a formal test of the sociality hypothesis alongside alternative explanations for the evolution of communicative complexity. We compiled data documenting variations in signal complexity among closely related species for several case study groups—ants, frogs, lizards and birds—and used new phylogenetic methods to investigate the factors underlying communication evolution. Social factors were only implicated in the evolution of complex visual signals in lizards. Ecology, and to some degree allometry, were most likely explanations for complexity in the vocal signals of frogs (ecology) and birds (ecology and allometry). There was some evidence for adaptive evolution in the pheromone complexity of ants, although no compelling selection pressure was identified. For most taxa, phylogenetic null models were consistently ranked above adaptive models and, for some taxa, signal complexity seems to have accumulated in species via incremental or random changes over long periods of evolutionary time. Becoming social presumably leads to the origin of social communication in animals, but its subsequent influence on the trajectory of signal evolution has been neither clear-cut nor general among taxonomic groups.
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Corral Verdugo, Víctor. "Structural Equations for Testing Nested Models in Interdisciplinary Research." Psicumex 14 (December 28, 2023): 1–28. http://dx.doi.org/10.36793/psicumex.v14i1.589.
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Barriers to compare opposed or alternative scientific theories exist, based on diverse premises. One is the concept of incommensurability, the idea suggesting that different paradigms or theories cannot be contrasted since they do not share common tenets. This paper proposes the use of nested models for testing the efficacy of isolated disciplinary explanations of psychological and social problems versus the power of interdisciplinary explanations. According to this approach, such nested models would include alternative disciplinary theories competing against each other and against an inclusive model that combines these unidisciplinary explanations. This situation is illustrated with an empirical study using a questionnaire on predictors of precautionary behaviors against COVID-19. Data was analyzed using structural equations, considering a psychological and a health-science perspective, and integrated into an interdisciplinary model. Results from this study showed that the best model was the interdisciplinary model, thus providing some evidence for the use of nested models as a method to integrate different disciplines. The advantages of this approach are discussed in the face of the growing, complex, and serious problems that humanity is nowadays experiencing.
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Harris, Jody. "Narratives of nutrition: Alternative explanations for international nutrition practice." World Nutrition 10, no. 4 (December 30, 2019): 99–125. http://dx.doi.org/10.26596/wn.201910499-125.
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International public health nutrition is an arm of international development that has recently gained visibility and traction. With growing numbers of actors involved, there are however multiple potential perspectives on what nutrition action means in practice. This empirical study aims to provide fresh insight and a stimulus to debate around research and practice in the world of international nutrition, exploring through literature review, interviews and political and social theory the questions: How has the discourse underpinning nutrition policy and practice evolved internationally over time; and how have changing narratives and interests affected the global agenda for nutrition?
A dominant discourse in international nutrition currently is of the need for multi-sectoral action for the reduction of child stunting. The paper traces the evolution of this narrative through analysis of conflict among paradigms and among the actors that propagate them; the role of discursive strategies and framings as ‘strategically ambiguous’ to bring diverse actors together, though with sometimes contradictory actions in pursuit of a common stated goal; and the ‘rendering technical’ of complex, often politically-charged processes in order to more simply frame a response.
There are practical implications of these divergent philosophies, ambiguous language, and contingent knowledge for the nutrition community and its actions to reduce the global burden of malnutrition. Problematizing nutrition issues in certain ways has implications for what is done to address them, so policy makers and practitioners should reflect on the limits that the ascendant paradigms, popular framings, and dominant forms of knowledge might impose on what may be done in their name.
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Üzümçeker, Emir. "The limits of the use of locus of control in industrial psychology: A critical evaluation." Psychological Thought 9, no. 2 (October 28, 2016): 149–58. http://dx.doi.org/10.5964/psyct.v9i2.186.
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Locus of control is a personality variable that is employed by researchers from diverse disciplines. This article examines the limits of the construct’s usage in industrial and organizational psychology. Although locus of control is documented to predict a wide array of workplace behavior such as job satisfaction, job performance and turnover intention, some important conceptual, methodological and empirical flaws raise suspicions on the explanations proposed by researchers. Considering the shortage of experimental and longitudinal evidence, it is argued that the causal direction of the well-established correlations between locus of control and other organizational behavioral variables might be the opposite of the theory’s expectation. It is also claimed that some related constructs such as self-efficacy and belief in a just world might provide further explanations for observed correlations.
