Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review.

BACKGROUND: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. OBJECTIVE: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. METHODS: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. RESULTS: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. CONCLUSIONS: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.

Transparency in Cognitive Training Meta-analyses: A Meta-review.

Meta-analyses often present flexibility regarding their inclusion criteria, outcomes of interest, statistical analyses, and assessments of the primary studies. For this reason, it is necessary to transparently report all the information that could impact the results. In this meta-review, we aimed to assess the transparency of meta-analyses that examined the benefits of cognitive training, given the ongoing controversy that exists in this field. Ninety-seven meta-analytic reviews were included, which examined a wide range of populations with different clinical conditions and ages. Regarding the reporting, information about the search of the studies, screening procedure, or data collection was detailed by most reviews. However, authors usually failed to report other aspects such as the specific meta-analytic parameters, the formula used to compute the effect sizes, or the data from primary studies that were used to compute the effect sizes. Although some of these practices have improved over the years, others remained the same. Moreover, examining the eligibility criteria of the reviews revealed a great heterogeneity in aspects such as the training duration, age cut-offs, or study designs that were considered. Preregistered meta-analyses often specified poorly how they would deal with the multiplicity of data or assess publication bias in their protocols, and some contained non-disclosed deviations in their eligibility criteria or outcomes of interests. The findings shown here, although they do not question the benefits of cognitive training, illustrate important aspects that future reviews must consider.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. METHODS: We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. RESULTS: Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. CONCLUSIONS: Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

Leah Z. Rand, Daniel P. Carpenter, Aaron S. Kesselheim, Anushka Bhaskar, Jonathan J. Darrow, and William B. Feldman Reply.

The authors respond to a letter by Mitchell Berger in the March-April 2024 issue of the Hastings Center Report concerning their essay "Securing the Trustworthiness of the FDA to Build Public Trust in Vaccines."

Measurement of relative transition strengths of 133Cs Rydberg D states using electromagnetically induced transparency.

Transition strengths between states are fundamental physical properties of atomic spectra. The differences in fine structure splitting of certain states are mainly attributed to the angular momentum parts of transition dipole matrix elements. These can be calculated by integrating the wave-functions theoretically and can be accessed by selecting corresponding polarizations of the exciting lasers experimentally. We measured the transition strengths ratios of nD 5 / 2 /nD 3 / 2 via Rydberg electromagnetically induced transparency (EIT) by changing the powers and polarizations of probing and coupling lasers in a room temperature cesium vapor cell. The variation of the ratios on the principal quantum number n which ranges from 40 to 62 is also investigated. Theoretical and experimental results agreed with each other.

Spin coated ultrathin PEDOT:PSS/SWCNT film with high electronic conductivity.

Conductive Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) has been extensively used as non-metallic electrodes. However, the relatively low electrical conductivity of pristine PEDOT:PSS film restricts its further application. Although doping high content conductive filler or increasing the film thickness are effective for enhancing the electrical property, the transparency is sacrificed, which limits the application of PEDOT:PSS films. In this study, preparing PEDOT:PSS composite film with highly conductive and transparent property was the primary purpose. To achieve this goal, single-walled carbon nanotubes (SWCNTs) and dimethyl sulfoxide (DMSO) was chosen to composite with PEDOT:PSS. The spin-coated SWCNT/PEDOT:PSS composite film exhibited excellent electrical conductivity and transparency. The electrical conductivity of composite film with desired transmittance property (78%) reached the highest value (1060.96 S cm-1) at the SWCNTs content was 6 wt%. Under the modification process applied in this work, the non-conductive PSS was partially removed by incorporated DMSO and SWCNTs. Then, the molecular chains of PEDOT stretched and adsorbed onto the surface of SWCNTs, forming a highly efficient three-dimensional conductive structure, which contributed to the enhancement of electrical conductivity and transparency. Additionally, the spin-coating process allowed for the reduction of film thickness, ensuring better transparency. This research contributed to expanding the further applications of PEDOT:PSS films in high-performance transparent film electrodes. .

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.

Increase transparency and reproducibility of real-world evidence in rare diseases through disease-specific Federated Data Networks.

