A Practical Framework for AI Transparency

Despite rapid AI adoption, transparency concerns persist. This article introduces the Triptych Framework, a multi-level approach integrating AI Governance, Explainable AI (XAI), and Value-Sensitive Design (VSD) to build proactive trust.

Victor Hale

March 30, 2026 · Updated October 10, 2026 · 3 min read

The Black Box Paradox: AI Adoption vs. Transparency Concerns

The tension between how people feel about AI and how they use it is undeniable. A video from Ethics Unwrapped at the University of Texas notes that while many express distrust because it's unclear how AI works, tools like ChatGPT were adopted globally at an unprecedented pace. This observation is supported by survey data. According to a Consumer Reports survey from August 2023, ChatGPT was the most commonly used AI chatbot service in America, with 19% of Americans having used it in the preceding three months. This rapid uptake, which outpaces expected technology adoption timelines, highlights a crucial dynamic: for many users, an AI's immediate usefulness can overshadow concerns about its inner workings.

Deconstructing Ethical AI: The Triptych Framework

To address the crisis of ethics and accountability stemming from unregulated AI deployment, a more integrated approach is necessary. A 2025 paper by Gatot Kustyadji and fellow researchers proposes a novel conceptual model called the "Triptych Framework for Ethical AI." Developed in the context of human capital management but with broader applications, the framework synthesizes three distinct fields—AI Governance, Explainable AI (XAI), and Value-Sensitive Design (VSD)—into a cohesive, multi-level governance structure. It aims to provide a holistic roadmap for the responsible design, deployment, and oversight of artificial intelligence, moving beyond isolated solutions to algorithmic bias and opacity.

The Triptych Framework for Ethical AI Governance

The framework proposed by Kustyadji and his colleagues deconstructs the challenge of ethical AI into three interconnected pillars, each operating at a different organizational level. This structure provides a clear model for businesses to evaluate and improve their AI governance, ensuring that accountability, transparency, and ethics are considered at every stage.

Pillar 1: Macro-Level Algorithmic Accountability

The highest level of the framework focuses on governance and organizational responsibility. Macro-Level Algorithmic Accountability is about establishing clear lines of authority and oversight for AI systems. This involves creating policies, review boards, and accountability structures that ensure AI is used responsibly across the entire organization. It answers the question, "Who is responsible when the AI makes a mistake?" by embedding accountability into the corporate structure itself, rather than treating it as a purely technical problem.

Pillar 2: Meso-Level Technical Transparency via XAI

The middle pillar addresses the "black box" problem directly through technical solutions. Meso-Level Technical Transparency relies on the field of Explainable AI (XAI) to make the decision-making processes of complex algorithms understandable to humans. Instead of accepting an AI's output on faith, XAI provides tools and methods to interpret why a specific decision was reached. This is crucial for debugging, ensuring fairness, and giving stakeholders—from developers to end-users—meaningful insight into how the system operates.

Pillar 3: Micro-Level Proactive Ethics through VSD

The foundational pillar of the framework is Micro-Level Proactive Ethics, which is achieved through Value-Sensitive Design (VSD). This approach insists that human values like fairness, dignity, and trust be considered from the very beginning of the AI design and development process. Rather than trying to correct for bias or ethical issues after a system is built, VSD integrates these considerations into the initial concept and coding. It is a proactive method for embedding ethics into the technology's core, ensuring the final product aligns with fundamental human principles.

Moving Forward: Implementing a Multi-Level Approach to AI Trust

The rapid adoption of powerful AI tools demonstrates that functionality can easily eclipse transparency as a driver of user behavior. This reality means that the responsibility for ethical implementation falls squarely on the organizations that design and deploy these systems. Brands should adopt a multi-level ethical AI framework, such as the Triptych Framework, to proactively address transparency and accountability, rather than solely relying on consumer demand for transparency. By integrating accountability at the governance level, transparency at the technical level, and ethics at the design level, companies can build a robust foundation for trust. Success can be measured through the implementation of clear policies for algorithmic accountability, integration of XAI tools in AI development, and documented use of VSD principles in new AI projects.