Artificial Intelligence and Race: a Systematic Review

Intahchomphoo, Channarong; Gundersen, Odd Erik

doi:10.1017/s1472669620000183

Cited by 17 publications

(9 citation statements)

References 22 publications

(21 reference statements)

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“…Literature has a healing function, which is embodied in two functions: "self-healing" and "other-healing," namely, healing oneself and healing others. Healing oneself means that in the process of literary creation, the writer vents his own negative emotions such as resentment, distress, pain, and resentment in the works, so that the psychological trauma can be healed, thus ensuring the writer's own mental health and personality [27]. Sima Qian's angry writing and Kafka's "Creation Sustains Me" are all typical "selfhealing."…”

Section: Discussionmentioning

confidence: 99%

Correlation Analysis of Japanese Literature and Psychotherapy Effects Based on an Equation Diagnosis Algorithm

Tingting

2022

Occupational Therapy International

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Objective. On the basis of inheriting the classical aesthetic orientation of Japanese literature, contemporary Japanese literature changes the creative methods of traditional Japanese writers, strives to transcend the national limitations of Japanese literature, and strives to capture literary materials with a “modern feel.” Combined with modern western literary techniques to express the “modern self,” and integrating popular literature with pure literature, Japanese writers created a “healing” literature that is popular all over the world. There is some value in exploring the correlation between Japanese literature and the influence of psychotherapy. Method. In this paper, an equation diagnosis algorithm based on iterative learning for a class of linear discrete systems is studied, and the existing discrete iterative learning diagnostics are improved. We exchange the order of time domain and iterative domain to achieve the purpose of point-by-point equation diagnosis. On this basis, a virtual influence initial value estimation algorithm with a sliding window is proposed. The impact that has not been diagnosed is estimated and used as the initial value of the impact, so as to reduce the number of iterations and improve the efficiency of equation diagnosis. This paper takes the public and readers as different perspectives, based on the use and satisfaction theory and prospect theory, and uses interviews and questionnaires to study the communication effect of Japanese literature from the three dimensions of cognition, emotion, and behavior. The cognitive part includes the public’s understanding and readers’ understanding and reading; the emotional part includes the public’s and readers’ impressions and emotional attitudes; the behavioral part includes the public’s reading situation and readers’ behavior changes. Results/Discussion. In the survey of loyal readers, although readers’ understanding of Japanese literature has improved compared with the general public, it is not high, and their liking and influence on behavior are slightly lower than the preset level. This paper believes that the existing problems of Japanese literature dissemination are mainly concentrated in three aspects: the market is somewhat biased, which narrows the cognitive scope of users; as a cross-cultural literary work, Japanese literature still makes readers feel that it exists; there is some sense of estrangement, which will affect their enjoyment in reading; as a niche interest, readers lack timely communication and sharing objects after reading, so it is difficult to enhance their sense of identity.

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Section: Discussionmentioning

confidence: 99%

Correlation Analysis of Japanese Literature and Psychotherapy Effects Based on an Equation Diagnosis Algorithm

Tingting

2022

Occupational Therapy International

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“…Despite of the advances in machine-learning techniques, a potential concern is that AI could threaten to extenuate inequalities in the geosciences. Bias is encoded into our machine-learning algorithms given that they are built by humans such that the use of AI and its machine-learning algorithms may reinforce and exacerbate the racism and "racial" bias humans have embedded into U.S. and western European societies' structures and institutions (Intahchomphoo & Gundersen, 2020). In addition, AI models are trained by data that are collected unequally in time, geography, and population representation (Ntoutsi et al, 2020) (Fatima et al, 2021).…”

Section: Leveraging Machine Learning and Artificial Intelligence (Ai)mentioning

confidence: 99%

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

Singha,

Sullivan,

Billings

et al. 2024

Earth's Future

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Two major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

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“…Poor data selection, due to biases, bad examples, and labels inaccuracy, might lead to the "garbage in, garbage out" phenomenon and poorly working models. When it comes to algorithms applied to the population, for example, cases have been reported in which the sample selection underrepresented or disadvantaged women and non-white people (Dieterich et al, 2016;Intahchomphoo and Gundersen, 2020), posing a wide range of ethical fairness issues (Ntoutsi et al, 2020). Furthermore, in mental health, large datasets required for good AI predictions are rare, and a high drop-out rate hinders the collection of longitudinal data: this could result in a non-representative set of data (Bickman, 2020).…”

Section: Challenges In ML and Future Scenariosmentioning

confidence: 99%

Machine learning in biosignals processing for mental health: A narrative review

et al. 2023

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Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain–computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

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Artificial Intelligence and Race: a Systematic Review

Cited by 17 publications

References 22 publications

Correlation Analysis of Japanese Literature and Psychotherapy Effects Based on an Equation Diagnosis Algorithm

Correlation Analysis of Japanese Literature and Psychotherapy Effects Based on an Equation Diagnosis Algorithm

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

Machine learning in biosignals processing for mental health: A narrative review

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