“Technology is neither good nor bad; nor is it neutral.” Melvin Kranzberg
The Algorithmic Gospel
Why belief needs boundaries
AI’s capabilities are growing faster than our collective ability to grasp their full consequences. Somewhere between the breathless headlines and the daily convenience of autocomplete, a quiet faith has formed a belief that AI’s verdicts are inherently correct, almost like divine decree. It’s tempting to trust a system that feels omniscient, but blind faith in any tool is a shortcut to complacency. Trust, when unearned, can be dangerous.
Kranzberg Perspective
Morality is in the hands that use it
Melvin Kranzberg was a renowned historian of technology, best known for formulating his Six Laws of Technology. The first of his laws being:
“Technology is neither good nor bad; nor is it neutral.” AI doesn’t have ethics coded into its DNA it’s shaped by the goals, values, and blind spots of the humans who build and train it.
This means AI’s impact isn’t preordained; it’s negotiated daily in design labs, data pipelines, and policy rooms. The algorithms are only as careful or careless as the choices we make. Kranzberg’s first law is less a warning and more a compass: it reminds us that our decisions, not the code itself, decide whether technology heals or harms.
A.I and the Resilience of Humanity
We’ve been here before
I am not inclined to downplay technological progress or declare ‘The end is nigh’ with each technological revolution. These revolutions have reshuffled the societal deck. Yet, as a species, we show remarkable resilience.
Every major innovation from the printing press to the personal computer has sparked both hype and dread. Each has upended industries, rewritten social norms, and triggered fierce debate. And every time, humanity has adapted. We have a knack for turning disruption into opportunity, eventually folding each tool into the everyday fabric of life.
AI will follow the same arc. It is not a harbinger of doom but a test of our adaptability and imagination. The question is not whether AI will change us, but how we will choose to change with it and whether we can guide it toward amplifying our humanity instead of eroding it.
A Call to Discernment
Don’t outsource your judgment
AI is only as good as the data it’s trained on and data, like people, carries baggage. Bias, gaps, and outdated information seep into models and shape their “truth.” The danger isn’t just at the corporate or government level; it’s in the everyday user treating AI’s answers as infallible.
Once AI starts replacing human decision-making in medicine, hiring, law enforcement, and news curation, blind trust becomes a liability. We shouldn’t fear AI like it’s the villain in a sci-fi thriller, but we also can’t treat it like an all-knowing oracle. Discernment the ability to ask, “Where did this answer come from?” might be our most important 21st-century skill.
Balancing Potential and Prudence
The knife, the fire, the code
Like fire or the printing press, AI is here to stay. The challenge is learning to wield it without getting burned. That means rejecting both extremes: starry-eyed acceptance and knee-jerk rejection.
The middle path is informed integration pairing innovation with vigilance. That includes educating the public on AI’s limitations, embedding ethical reviews into product cycles, and demanding transparency in how systems are trained. Policies must evolve alongside the tech, not lag years behind. If AI is going to be as embedded in our lives as electricity, then safeguards have to be just as invisible and just as reliable.
Insights and Inspiration
The dataset problem isn’t going away
A recent Wired investigation highlighted a persistent flaw in AI development: the reliance on scraped internet data. It’s cheap, fast, and full of problems from embedded bias to outright falsehoods. Cleaning that data is expensive and slow, which means companies often skip it, baking society’s prejudices right into the systems they deploy.
Researcher Abeba Birhane and others have called for bias mitigation to be a standard, non-negotiable step in AI development, not an afterthought. Without public pressure, Big Tech is unlikely to prioritize it over speed to market. If AI is to serve humanity broadly respecting rights, diversity, and dignity it must be trained on data that reflects those values, not undermines them.
Usage & Application
AI’s influence is already everywhere: in résumé screening software, predictive policing tools, medical diagnostics, education platforms, and creative workspaces. The practical steps differ depending on your role. Engineers need to build explainability into models. Educators should teach students to question machine-generated answers. Policymakers must draft regulations agile enough to keep up with innovation. And everyday users? They can start by making it a habit to verify not just accept what AI tells them.
The article by Wired magazine is a must-read for anyone using or interested in AI. It highlights the critical need for vigilance and responsibility in AI’s development and application. I’m pinning it below
Abeda Birhane : AI Datasets
Beyond the Algorithm
Further reading and watching
- Abeba Birhane – Scaling Generative AI Datasets Disproportionately Scales Racist Outputs, Especially Against Black Men : “Shows how biased datasets amplify racism in generative AI systems.”
- World Economic Forum – AI for Impact: The Role of Artificial Intelligence in Social Innovation : “Explores how AI can drive social innovation and tackle global challenges.”
- UNESCO – Recommendation on the Ethics of Artificial Intelligence : “Outlines global principles for ethical and human centered AI development.”
- Cathy O’Neil – Weapons of Math Destruction :”A landmark book exposing how big data algorithms create inequality and reinforce injustice.”
- Wired: How to Stop Your Data From Being Used to Train AI:”Practical advice on protecting personal information from being harvested to fuel AI models.”
Final Thoughts
Curiosity With Caution
AI will continue to grow smarter, faster, and more persuasive. Our challenge isn’t to slow it down but to keep ourselves from speeding past the point of accountability. Faith in technology isn’t wrong but faith without verification is risky. If we can pair our curiosity with caution, we have a shot at shaping an AI-powered future that reflects the best of us, not just the fastest version of us.
References:
- Melvin Kranzberg. Six Laws of Technology. 1985
- Abeba Birhane. The Dangers of Stochastic Parrots. 2021
- Wired Magazine. The Problem With AI’s Training Data. 2025
- Cathy O’Neil. Weapons of Math Destruction. 2016
