Trust the Data – But Not Blindly

Your point is well-articulated, and I agree that separating raw data from the meaning we assign to it is essential for sound decision-making. Numbers without context can be misleading, and context without careful interpretation can be equally dangerous. The example of “5” is particularly effective, it illustrates how the same value can signal very different things depending on what it measures, how it was derived, and the broader situation it sits in. This principle is especially important when working with AI-generated insights, because while AI can process vast amounts of data, it doesn’t inherently understand meaning, it detects patterns and correlations, but we as humans must determine whether those patterns are relevant, valuable, or even valid. Your mention of statistics is also spot on. Averages, percentages, and counts can easily obscure the underlying variability or the causal factors that really matter. The “why” behind the number is often more important than the number itself. In practice, this means we should treat AI outputs and statistical summaries as starting points, not final truths. They should prompt further questioning: What does this measure? Why was it measured this way? What’s missing from the picture? In other words, trust the data’s accuracy, but challenge its relevance, completeness, and interpretation before drawing conclusions or acting on it.

I’d challenge the AI’s recommendation because metrics can miss context. In one case, our CRM flagged a sales lead as “low performing,” but a deeper review showed they were negotiating a multi-million contract, work not logged in the system. Removing them would have risked revenue and client trust.

To keep decisions fair, I pair AI insights with human review panels, document the rationale, and update tracking systems to capture qualitative contributions. This way, we leverage data without losing the human judgment that protects both ethics and results.

You’ve raised some important distinctions here, and I appreciate the emphasis on separating data from information. I agree that raw data in isolation is not inherently trustworthy, it needs to be validated, contextualized, and often statistically analyzed before it can support informed decision-making. Your point about AI outputs being probabilistic is also key. Many people still assume AI delivers “answers” rather than probabilistic predictions, and that misunderstanding can lead to misplaced trust in the system. The “human in the loop” principle is indeed central to Responsible AI, ensuring that judgment, domain expertise, and contextual awareness guide final decisions. Where I might expand on your comment is in the relationship between responsibility and ethics. Responsible AI certainly includes processes for validation, governance, and oversight but ethics underpins why we adopt those safeguards in the first place. It’s about ensuring AI decisions align with organizational values, protect fairness, and avoid harm, not just about technical correctness. In other words, ethics is a dimension within Responsible AI, not separate from it. So I think we’re aligned in principle data must become meaningful information, AI outputs must be critically evaluated, and humans must remain accountable. The conversation then becomes how to embed both technical responsibility and ethical intention into AI-enabled decision-making.

Thanks for your time. What you mentioned, technical responsibility and ethical intention, must be part of Responsible AI. Just to comment, I am in charge of that in the greatest consulting firm in the world which is working and selling this type of things. My comment is just to pointed out I am "living" this type of things, no matter I am working with AI from 1989. The decision making is on hands on the human being, not in hands of AI. So, when the human being has the information created by the AI, which is always probabilistic then the human being will ever get values with probability associated to them, she/he must take the decision.

You’ve made an excellent point; data without context can easily mislead. For example, a company like Netflix once saw a spike in user sign-ups during a hit show release. The raw number looked great, but deeper analysis showed most new users canceled after the free trial. The “5% growth” in subscribers wasn’t sustainable; it was a short-term event.

Similarly, in 2023, Microsoft reported a 7% drop in PC sales. Without context, that sounds worrying. But when examined closely, the drop was due to a post-pandemic market correction, not a long-term decline in demand for their products.

AI is similar; it can detect patterns, but it doesn’t know which ones matter. That’s why human reasoning is critical. Statistics like averages can hide key details; for instance, if Amazon’s average delivery time is “2 days,” it doesn’t reveal that rural areas might wait 5 days while city deliveries arrive the next day.

In short, numbers are starting points. The right question isn’t just “what is the number?” but “why is it this number, and what does it truly mean for decision-making?”

Hello Shenila,
Thank you for the thought-provoking reflection.

I came across an article entitled "Why Writing Must Remain Central to Human Knowledge in Higher Education." The author discusses the potential loss of human knowledge that may arise from the growing use of AI applications. He points out that writing has been an essential tool for individuals to record their knowledge and deepen their understanding, in particular, in developing skills such as reasoning and critical thinking. But, he expresses concerns that our growing dependence on AI tools in education could jeopardize these vital skills.

There is a parallel between the issue of (un)trusting data and the use of AI. In both cases, we need to apply judgment, context, and critical thinking to ensure that the data supports sound decisions. Ultimately, human insight is what transforms information into meaningful knowledge.

Here is the link to the article:
https://giemmolo.substack.com/p/outsourcin...hy-writing-must

"Students can now produce fluent academic prose in seconds using AI tools. What once required hours of wrestling with ideas, organizing knowledge, testing understanding through expression, discovering gaps in reasoning can now be bypassed entirely. Yet the cost extends far beyond individual skill development to something more fundamental: the erosion of knowledge itself. For millennia, humans have used writing not just to record what they know, but to discover what they understand. The struggle to articulate complex ideas reveals the boundaries of comprehension, forces integration of disparate concepts, and builds the rich knowledge networks that define expertise. Writing has been the medium in which raw information transforms into understanding."

Maria

In my view, an ethical project leader’s responsibility begins with remembering that AI is a decision-support tool — not a decision-maker.
In this example, the dashboard’s “underperforming” flag is based on a limited set of tracked metrics. The ethical risk comes when those metrics ignore high-value work that isn’t system-captured, like safeguarding millions in client revenue through negotiations.
From my operations and process improvement experience, I’ve learned to approach such conflicts in three steps:
1️⃣ Interrogate the metric – Before acting, validate what’s being measured and what’s missing. Data without scope context is incomplete.
2️⃣ Balance quant and qual – Combine tracked data with qualitative evidence from direct observation, peer input, and stakeholder feedback.
3️⃣ Document the rationale – If you override an AI recommendation, record why, so the decision is transparent and can help refine the system.
The outcome should protect both fairness to individuals and trust in the measurement system. If our people believe they’re judged on an incomplete picture, we risk disengagement.

AI can make us faster, but it’s our human judgment — grounded in ethics and organizational values — that ensures we’re also making the right calls.

Right on point. I agree perfectly with you..