Lack Of Transparency Research Articles

Exploring Generative Artificial Intelligence

This work reviews the concept of Generative Artificial Intelligence (GAI), its main characteristics, and its classification inside the Artificial Intelligence (AI) field. In this way, the main GAI foundational algorithmic architectures for data generation are presented; each considers a previously gathered data set and generates novel and synthetic data. Subsequently, use cases for generative models reported in the literature are presented, ranging from applications in healthcare, education, and customer experiences. The deployment of GAI models still needs to overcome a series of challenges inspected in this document, considering the presence of bias, lack of transparency, and hallucinations presented by the models. Finally, the risks and misuse of GAI are reviewed since widespread use of this type of technology is expected in society.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

Leveraging blockchain for sustainable supply chain management: A data privacy and security perspective

This review examines how blockchain technology can be leveraged to enhance data privacy and security in sustainable supply chain management (SSCM). As global supply chains become increasingly complex and the demand for sustainability grows, ensuring data privacy and security has become a critical concern. Traditional supply chain systems often face challenges such as data breaches, lack of transparency, and difficulty in tracing products and materials. Blockchain technology, with its decentralized, immutable, and transparent architecture, offers a promising solution to these challenges. Blockchain can enhance data security by ensuring that data is tamper-proof, traceable, and encrypted, thus protecting sensitive information across the supply chain. It provides transparency while allowing permissioned access, ensuring that stakeholders can verify data without exposing confidential information. Furthermore, privacy-preserving technologies such as zero-knowledge proofs and homomorphic encryption allow verification of data without compromising its security. Smart contracts enable automated compliance with regulatory frameworks like GDPR, reducing the risk of human error and improving operational efficiency. The integration of blockchain in SSCM can improve traceability, transparency, and accountability, thereby promoting environmental and social sustainability. By tracking the origin and journey of goods, blockchain helps verify ethical sourcing practices and reduce carbon footprints. However, the technology also presents challenges, including scalability, integration with legacy systems, and cost considerations. Through case studies in industries such as food, textiles, and renewable energy, this review highlights the practical applications and benefits of blockchain for SSCM. It concludes that blockchain has the potential to revolutionize supply chain operations, but careful consideration must be given to overcoming its technical and financial barriers to widespread adoption.

Exploring the critical factors in pharmaceutical supply chains revealed during COVID-19 and addressing missing links

The COVID-19 pandemic revealed profound weaknesses in global pharmaceutical supply chains, resulting in widespread shortages of critical medical supplies, delayed drug manufacturing, and disruptions in vaccine distribution. These supply chain vulnerabilities were exacerbated by several key factors, including an overreliance on a limited number of countries for active pharmaceutical ingredients (APIs), insufficient local manufacturing capacity, and logistical constraints in transportation and distribution networks. The pandemic also exposed inefficiencies in regulatory frameworks, leading to delays in approvals and limited agility in responding to rapidly changing demands. Furthermore, the lack of transparency and real-time monitoring across global supply chains hindered effective decision-making and coordination, aggravating the crisis. This paper provides an in-depth analysis of the critical factors that contributed to pharmaceutical supply chain disruptions during COVID-19, identifying missing links such as the need for diversification of supply sources, enhanced flexibility in manufacturing processes, and the integration of advanced technologies like artificial intelligence (AI), blockchain, and automation for supply chain optimization. The analysis highlights how supply chain visibility, digital innovation, and risk management strategies could improve resilience in the face of future global health crises. In addition to identifying these challenges, the paper proposes solutions to address the missing links in pharmaceutical supply chains, including the decentralization of production, the development of regional manufacturing hubs, and the creation of public-private partnerships to bolster local production. The role of government policies in facilitating faster regulatory approvals, fostering international cooperation, and incentivizing innovation is also explored. Case studies on successful post-COVID-19 adaptations, such as the rapid scale-up of vaccine production and the reconfiguration of personal protective equipment (PPE) supply chains, offer insights into effective strategies for building more resilient systems. Ultimately, this paper underscores the importance of a collaborative, multi-stakeholder approach to ensuring the stability and security of pharmaceutical supply chains. It calls for proactive measures to strengthen global supply chain infrastructures, improve regulatory agility, and embrace emerging technologies that can predict and respond to disruptions. These steps are crucial to safeguarding the continuous delivery of essential pharmaceuticals during future pandemics or other large-scale disruptions, ensuring global health security in an increasingly interconnected world.

