BBS:      TELESC.NET.BR
Assunto:  When machines remember us
De:       Mike Powell
Data:     Tue, 10 Feb 2026 17:16:36 -0500
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When machines remember us: Rethinking privacy in the age of humanoids

Opinion By Dr. Najwa Aaraj published 8 hours ago

How humanoid AI will redefine privacy, trust, and dignity

As policymakers race to regulate AI, a more intimate form of artificial
intelligence is emerging quietly, yet profoundly. The next revolution in
technology will not arrive as an app or an algorithm. It will walk toward us,
look us in the eye, and ask how it can help.

Humanoid robots are poised to leave the laboratory and step into our daily
lives: greeting guests in hotels, assisting patients in hospitals, tutoring
children, guiding us in malls, and eventually sharing our workplaces and homes.

Goldman Sachs forecasts that consumer sales will surpass one million units by
2035, a signal that this future is not speculative, but rapidly approaching. As
their forms become familiar, their presence will test one of humanity's
oldest instincts: the desire for privacy.

New era of trust

Until now, our digital existence has unfolded through screens and sensors we
could switch off. A phone slips into a pocket; a smart speaker rests quietly on
a shelf. But a humanoid is different. It observes, learns, reasons and acts
continuously.

It can read tone, posture, and emotion, capturing data far beyond what a
microphone or camera could record. In the age of humanoids, privacy will no
longer mean simply protecting what we say. It will mean defining what machines
are allowed to know about who we are.

This shift demands a new kind of trust. For decades, technology companies have
asked for our "consent" through lengthy forms and hidden clauses. Yet no
checkbox can capture the complexity of interacting with a learning, adaptive
robot.

When a humanoid helps an elderly patient stand, it must analyze posture,
predict balance, and detect hesitation. Every gesture produces intimate data.
But who owns those fleeting moments; the patient, the hospital, or the
robot's creator? And how can we ensure such data serves human dignity rather
than convenience alone?

Privacy preserving technologies

Existing privacy laws were built for files, not faces, for static storage, not
dynamic interaction. With humanoids, privacy becomes fluid, negotiated in real
time through movement, proximity and context.

Policymakers will need adaptive regulatory frameworks that evolve as quickly as
these systems do, incorporating continuous risk assessments and ethical design
principles from the very start. This is privacy by architecture, engineering
discretion so that it is not optional, but automatic.

At the core of this architecture lie cryptography and cryptographic protocols,
the science that makes privacy enforceable by design.

They enable humanoids to learn and respond to human needs without revealing the
underlying data. Rather than trusting that sensitive information won't be
misused, cryptographic techniques ensure it cannot be accessed in the first
place. This is the difference between policy promises and mathematical
guarantees.

In a world where humanoids continuously observe, interpret, and act, such
guarantees are essential. Encryption and privacy-preserving technologies can
transform ethical intentions into operational safeguards, anchoring trust in
the code itself.

Modern privacy engineering already offers tools for this vision. Techniques
such as federated learning, homomorphic encryption, and secure multi-party
computation allow AI systems to learn from local data without exposing it.

A humanoid can thus improve its assistance over time while keeping sensitive
information within its own encrypted domain. Privacy, in this sense, is not
just a social value, it is a scientific discipline advancing in parallel with
robotics.

Yet, the code behind humanoids must reflect more than just technical function,
it must embody social norms. In many cultures, cues like posture, gaze, and
proximity signal respect or intrusion.

Robots that move among us must be attuned not only to our privacy but to our
customs, boundaries, and emotional comfort. Trust will depend not just on what
machines can do, but on how gracefully and respectfully they do it.

If we embed privacy and dignity at the heart of humanoid systems, through both
code and conduct, these machines can help us reclaim control over data that
today flows unchecked through digital platforms.

A care humanoid can allow elderly individuals to live independently without
constant human oversight. A humanoid tutor can keep a child's learning data
safer than a cloud-based platform by processing it locally. The goal is not to
reject these technologies, but to guide them toward humane, transparent, and
ethical ends.

Respect, discretion and care

As a scientist and researcher, I see robotics as a mirror, reflecting not only
our engineering ambition but our ethical imagination. At the Technology
Innovation Institute, we are building physical artificial intelligence that
must engage with the world in all its complexity.

This means designing not only for function, but for respect, discretion, and
care. As we teach machines to perceive us, we are also redefining - with
intention - what it means to be truly seen.

The task before policymakers, scientists, and citizens is to move from reaction
to anticipation, to write the rules of coexistence before machines arrive at
our doorsteps. Privacy, once a personal concern, must now become a shared
design principle.

Humanoids are arriving at a defining moment for society. Their emergence will
test our ability to govern technology with foresight, ethics, and compassion.

If we succeed, we will build a future where physical artificial intelligence
safeguards, rather than sacrifices, human potential; proving that innovation
and integrity can coexist by design.


This article was produced as part of TechRadarPro's Expert Insights channel
where we feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those
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