Status : Verified
| Personal Name | Rudas, Shaira Anne Marie Panaligan |
|---|---|
| Resource Title | Trusting the machine: a technology foresight study on explainable AI (XAI) as the pathway to agentic AI adoption in the Philippines (2035) |
| Date Issued | 17 December 2025 |
| Abstract | Imagine a world where medical professionals, financial institutions, and workplaces no longer rely solely on human judgment. A world in which artificial intelligence (AI) systems play a central role in decision-making, analyzing complex patterns, identifying risks, and providing real-time recommendations. From diagnosing illnesses to approving loans to answering customer inquiries, AI is steadily moving from the periphery into the heart of decision-making. But along with this potential lies a fundamental question that cuts across industries and societies: can we trust the machine? Globally, artificial intelligence is praised for its rapid processing and efficiency, but it also faces criticism due to its lack of transparency and potential biases. Despite their accuracy, black-box models frequently do not provide explanations for their decision-making processes. This can lead to uncertainty for medical professionals regarding critical healthcare recommendations, difficulties for financial institutions in justifying credit evaluations, and customer dissatisfaction with chatbots that appear assured yet often deliver incorrect responses. The primary challenge extends beyond enhancing AI intelligence; it involves ensuring AI systems are comprehensible, accountable, and in alignment with human values. In the Philippines, where digital adoption is rapidly increasing in multiple sectors, the issue of trust plays an essential role. It goes beyond being a technical necessity; it serves as a social contract that connects individuals, institutions and intelligent systems. Without trust, the rate of adoption will diminish, skepticism will rise and chances for innovation will be missed. This research seeks to fill the current gap by applying technology foresight and scenario planning to explore how Explainable AI (XAI) can build trust in Agentic AI (AAI). The study aims to present different potential, probability, and preferred future scenarios for integrating Agentic |
| Degree Course | Master of Technology Management |
| Language | English |
| Keyword | Artificial intelligence; AI; Agentic AI; Explainable AI; XAI; Technology foresight |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : P - Author wishes to publish the work personally.
Access Permission : Limited Access