A Review of Palm Oil Plasma Land Conflicts from the Perspective of Agrarian Reform: Analysis of Decision Number 3661 K/PDT/2019

Abstract: The government has addressed disparities in palm oil plantation ownership through the partnership model of the Nucleus Estate Smallholder Plantation (PIR-BUN) using a core-plasma scheme as outlined in Presidential Instruction No. 1 of 1986, aiming to achieve agrarian reform. However, in practice, the nucleus-plasma scheme has led to land conflicts and issues, one of which occurred in Teluk Bakung Village, West Kalimantan. Therefore, to ensure the effective implementation of the nucleus-plasma scheme in the future and the realization of agrarian reform, it is necessary to investigate the causes of plasma land conflicts in Teluk Bakung Village and the impacts of these conflicts on agrarian reform efforts. This study adopts a normative juridical method, examining secondary data from literature and primary data from Supreme Court Decision No. 3661 K/PDT/2019 concerning PT PALM’s default in profit-sharing on plasma land. The findings indicate that plasma land conflicts arise from differing interpretations of the 20% provision for community plantation development, inadequacies in institutional support and processes for the PIR-BUN program, and a lack of transparency from the nucleus company. These conflicts ultimately negatively impact the socio-economic aspects of plasma farmers in Teluk Bakung Village and hinder agrarian reform efforts, particularly in the region. Keywords: Agrarian Reform, PIR-BUN, Plasma Land

The Promises and Challenges of AI-Facilitated Mental Health Care

Mental health is an increasingly critical issue that demands attention. With the rapid advancement of Artificial Intelligence (AI) across various domains, AI-facilitated mental health care is particularly promising and warrants further exploration in healthcare settings. This review provides insights into the current state of AI-facilitated mental health care, identifying both its potential benefits and the challenges that need to be addressed. Particularly during the COVID-19 pandemic, when access to traditional mental health services was limited due to social distancing rules and other restrictions, the potential of AI to facilitate mental health care effectively showed. Despite the promise of AI in mental health care, several challenges remain. The lack of transparency in many algorithms, often referred to as the “black box” issue, poses significant concerns. Additionally, the rapid development and continuous iteration of numerous mental health applications have resulted in a scarcity of comprehensive data to evaluate their effectiveness. The findings from this review can inform future research and practice in the mental health field, guiding the development of more effective and transparent AI-driven solutions to support mental health conditions.

Sailing along the global tobacco supply chain: public health insights of innovative database project

Abstract Background Tobacco control commonly focuses on reducing tobacco demand rather than tobacco supply. This paper presents findings from a major four-year project on the tobacco supply chain. Transnational Tobacco and Leaf Companies (TTLCs) and other tobacco supply chain companies argue that provision of livelihoods, exports and corporate social responsibility (CSR) exonerates tobacco. Our objective was to consider these claims. Methods This paper presents the results of three studies: a literature review and two analyses of data from the innovative Tobacco Supply Chain Database (TSCD). Study 1: A scoping review of academic literature (n = 124 papers) reviewing how the tobacco supply chain embeds the tobacco industry as a development actor. Study 2: The statistical association between countries hosting TTLC subsidiaries (TSCD) and TTLC interference in health policy (Global Tobacco Industry Interference Index) was analysed (n = 76 countries). Study 3: Document and website analysis of TSCD non-TTLC tobacco supply chain companies comparing descriptions of CSR and tobacco supply chain involvement (n = 80 companies). Results Study 1: Although there are instances of advantages of involvement in the tobacco supply chain, TTLCs tends to be associated with declines in tobacco workers’ income, equality, quality of life (cultural and health and wellbeing) and nations’ environment and economy. Study 2: Countries that hosted more TTLCs had more policy interference (farming subsidiaries p=.046, tobacco product manufacturing subsidiaries p=.008). Study 3: Non-TTLC companies often lack transparency regarding their connection to tobacco. For example, 68% linked to CSR activity on their homepage whereas only 16% linked to involvement in the tobacco supply chain. Conclusions Tobacco supply chain involvement is detrimental to public health, the environment and governance. Countries and companies should seek to extricate themselves from the tobacco supply chain. Key messages • Tobacco supply chain involvement weakens public health governance despite CSR activities. Lack of transparency on supply chain involvement by companies is thus a concern. • The new Tobacco Supply Chain Database illuminates previously hidden areas of the tobacco supply chain: the location of tobacco industry subsidiaries and presence of independent companies.