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Breeze, Maddie, Hugo Gorringe, Lynn Jamieson, and Michael Rosie. "‘Everybody's Scottish at the end of the day’: Nationalism and Social Justice Amongst Young Yes Voters." Scottish Affairs 24, no. 4 (November 2015): 419–31. http://dx.doi.org/10.3366/scot.2015.0094.
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In the wake of the diverse mobilization for Yes in the 2014 Independence Referendum and unprecedented SNP gains in the 2015 General Election, a number of commentators deployed a rhetoric of ‘dangerous nationalism’ by way of explanation and criticism. Such an interpretation is refuted by survey evidence and complicated by sociologies of nationalism and national identity. This short article joins these debates, and presents analysis of ten explorative interviews conducted with young Yes voters between the Referendum and the General Election. There is no simple narrative of ‘nationalism’ in these young people's accounts. Some explicitly distanced themselves from nationalist sentiment, emphasising a commitment to social justice as their key motivation for Yes. Others shared this primary concern but expressed it as overlapping with a national(ist) identity in complex ways. While disavowals of ‘romantic’ or ‘narrow’ nationalism occur on both sides of the independence debate, this study points to how more banal, and nuanced discourses of national(ist) identity underpin respondents’ explanations for voting Yes.
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Arya, Vijay, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, et al. "AI Explainability 360: Impact and Design." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12651–57. http://dx.doi.org/10.1609/aaai.v36i11.21540.
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As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360, an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
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Sandis, Constantine. "One Fell Swoop." Journal of the Philosophy of History 9, no. 3 (November 2, 2015): 372–92. http://dx.doi.org/10.1163/18722636-12341308.
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In this essay I revisit some anti-causalist arguments relating to reason-giving explanations of action put forth by numerous philosophers writing in the late ‘50s and early ‘60s in what Donald Davidson dismissively described as a ‘neo-Wittgensteinian current of small red books’. While chiefly remembered for subscribing to what has come to be called the ‘logical connection’ argument, the positions defended across these volumes are in fact as diverse as they are subtle, united largely by a an anti-scientistic spirit which may reasonably be described as historicist. I argue that while Davidson’s causalist attack was motivated by an important explanatory insight borrowed from Hempel, it caused serious damage to the philosophy of action by effectively brushing over a number of vital distinctions made in the aforementioned works. In seeking to revive these I propose an approach to the theory of action explanation that rescues the anti-causalist baby from the historicist bathwater.
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Kim, Kyungmin, Theresa Frangiosa, Virginia Biggar, Dawne Finnie, Lauren Bangerter, Joseph Gaugler, Maria Lapid, and Joan Griffin. "A METHODOLOGICAL APPROACH TO IDENTIFY EPISODES OF LUCIDITY AMONG PEOPLE WITH ALZHEIMER'S DISEASE." Innovation in Aging 6, Supplement_1 (November 1, 2022): 88–89. http://dx.doi.org/10.1093/geroni/igac059.352.
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Abstract People with late-stage Alzheimer’s disease and related dementias (ADRD) who are assumed to have lost coherent cognitive capacity may exhibit unexpected episodes of lucidity (EL). Given the transient nature and lack of scientific explanation of the phenomenon, EL is under-investigated and poorly understood. To better understand this phenomenon, we set out to develop an operational definition of EL. Based on survey data from former and current family caregivers participating in UsAgainstAlzheimer’s A-LIST® (N = 480), we defined four EL typologies. We then interviewed 25 caregiver respondents about their experiences and used analyzed qualitative data to refine the preliminary typologies. Finally, we conducted a Delphi consensus panel with clinicians, researchers, and health care providers with medical, pharmacological, and clinical expertise to describe potential explanations for EL to help further refine the typologies. Next we will test the validity of these typologies in a prospective, demographically diverse sample of current family caregivers.