PURPOSE: In rare diseases, real-world evidence (RWE) generation is often restricted due to small patient numbers and global geographic distribution. A federated data network (FDN) approach brings together multiple data sources harmonized for collaboration to increase the power of observational research. In this paper, we review how to increase reproducibility and transparency of RWE studies in rare diseases through disease-specific FDNs. METHOD: To be successful, a multiple stakeholder scientific FDN collaboration requires a strong governance model in place. In such a model, each database owner remains in full control regarding the use of and access to patient-level data and is responsible for data privacy, ethical, and legal compliance. Provided that all this is well documented and good database descriptions are in place, such a governance model results in increased transparency, while reproducibility is achieved through data curation and harmonization, and distributed analytical methods. RESULTS: Leveraging the OHDSI community set of methods and tools, two rare disease-specific FDNs are discussed in more detail. For multiple myeloma, HONEUR-the Haematology Outcomes Network in Europe-has built a strong community among the data partners dedicated to scientific exchange and research. To advance scientific knowledge in pulmonary hypertension (PH) an FDN, called PHederation, was established to form a partnership of research institutions with PH databases coming from diverse origins.

An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation by the IMPALA (Inter coMPany quALity Analytics) Consortium.

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials.

A standardized approach to measuring gender transparency in languages.

Languages can express grammatical gender through different ortho-phonological regularities present in nouns (e.g., the cues "-o" and "-a" for the masculine and the feminine respectively in Italian, Portuguese, or Spanish). The term "gender transparency" was coined to describe these regularities (Bates et al., 1995). In gendered languages, we can hence distinguish between transparent nouns, i.e., those displaying form regularities; opaque nouns, i.e., those with ambiguous endings; and irregular nouns, i.e., those that display the typical form regularities but are associated with the opposite gender. Following a descriptive analysis of such regularities, languages have been recently classified according to their degree of gender transparency, which seems relevant in regard to gender acquisition and processing. Yet, there are certain inconsistencies in determining which languages are overall transparent and which are opaque. In particular, it is not clear whether some other complex regularities such as derivational suffixes are also "transparent" cues for gender, what really constitutes an "opaque" noun, or which role orthography and morphology have in transparency. Given the existing inconsistencies in classifying languages as transparent or opaque, this work introduces a proposal to assess gender transparency systematically. Our methodology adapts the standardized factors proposed by Audring (2019) to analyse the relative complexity of gender systems. Such factors are adapted to gender transparency on the basis of the literature on gender acquisition and processing. To support the feasibility of such a proposal, the concepts have been instantiated in a quantitative model to obtain for the first time an objective measure of gender transparency using European Portuguese and Dutch as instances of target languages. Our results coincide with the theoretically expected outcome: European Portuguese obtains a high value of gender transparency while Dutch obtains a moderately low one. Future adaptations of this model to the gender systems of other languages could allow the continuum of gender transparency to sustain robust predictions in studies on gender processing and acquisition.

CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications.

Objective: To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications. Materials and Methods: Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model. We also fine-tuned PubMedBERT models limited to checklist items associated with specific sections (e.g., Methods) to evaluate whether such models could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC). Results: Fine-tuned PubMedBERT model that takes as input the sentence and the surrounding sentence representations and uses section headers yielded the best overall performance (0.71 micro-F1, 0.64 macro-F1). Data augmentation had limited positive effect, UMLS-EDA yielding slightly better results than data augmentation using GPT-4. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model yielded higher performance for methodology items, other section-specific models did not have significant impact. Conclusion: Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks and can help authors in improving the reporting quality and completeness of their manuscripts.

From "exceptional ethics" to public health ethics. Patients and proxies facing COVID-19. Ethics and public health during COVID-19 / D'une « éthique d'exception ¼ à une éthique de la santé publique. Patients et proches face à la COVID-19. Éthique et santé publique en temps de COVID-19.

The study aimed to elicit the perception and ethical considerations of patients and proxies with respect both to the individual medical decisions and public health decisions made during the COVID-19 crisis. It used a qualitative, multi-center study based on semi-directive interviews, conducted by an interdisciplinary team. The analysis was conducted using a thematic analysis approach and an ethical framework. Three themes emerged from the analysis: 1) patients, unlike proxies, did not complain about their diminished role in the decision-making process. Both highlighted the importance of "basic care" as opposed to a technical approach to treatment; 2) despite the transparency of the information process, a deep "crisis of trust" has developed between citizens and public authorities; 3) although both patients and proxies accepted the limitations of personal liberties imposed in the name of public health, they argued that these limitations should respect certain boundaries, both temporal and spacial. Above all, they should not affect basic affective human relationships, even if such boundaries are a factor in an increased risk of infection. The study showed that there is a need to reconsider the definition and the main principles of public health ethics, namely transparency and proportionality.

