Imagine you’re walking through your neighborhood and a four-foot-tall robot walks up beside you. It greets you by name, remembers your favorite coffee order, and offers to carry your groceries. You’ve never seen it before. Should you trust it?
That question isn’t science fiction anymore. Machines are getting smart. Large language models (LLMs) already contain vast amounts of information. They know about the physical world, human behaviors, our history, the nature of human jobs, and the behaviors of our pets. This stored information allows LLMs and other AIs to write books, make us laugh, fix computer code, get perfect scores on medical licensing exams, and file our taxes. LLMs, when given a physical body, are starting to autonomously navigate cities and hospitals, can open doors and get into robotic cars, hold conversations, and learn about the humans around them. Our generation is watching machines wake up. Robots aren’t just inert piles of plastic and metal anymore, but are growing into teachers, co-workers, and health companions. Some humans cry when familiar robots receive LLM or privacy upgrades that change their personality. Soldiers have tried to help robot team members to safety, despite it being (rationally) clear that machines can be fixed or replaced.
The main challenge is how fast AI is improving. People have spent thousands of years developing systems for vetting and reputation. You trust your Uber driver because you can see their rating and ride history. Your family doctor (hopefully) has performed hundreds of successful procedures over years of training. You might trust a teacher because your school district hired them, presumably after extensive vetting. None of this exists yet for robots. A robot in your home or office could be a marvel or a liability.
The stakes are higher than a buggy app or a hacked email account. We all understand how catastrophic a major cyberattack can be — banks closed, infrastructure disabled, sensitive data stolen. A compromised household robot could be misused from anywhere in the world, such as to remotely open your front door from inside your home. An autonomous delivery bot could be repurposed to harm its recipient. When the software that can already manipulate our digital systems gains the ability to act in the physical world, the potential for harm includes real-world injury and risk.
The importance of transparency
At OpenMind, we think that part of the answer is transparency. The robots we build and the software they run are open source. You don’t have to take my word for what’s inside — you can read the code. Beyond open software, when our robots boot, they download immutable guardrails like Asimov’s Laws of Robotics from the Ethereum blockchain. That way, their rules aren’t hidden in a private database. The rules are public, verifiable, and tamper-resistant. It’s the robot equivalent of knowing that all Uber drivers have agreed to the same rules of conduct, and the same rules of the road. Why go to those lengths?
Many of the environments where human-facing universal robots can provide benefits — homes, hospitals, schools — are sensitive and personal. A tutoring robot helping your kids with math should have a track record of safe and productive sessions. An elder-care assistant needs a verifiable history of respectful, competent service. A delivery robot approaching your front door should be as predictable and trustworthy as your favorite mail carrier. Without trust, adoption will never take place, or quickly stall.
Trust is built gradually and also reflects common understanding. We design our systems to be explainable: multiple AI modules talk to each other in plain language, and we log their thinking so humans can audit decisions. If a robot makes a mistake — drops the tomato instead of placing it on the counter — you should be able to ask why and get an answer you can understand.
Over time, as more robots connect and share skills, trust will depend on the network too. We learn from peers, and machines will learn from us and from other machines. That’s powerful but just like parents are concerned about what their kids learn on the web, we need good ways to audit and align skill exchange for robots.. Governance for human–machine societies isn’t optional; it’s fundamental infrastructure.
So, how do you trust a robot you’ve never met? With verification and reputation systems we use for humans – but adapted for machines. Public rules, explainable decisions, standards that are visible, enforceable, and human-first. Only then can we get to the future we actually want: one where robots are trusted teammates in the places that matter.
(For readers unfamiliar: Isaac Asimov’s Three Laws of Robotics — first introduced in 1942 — state that a robot may not harm a human or, through inaction, allow a human to come to harm; must obey human orders unless those orders conflict with the first law; and must protect its own existence so long as that protection does not conflict with the first or second laws.)
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