Aplikasi Monitoring Pegawai ASN Kelurahan Pademangan Timur

This study aims to design, develop, and test an Android-based monitoring application for CivilServants (ASN) in Kelurahan Pademangan Timur using the prototypemethod. The background ofthe study highlights the need for an effective system for managing ASN performance at the villagelevel and the lack of research focusing on similar applications at this level. The problemidentification points to the absence of an integrated system, limited information accessibility, lackof transparency, insufficient use of information technology, and gaps in previous research. Thisstudy produces an application that enables effective monitoring and management of ASNperformance, with benefits such as increased efficiency, transparency, user satisfaction, andcontributions to the development of information technology in the public sector. The results of thisstudy are expected to provide a reference for the development of similar applications in otherregions of Indonesia.

Village Asset Management Policy Implementation In Galagamba Village, Ciwaringin District, Cirebon Regency

This study aims to analyze the management of village assets in Galagamba Village, Ciwaringin District, Regency. In the context of village development, effective asset management is essential to improve community welfare. The research method used is a combination of qualitative and quantitative approaches, with data collection through in-depth interviews, questionnaires, observation, and document analysis. The results showed that village asset management still faces various challenges, including low community participation, weak accountability, and lack of transparency in asset information management. In addition, many assets are poorly inventoried and in need of repair. The impact of this suboptimal management is seen in the social and environmental welfare of the community. Based on the findings, it is recommended to increase the capacity of human resources, open space for community participation, and apply the principle of transparency in asset management. This research provides important insights for village governments in formulating more effective and sustainable policies for village asset management in the future.

Socio economics impacts of CPEC on Pakistan; the dark side of the project

The China-Pakistan Economic Corridor (CPEC) is a flagship project under China's Belt and Road Initiative (BRI) with significant investments in energy projects, infrastructure development, and special economic zones. While CPEC has been praised as a game-changer for Pakistan but major concerns have been raised about its negative socio-economic impacts. This research article spreads light on a comprehensive literature review to find out these impacts and identify research gaps that the researchers have not filled yet. This paper reveals that CPEC intensifies existing socio-economic inequalities, displaces local communities, causes environmental degradation, and raises concerns about debt sustainability of Pakistan. The lack of transparency in project execution has further boosted these issues. In this paper I have examined the experiences of aboriginal communities and gendered repercussions, investigated labor rights and waged conditions, and environmental impacts. As the project is still not fully completed, new issues may arise with the completion and passage of time. By conducting the research further deep, policymakers can gain a better understanding of the potential outcomes of CPEC and develop strategies accordingly to eliminate any negative impact that can be hazardous for Pakistan's economy, environment, and society.

Clinical nurses’ legal roles, challenges, and responses to enabling legislation in China: a qualitative study

BackgroundWith the surge in China’s registered nurse population, defining their rights and responsibilities is crucial for ensuring the quality of medical services. However, challenges such as unresolved medical disputes and unclear duties persist due to the inadequacy and lack of transparency in current nursing laws and policies. This study evaluated nurses’ legal awareness, legislative needs, and views on laws and policies affecting their practice, providing insights for future improvements in nursing legislation.MethodsThis study employed a descriptive qualitative approach, utilizing purposive sampling to select 16 nurses for in-depth exploration of their experiences. Semi-structured interviews were conducted between December 2023 and March 2024. Data analysis was performed using directed content analysis.ResultsA total of 16 nurses with various positions and titles were interviewed. The analysis revealed three main themes and nine sub-themes, focusing on existing issues in the nursing environment, legal training needs, and attitudes towards legislative empowerment.ConclusionsIn China, there is a gap in legal knowledge and training among nurses. Enhancing their legal literacy is crucial for elevating their professional status and the quality of their work. Our findings underscore strong support for the enactment of the Nurse Act with a focus on practice conditions, rights protection, and career development. National authorities and experts must expedite this legislation to safeguard the rights of nurses, bolster their professional confidence.

