As a student and intern, I can’t avoid AI in my studies and work. AI is no longer a novelty but a vital tool in how I get things done in school and in the office.
AI has permeated search engines, social media, and other applications. AI is everywhere. In our studies, work, and entertainment. AI adoption is everywhere.
AI has become so widespread that it’s no longer a question of whether brands are using AI but rather how they are using AI. This is a trust issue. And trust can only happen when there is transparency. You can’t trust brands and organizations that do not tell us how they use AI, especially since their decisions affect us, users, and stakeholders.
AI is now influencing our communication, decision-making, and the flow of information. Choosing not to disclose AI use carries consequences. Opacity can mislead users, obscure accountability, and weaken trust. Just look at what’s happening in social and mainstream media, where AI remains controversial and poorly understood by many.
The Reality and Responsibility of AI Adoption
Despite its ever-spreading use, AI is still a gray area to many. Some users hide their reliance on AI tools. Others openly condemn its use, ranting about ethics, originality, accountability, and even its environmental effects. People are still uncertain about how AI fits into existing social and professional norms.
Empirical data suggests that AI adoption is well underway. As shown in the findings of Chua et al. (2020), more than 80 percent of Southeast Asian regions are already in the early stages of AI adoption. Nearly half of the surveyed organizations are already piloting AI initiatives, with only a small percentage reaching advanced implementation.
AI is inevitable. We cannot resist AI adoption. But we can be transparent about it.
The Importance of Transparency in AI
According to Werner (2024), AI transparency involves several key elements that make AI systems more understandable and accountable:
- Explainability
AI use should come with clear reasons behind its outputs. Factors, data, logic, and sources behind the results should be accessible for easy scrutiny. Users shouldn’t trust content they cannot evaluate.
- Data Disclosure
Transparency also means disclosing how data was obtained, used, and who has access to it. This openness reassures users that their information is not being exploited without their knowledge.
- Algorithmic Transparency
Organizations should also be open to how they are training their AI. Algorithms should be tested for bias, error, and reliability. Doing this shows that brands are holding themselves accountable. It reassures stakeholders that fairness and accuracy are actively considered.
- Communication
Brands and decision-makers should have an ongoing engagement with their stakeholders. Educate people on AI’s role, capabilities, and limitations. Updates should be provided when systems, policies, or models change. Transparency, in this sense, is a continuous commitment.
The Risks When Transparency Is Absent
A lack of transparency weakens accountability. You cannot identify nor reason against bias, error, or ethical lapses if responsibility is obscured.
A study showed that lack of transparency is not an isolated issue but an industry-wide trend. The 2025 Foundation Model Transparency Index, developed by researchers from Stanford and other leading institutions, found that major AI companies scored an average of just 40 out of 100 in transparency, reflecting widespread deficiencies in data disclosure, risk reporting, and accountability practices (Foundation Model Transparency Index, 2025).
The Index further reveals that transparency is largely determined by corporate choice rather than technical constraint. Most brands provide little to no information about training data, risk mitigation, or environmental impact.
Transparency as a Condition for Trust and Adoption
The success of AI adoption depends heavily on trust. When users understand how AI creates content, they are more likely to accept the sincerity of brands. When people know how their data is handled, they are more likely to accept the credibility of brands. Transparency erases skepticism. It highlights that organizations are prioritizing responsibility over efficiency.
In communication-driven contexts, users don’t feel misled if AI use is revealed. Decisions don’t appear automated if they come with explanations. Transparent and explainable AI use helps reduce trust issues by allowing users to evaluate outcomes rather than accept them blindly.
Ultimately, the question is whether organizations are willing to be accountable for how they use AI.
REFERENCES
Chua, S. G., Dobberstein, N., & Sriram, K. (2020). Racing toward the future: artificial intelligence in Southeast Asia. AT Kearney, no date, online.
Ngo, V. M. (2025). The AI transparency dilemma: when more is less for trust and adoption. Information Discovery and Delivery.
https://babl.ai/the-role-of-transparency-and-accountability-in-ai-adoption/
https://www.weforum.org/stories/2025/01/why-transparency-key-to-unlocking-ai-full-potential
https://crfm.stanford.edu/fmti/December-2025/index.html
http://pagecenter.psu.edu/blog/study-finds-companies-lacking-in-ai-transparency