L'étude vise à analyser la perception que les patients et les proches de patients pris en charge pendant la crise de la COVID-19, ont pu avoir de leur prise en charge, et leurs réflexions éthiques sur la place et la définition de la santé publique. L'étude a utilisé une méthode qualitative et multicentrique. Les entretiens semi-directifs ont été conduits par une équipe pluridisciplinaire et analysés avec une approche thématique et une grille de lecture éthique à partir des principes de l'éthique biomédicale. Trois thèmes ont émergé : 1) Les patients ont exprimé peu de revendications de participer aux décisions médicales les concernant, contrairement aux proches qui se sont sentis exclus de leur rôle. Tous ont mis l'accent sur l'importance des soins de base par rapport aux soins techniques ; 2) La gestion de la crise n'est pas jugée sévèrement, mais une crise de confiance importante a été mise en évidence, malgré la « transparence ¼ affichée de l'information ; 3) les contraintes collectives ont été largement acceptées au nom de la solidarité, mais on a jugé qu'elles doivent avoir des limites (temporelle et spatiales). Surtout, elles ne doivent pas empêcher des relations humaines simples et essentielles. L'étude met en évidence qu'il est nécessaire de développer une réflexion nouvelle autour de l'éthique de la santé publique : il convient de questionner les principes de « transparence ¼ et de « proportionnalité ¼ et d'adopter une définition de « santé publique ¼ plus large que la minimisation du risque infectieux.

Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review.

Topic: This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Clinical Relevance: Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Methods: Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Results: Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Conclusion: Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Fluorine-Containing Ionogels with Stretchable, Solvent-Resistant, Wide Temperature Tolerance, and Transparent Properties for Ionic Conductors.

Stretchable ionogels, as soft ion-conducting materials, have generated significant interest. However, the integration of multiple functions into a single ionogel, including temperature tolerance, self-adhesiveness, and stability in diverse environments, remains a challenge. In this study, a new class of fluorine-containing ionogels was synthesized through photo-initiated copolymerization of fluorinated hexafluorobutyl methacrylate and butyl acrylate in a fluorinated ionic liquid 1-butyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide. The resulting ionogels demonstrate good stretchability with a fracture strain of ~1300%. Owing to the advantages of the fluorinated network and the ionic liquid, the ionogels show excellent stability in air and vacuum, as well as in various solvent media such as water, sodium chloride solution, and hexane. Additionally, the ionogels display impressive wide temperature tolerance, functioning effectively within a wide temperature range from -60 to 350 °C. Moreover, due to their adhesive properties, the ionogels can be easily attached to various substrates, including plastic, rubber, steel, and glass. Sensors made of these ionogels reliably respond to repetitive tensile-release motion and finger bending in both air and underwater. These findings suggest that the developed ionogels hold great promise for application in wearable devices.

More Is Not Always Better: Impacts of AI-Generated Confidence and Explanations in Human-Automation Interaction.

OBJECTIVE: The study aimed to enhance transparency in autonomous systems by automatically generating and visualizing confidence and explanations and assessing their impacts on performance, trust, preference, and eye-tracking behaviors in human-automation interaction. BACKGROUND: System transparency is vital to maintaining appropriate levels of trust and mission success. Previous studies presented mixed results regarding the impact of displaying likelihood information and explanations, and often relied on hand-created information, limiting scalability and failing to address real-world dynamics. METHOD: We conducted a dual-task experiment involving 42 university students who operated a simulated surveillance testbed with assistance from intelligent detectors. The study used a 2 (confidence visualization: yes vs. no) × 3 (visual explanations: none, bounding boxes, bounding boxes and keypoints) mixed design. Task performance, human trust, preference for intelligent detectors, and eye-tracking behaviors were evaluated. RESULTS: Visual explanations using bounding boxes and keypoints improved detection task performance when confidence was not displayed. Meanwhile, visual explanations enhanced trust and preference for the intelligent detector, regardless of the explanation type. Confidence visualization did not influence human trust in and preference for the intelligent detector. Moreover, both visual information slowed saccade velocities. CONCLUSION: The study demonstrated that visual explanations could improve performance, trust, and preference in human-automation interaction without confidence visualization partially by changing the search strategies. However, excessive information might cause adverse effects. APPLICATION: These findings provide guidance for the design of transparent automation, emphasizing the importance of context-appropriate and user-centered explanations to foster effective human-machine collaboration.