Study on the Problems and Countermeasures of Tourism Market Supervision in Ethnic Areas

Ethnic regions are increasingly capturing the attention of tourists, thanks to their unique cultural heritage and natural resources, thus becoming indispensable growth points in the tourism market. However, the rapid expansion of the tourism sector has imposed heightened demands on regulatory mechanisms. Currently, the tourism market in these regions faces a myriad of challenges, including insufficient oversight, a lack of transparency in information, and varying quality among market participants. These issues not only diminish the visitor experience but also impede the advancement of sustainable tourism development. Consequently, there is an urgent need for a comprehensive and effective regulatory strategy to standardize market order and ensure tourism quality. This study analyzes the current state and challenges of the tourism market in ethnic areas and proposes a series of targeted recommendations, including the enhancement of the regulatory framework, increased enforcement efforts, improved information transparency, and the elevation of participant quality. These strategies aim to foster the robust development of the tourism market, enhance visitor satisfaction, and achieve sustainable growth in the tourism industry of ethnic regions.

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection

Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.

A Climate of Stigma, Uncertainty, and Distrust: Stakeholder Perception of Barriers to SNF Placement for Patients with Opioid Use Disorder Treated with Methadone.

Patients with opioid use disorder (OUD), especially those treated with methadone, face significant challenges to placement in a skilled nursing facility (SNF). Efforts to address this via legal actions have not resulted in improved access. To understand regulatory and non-regulatory factors that impact SNF placement of patients with OUD treated with methadone. Observational qualitative study. Stakeholders in the hospital-to-SNF referral process as well as those with specific expertise related to OUD. Open-ended, semi-structured interviews. Interviews with 15 participants identified three key themes that function together in addition to logistic and financial barriers: (1) stigma and perception of risk, (2) uncertain regulatory environment, and (3) distrust between responsible entities. Fundamentally, many SNFs do not feel they can provide necessary care related to OUD and methadone. They tend to be disinclined to care for patients with OUD and express concerns about perceived risks such as overdose, violence, or discomfort to other residents. SNFs are also very motivated to avoid regulatory citations and fines related to OUD or methadone. Since confusion and misinformation about relevant policies and procedures is common, many opt to decline these patients. Compounding these challenges, entities responsible for coordinating care demonstrate poor communication and lack of transparency with each other. Referral and declination information sent between hospitals and SNFs is often considered to be incomplete or incorrect, and many hospitals have stopped referring patients with OUDtreated with methadone to SNFs altogether. Regulatory bodies are often feared by healthcare providers and administrators and interaction is avoided. Finally, legal oversight representatives report that they do not receive sufficient information to properly investigate concerns. This study identifies the climate of stigma, uncertainty, and distrust between responsible entities that stymies improvement efforts. Creation of meaningful reform must address each of these areas.

The Interplay of DeFi and CeFi: Analyzing Decentralized Finance through the Lens of the Shiva Milk Story and the Ramayana Metaphor

This paper explores the evolution of decentralized finance (DeFi), represented as Rama, and its adversary, centralized finance (CeFi), represented as Ravana in the metaphorical context of the Ramayana. Drawing inspiration from the story of the Raja's attempt to fill a temple with milk for Lord Shiva, we examine how individual greed, lack of transparency, and improper contributions can lead to the collapse of collective financial systems. Just as Ravana's greed and manipulation disrupt harmony, CeFi is characterized by centralized control, fostering inefficiency and inequity. In contrast, DeFi offers a decentralized, transparent system that aims to align with Shiva's Linga, the symbol of purity and divine order, ensuring collective participation for an optimized financial ecosystem.

Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.

