Can AI Detect Lies?

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Welcome Back to XcessAI

Hello AI enthusiasts and business leaders,

Last week, we attended a private event where industry professionals and investors discussed AI’s impact on business. The conversation quickly evolved into some thought-provoking themes. One topic created quite a buzz around the table: Can AI detect lies in corporate communication?

It wasn’t just a hypothetical. People wanted to know: can large language models understand intent? Can they read between the lines? Can they spot the subtle red flags in earnings calls, press releases, or management commentary?

It sparked such a rich discussion that went beyond finance. So we decided to explore it further this week. Ultimately, this isn’t just a finance question. It’s a language question. A trust question. And increasingly, an AI question.

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The Subtle Power of Language

Whether it’s a CEO on an earnings call, a politician at a podium, or a manager giving feedback, words matter.

We’ve long known that language carries more than information. It carries tone, confidence, avoidance, and sometimes deception. Human communication is filled with subtle cues: hesitations, euphemisms, and over-clarifications that hint at what’s not being said.

The problem? It’s easy to miss those signals.
Humans are biased. Tired. Distracted. And we’re often more charitable than analytical.

Why AI Might Actually Be Better at This Than Us

Large Language Models (LLMs) are trained on enormous amounts of text—books, articles, speeches, earnings calls, Reddit debates. This makes them deeply familiar with how people tend to express truth… and how they tend to avoid it.

This gives rise to a fascinating possibility: Can an LLM be trained not just to summarize what someone said, but to infer what they’re avoiding?

We’re already seeing early applications:

• Earnings call sentiment analysis that goes beyond “positive” or “negative”

• PR language flagging to detect overused corporate euphemisms

• Communication coaching that shows managers how their language might undermine clarity or trust

These tools aren’t reading minds. But they are becoming very good at analysing patterns that signal discomfort, evasion, or manipulation.

And unlike humans, AI can do this at scale, without bias, and in real-time.

But Can AI Really Detect Lies?

At this stage, we believe AI can’t detect a lie the way a polygraph claims to. It can’t know a speaker’s intention or verify a hidden truth.

What it can do is flag linguistic patterns that deviate from expected norms, and that historically correlate with deceptive or evasive behaviour.

Think of it like a radar:

• Not saying “we are confident” when it’s usually present

• Using vague language when specifics are expected

• Overusing qualifiers like “we believe,” “we anticipate,” or “we hope”

• Avoiding first-person pronouns (“the company” vs. “we”) in moments of accountability

When these patterns stack up, AI can flag them - not as proof of dishonesty, but as a reason to look more closely.

Additionally, when combined with real-time data and newsflow, AI might even detect when certain claims are objectively inconsistent with reality.

For instance, if a public statement claims rising sales while multiple independent sources report a decline, AI might flag this as a potential inconsistency worth further investigation.

Bottom line: AI is opening new possibilities for analysing and enhancing corporate communication.

Applications Beyond Finance

This isn’t just useful for finance professionals. AI language analysis has implications across multiple industries:

• Legal – reviewing deposition transcripts or court testimony for evasiveness

• HR – identifying passive-aggressive or gaslighting behaviour in internal comms

• Media & PR – analysing public figures’ statements for rhetorical manipulation

• Education – helping students learn how to craft clearer, more accountable arguments

In every domain where clarity, intent, and honesty matter, AI can become a powerful second set of eyes.

Implications for Leaders

If you’re a business leader, this evolution raises important questions:

1. How transparent is your language?
   AI tools may soon evaluate not just what you say, but how you say it.

2. Are you coaching your team to communicate with clarity and accountability?
   Ambiguous language may no longer fly under the radar.

3. Are you prepared for your public statements to be machine-analysed in real time?
   Stakeholders may soon be reading AI summaries of your tone and framing—alongside your transcript.

Final Thoughts: Truth in the Age of Machines

AI isn’t going to replace trust. But it might start to audit it.

As language models evolve, we’ll gain new tools to explore how people speak, not just for what’s said, but for what’s implied, avoided, or dressed up in ambiguity.

That creates both new pressure and new opportunity for leaders, communicators, and educators to focus not just on messaging, but on meaning.

In a world where machines are listening, maybe we’ll all speak a little more clearly.

Until next time,
Stay curious and keep exploring the future of AI.

Fabio Lopes
XcessAI

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