Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance

Bell, Andrew; Nov, Oded; Stoyanovich, Julia

doi:10.1017/dap.2023.8

Cited by 12 publications

(3 citation statements)

References 78 publications

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“…On non-high-risk systems, only limited transparency obligations are imposed, while for high-risk systems, many restrictions are imposed on quality, documentation, traceability, transparency, human oversight, accuracy, and robustness. Bell et al [46] state that transparency is left to the technologists to achieve and propose a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems, which is a useful initiative. Besides GDPR, there are other privacy laws for which XAI might be an interesting development.…”

Section: Legal and Regulatory Compliancementioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

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Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

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Section: Legal and Regulatory Compliancementioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

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“…Current scholars are unanimous in calling for expanding the concept of SCT by disclosing non-operational information to external stakeholders rather than supply chain partners (Schnackenberg & Tomlinson, 2016). SCT shifts the focus from internal supply chain partners to external stakeholders such as consumers, governments, and non-governmental organizations (Bell et al, 2023). Sodhi and Tang (2019) claim SCT discloses information about upstream firms and products to the public, including consumers and investors.…”

Section: Supply Chain Transparencymentioning

confidence: 99%

Creating social value through operational supply chain transparency

He,

Liu,

Liu

et al. 2023

Corp Soc Responsibility Env

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Whether inclusive business (IB) supply chain transparency (SCT) can add value to a firm as an essential strategic resource is controversial. This study investigates how IB SCT affects willingness to pay a premium (WPP). Conditional process analysis was used to analyze survey data collected from 633 Chinese subjects in a scenario‐based role‐playing experiment. Analysis of the responses demonstrates that IB SCT indirectly enhances WPP through indirect reciprocity. In addition, the stronger the costly prosocial attributes of customers, the greater the indirect effect of IB SCT on WPP. The results indicate that IB SCT can add value to firms through consumer rewards, especially for costly prosocial consumers. These research constructs provide valuable insights for operators adopting IB to help them communicate and coordinate IB resources with consumers for IB sustainability.

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“…Recent works on explainable ML methods for policy development (Amarasinghe et al, 2023) underline the importance of contextualizing ML explanations and highlight the limitations of existing XAI techniques. Furthermore, they highlight the importance of stakeholder’s engagement and the need to prioritize policymakers’ requirements rather than relying on technology experts to produce explanations for ML-based policies (Bell et al, 2023). Moreover, there are no agreed and proven ways for selecting models that balance performance and explainability in line with the requirements of policymakers. Regulatory compliance : ML-based systems for public policymaking must comply with emerging regulations in AI, such as the AI Act of the European Parliament and Council of Europe (European Commission, 2021).…”

Section: Introductionmentioning

confidence: 99%

Explainable and transparent artificial intelligence for public policymaking

Papadakis,

Christou,

Ipektsidis

et al. 2024

Data & Policy

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Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

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Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance

Cited by 12 publications

References 78 publications

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Creating social value through operational supply chain transparency

Explainable and transparent artificial intelligence for public policymaking

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