Mega corruptions in mega projects: Debating the dynamics of dam construction in Ethiopia

Background This study critically examines the dynamics of corruption within Ethiopia’s dam construction projects, focusing on the Grand Ethiopian Renaissance Dam (GERD the Gilgel Gibe series, and Genale Dawa. The research highlights how weak management practices, insufficient oversight, and centralized decision-making, resulting in contribute to inflated costs, project delays, and compromised construction quality. A comprehensive understanding of these issues is crucial for addressing the challenges that impede Ethiopia’s infrastructure development. Methods A qualitative research approach was employed, integrating both primary and secondary data sources. The study utilized four theoretical frameworks—Principal-Agent Theory, Rent-Seeking Theory, Institutional Theory, and Neo-Patrimonialism—to analyze the structural and behavioral factors enabling corruption in Ethiopia’s dam construction projects. Results The study found that corruption is largely driven by weak management practices, insufficient oversight, and centralized decision-making. These factors contribute to inflated project costs, significant delays, and compromised construction quality. The lack of transparency and inadequate monitoring exacerbate these issues, leading to inefficient resource use and undermining the projects’ intended benefits. Economic impacts include increased public debt and strained financial resources. Socially, corruption erodes public trust and adversely affects the communities involved. The findings also highlight how information asymmetry and misaligned incentives contribute to corruption, with weak governance structures and informal networks further exacerbating the problem. These issues lead to cost overruns, increased debt, and diminished public confidence, which negatively impacts development outcomes. Conclusions While corruption in Ethiopia’s dam construction projects poses significant challenges, it is not insurmountable. Strengthening legal and regulatory frameworks, enhancing institutional capacity, promoting community participation, and leveraging international cooperation are crucial for mitigating these risks. Recommendations include implementing e-procurement systems, establishing independent oversight bodies, and promoting transparency to ensure these mega projects achieve their intended economic and social benefits.

Explainable AI for Transparent and Trustworthy Tuberculosis Diagnosis: From Mere Pixels to Actionable Insights

Building transparent and trustworthy AI-powered systems for disease diagnosis has become more paramount than ever due to a lack of understanding of black box models. A lack of transparency and explainability in AI-driven models can propagate biases and erode patients' and medical practitioners' trust. To answer this challenge, Explainable AI (XAI) is drastically emerging as a practical solution and approach to tackling ethical concerns in the health sector. The overarching purpose of this paper is to highlight the advancement in XAI for tuberculosis diagnosis (TB) and identify the benefits and challenges associated with improved trust in AI-powered TB diagnosis. We explore the potential of XAI in improving TB diagnosis. We attempt to provide a complete plan to promote XAI. We examine the significant problems associated with using XAI in TB diagnosis. We argue that XAI is critical for reliable TB diagnosis by improving the interpretability of AI decision-making processes and recognising possible biases and mistakes. We evaluate techniques and methods for XAI in TB diagnosis and examine the ethical and societal ramifications. By leveraging explainable AI, we can create a more reliable and trustworthy TB diagnostic framework, ultimately improving patient outcomes and global health. Finally, we provide thorough recommendations for developing and implementing XAI in TB diagnosis using X-ray imaging

“I Don’t Know Whether It’s Priority, or Capacity, or Both”: Implementation Gaps in Employment Policies Targeting People with Disabilities in Kenya and Bangladesh

Disabled people are often excluded from employment, particularly in accessing waged employment. This exclusion persists despite enabling policies and legislature, indicating a need to address gaps in implementation. To improve policy implementation, it is crucial to better understand the gap between policy intention (what was planned) and effect (what happens), including reasons that widen this gap (what explains what happened). This study explores this in the context of Kenya and Bangladesh, drawing on key informant interviews and analysis of policies and programmes related to employment, social protection, and disability. Using the policy triangle framework, we analysed the policy context, process, actors, and content of relevant policies and programmes. The study identified key policy provisions on employment quota, social assistance, job placement services, tax incentives, and training opportunities, all of which were designed to promote employment for disabled people. However, implementation is limited in both countries, for reasons including ambiguity in policies, unavailability of data for monitoring, and lack of transparency among implementers. The data suggest that addressing these aspects would promote accountability among duty-bearers, advocacy power among rights-holders, and ultimately improve implementation and inclusion of disabled people in employment.