[Good Business] The AI nutrition label: Why we need to know what’s inside before we trust it

• The integration of AI into various sectors raises fundamental questions about trust, transparency, and explainability, emphasizing the need for clarity in AI decision-making processes.
• Trustworthy AI requires that organizations be open about how AI systems operate, ensuring that users can understand and question the decisions made by these technologies.
• Responsibility for AI outcomes lies with humans, and fostering a culture of transparency and accountability is essential for building trust in AI applications.

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Whenever a new artificial intelligence (AI) application is introduced, the first questions are usually practical: What can it do? Will it make work faster? Can it improve productivity, reduce costs, or help us make better decisions?

These questions matter as AI becomes increasingly integrated into our workplaces, schools, businesses, and everyday lives. But before we ask what AI can do for us, perhaps we should first ask a more fundamental question:

Can we trust it?

This question lies at the heart of Transparency and Explainability, one of the eight guiding principles in the Association of Analytics and AI of the Philippines (AAP) Code of Ethics for AI Professionals. It belongs to the pillar of Trustworthy AI, which challenges us to ask a simple but powerful Filipino question:

“Mapagkakatiwalaan ba ito?” (Can this be trusted?)

The question is familiar because we ask it in many areas of life. When we buy food, we read the label to understand its ingredients, nutritional value, and expiration date. When a doctor prescribes medicine, we ask what it is for, how much to take, and what side effects it may have. (READ: Warning labels on food? How a new bill could change Filipino diets)

We do not consume something simply because it is available or because someone says it works. We seek clarity because trust is built on understanding.

We should apply the same principle to AI.

Today, AI helps determine what appears in our social media feeds, what products are recommended to us, and what content we consume. Organizations use it to screen job applicants, assess employees, forecast outcomes, detect fraud, and support decisions. In healthcare, finance, education, and government, AI is increasingly becoming an adviser.

Yet unlike a food label or medicine leaflet, many AI systems remain difficult for ordinary people to understand.

Transparency means being open about when AI is being used, what data it relies on, and its capabilities and limitations. Explainability means that when an AI system makes a recommendation or influences a decision, people can understand the reasoning behind it in language that makes sense.

Simply put:

If AI affects people, people deserve an explanation.

This becomes especially important when AI influences decisions about jobs, promotions, loans, insurance, healthcare, education, or access to opportunities.

Imagine applying for a job and receiving an automated rejection without knowing why. Imagine being denied a loan because of a risk score no one can explain. Or imagine an employee being classified as a low performer by an algorithm, yet neither the employee nor the manager understands how the assessment was made.

The issue is not simply whether AI was used.

The issue is whether people can understand and trust the process behind the decision.

As someone who has spent much of my professional life in human resources, talent analytics, and digital transformation, I find this especially important in the workplace. Organizations are rapidly adopting AI in recruitment, workforce planning, performance management, and talent development. These technologies can improve efficiency and help leaders process information at a scale that would otherwise be impossible.

But efficiency alone should never define success.

People are not data points. Careers are not algorithms. Human potential cannot be fully captured by a score generated by a machine.

When technology begins influencing livelihoods and futures, transparency becomes more than a technical feature. It becomes an ethical responsibility.

Trust is not created by sophisticated algorithms alone. It grows when people understand how decisions are made, when organizations are willing to explain their systems, and when individuals can question outcomes that affect them.

Transparency also benefits organizations. Employees are more likely to accept AI-assisted processes when they understand them. Customers are more willing to engage with AI-powered services when they know how their information is being used. Regulators, investors, and stakeholders gain confidence when organizations can explain and justify the technologies they deploy.

In this sense, transparency is not merely an ethical requirement. It is also a business advantage.

This is why responsibility belongs to everyone in the AI ecosystem.

For users, it means asking questions instead of accepting every AI-generated recommendation at face value. We need to understand when AI is being used, recognize its limitations, and verify important outputs.

For developers and designers, it means building understandable systems, documenting how they work, testing for unintended consequences, and communicating limitations honestly. Innovation should never come at the expense of clarity.

For leaders, it means establishing governance, ensuring meaningful human oversight, and creating environments where AI recommendations can be challenged. Most importantly, leaders must remember:

Accountability cannot be delegated to a machine.

When an AI-assisted decision causes harm, responsibility ultimately rests with the humans who designed, approved, deployed, and relied on the technology.

Ultimately, the future of AI will not be determined only by how powerful the technology becomes. It will also be determined by how responsibly we choose to use it.

The question before us, therefore, is not simply whether AI is intelligent enough.

The more important question is:

Is it trustworthy enough?

Whenever AI influences our decisions, organizations, or communities, we would do well to return to the challenge posed by the AAP Code of Ethics:

“Mapagkakatiwalaan ba ito?” (Can this be trusted?)

If we cannot understand it, explain it, or question it, perhaps we should not trust it just yet.

Because trustworthy AI is ultimately not about machines. It is about people—and our commitment to ensuring that technology remains transparent, accountable, and worthy of the trust we place in it. – Rappler.com

Liza Manalo-Mapagu is the CEO of ASEAMETRICS, Program Chair of the Psychology Department at Asia Pacific College, and a member of the Responsible AI and Analytics Council (RAAIC) of the Association of Analytics and AI of the Philippines (AAP). She advocates for Trustworthy and Human-Centric AI that advances both organizational performance and human dignity.

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