Ownership Beneath: Transparency of Land Ownership in Times of Economic Crime.

This article considers 'ownership beneath' in light of the Economic Crime (Transparency and Enforcement) Act 2022, which has introduced a new Schedule 4A into the Land Registration Act 2002. The legislation, with notable exceptions, requires overseas entities to publicly reveal their beneficial owners, with criminal and land law consequences if transparency requirements are not met. The article explores how ownership beneath operates and can be made more transparent, noting the three different forms of beneficial ownership employed: as control, behind a trust and as a consequence. Emphasising the distinctive nature of beneficial ownership of land, the analysis recommends amending ECTEA 2022 to focus on land ownership, not merely landowning overseas entities, facilitating greater transparency by expanding the definition of registrable beneficial owners, closing the loophole where information is not available and requiring public disclosure of most trust information.

Implementation of Digital Health Ethics: A First Step with the Adoption of 16 European Ethical Principles for Digital Health.

The potential for technology to transform health care is tremendous, but advances in digital health may also bring privacy and data security challenges that may exacerbate inequalities. Hence, it is critical that the development of digital health is included in a framework of humanistic and ethical values. France drew up its roadmap for accelerating the shift towards digital health with ethics at the forefront, along with security and interoperability pillars. Criteria such as digital health for all, transparency of data processing, trustworthy AI, and eco-responsibility and sustainability of digital health were elaborated. Under the French Presidency of the Council of the European Union, building on the proposal of ethical criteria from France, eHealth network representatives unanimously adopted 16 European ethical principles for digital health, formalizing trust commitments towards European citizens and paving the way for the European Health Data Space.

Disclosure following a medical error: lessons learned from a national initiative of workshops with patients, healthcare teams, and executives.

BACKGROUND: Despite the increase in disclosures of medical errors, transparency remains a challenge. Recognized barriers include shame, fear of litigation, disciplinary actions, and loss of patient trust. In 2018, the Israeli Ministry of Health initiated a series of workshops about disclosure of medical errors. The workshops involved medical center executives, healthcare providers, patients, and family members of patients who had previously been harmed by a medical error. This study presents the lessons learned about perceived challenges in disclosure of errors in 15 such workshops. METHODS: Data collection included participant observations in 15 workshops, full audio recordings of all of the workshops, and documentation of detailed field notes. Analysis was performed under thematic analysis guidelines. RESULTS: We identified four main themes: "Providers agree on the value of disclosure of a medical error to the patient"; "Emotional challenges of disclosure of medical error to patients"; "The medico-legal discourse challenges transparency"; and "Providers and patients call for a change in the culture regarding disclosure of medical errors". Participant observations indicated that the presence of a patient who had experienced a tragedy in another hospital, and who was willing to share it created an intimate atmosphere that enabled an open conversation between parties. CONCLUSION: The study shows the moral, human, and educational values of open discourse in a protective setting after the occurrence of a medical error. We believe that workshops like these may help foster a culture of institutional disclosure following medical errors. We recommend that the Ministry of Health extend such workshops to all healthcare facilities, establish guidelines and mandate training for skills in disclosure for all providers.

Design of an Optical Device Based on Kirigami Approach.

The aim of this work was to design a kirigami-based metamaterial with optical properties. This idea came from the necessity of a study that can improve common camouflage techniques to yield a product that is cheap, light, and easy to manufacture and assemble. The author investigated the possibility of exploiting a rotation to achieve transparency and color changing. One of the most important examples of a kirigami structure is a geometry based on rotating squares, which is a one-degree-of-freedom mechanism. In this study, light polarization and birefringence were exploited to obtain transparency and color-changing properties using two polarizers and common cellophane tape. These elements were assembled with a rotating-square structure that allowed the rotation of a polarizer placed on the structure with respect to a fixed polarizer equipped with cellophane layers.