As artificial intelligence spreads into nearly every aspect of our lives—from classrooms to clinics to courtrooms—we keep hearing the same promise: AI will make life better for people. But what does better really mean? Is it faster service? Smarter decisions? More convenience? And who gets to decide?
This isn’t just a philosophical question—it’s a practical one. Because if “better” isn’t defined by the people AI is meant to serve, it will be defined by someone else. Probably quietly. Probably in code.
Better for Whom?
Let’s start with the obvious: what’s better for one person might be disruptive—or even harmful—for another.
- A hospital may use AI to optimize surgical schedules, but if the model deprioritizes rural patients or those without insurance, is that truly better?
- An education platform might use AI to recommend learning paths, but if those algorithms reinforce existing biases—labeling one student as “low potential” without room for growth—is that helpful or harmful?
- A retail company might use AI to automate customer support, but if that means frustrated customers can no longer speak to a real person, what’s the trade-off?
When we say “make life better,” we must ask: better for whom? And at what cost?
Why Humans Must Be at the Center of Defining “Better”
Technology doesn’t define human values—we do. But if we let AI systems evolve without clear ethical guidance, they will default to what they were trained on: efficiency, speed, and patterns from past data. That’s not enough.
What’s missing in many AI development pipelines is a sustained commitment to human-centered design, privacy protections, copyright transparency, and ongoing ethical reflection.
What “Better” Looks Like—In the Real World
In the Classroom
AI can support teachers by identifying struggling students, generating lesson plans, or offering real-time translations. But:
- Access must be equitable—not just available to wealthier districts.
- Human oversight is essential—students shouldn’t be locked into algorithmic tracks.
- Bias must be actively challenged—to prevent reinforcing systemic inequities.
In Healthcare
AI can accelerate diagnoses and support mental health triage. But:
- It must respect cultural context and patient dignity.
- Doctors must retain authority to override flawed outputs.
- Data use must comply with privacy regulations and ethical consent.
In Public Services
Governments are using AI for benefits eligibility, public safety tools, and more. But:
- Transparency is key—people need to know how decisions are made.
- Appeal systems must exist for those affected by automated decisions.
- Oversight must be public and accountable—not outsourced to black-box systems.
When “Better” Means Job Loss: The Tension Behind Automation
One of the most common fears surrounding AI isn’t science fiction—it’s job displacement. For many workers, especially in service, logistics, and administrative roles, the idea that AI is making things “better” can feel hollow when it comes at the cost of a paycheck.
Companies often frame automation as innovation. But to the person whose role has been replaced by a chatbot or a machine learning model, “better” doesn’t always feel like progress. It feels like being left behind.
Even tech leaders are sounding the alarm. In April 2025, the CEO of Fiverr warned that generative AI is already displacing workers—especially freelancers—at a rate faster than anticipated. He emphasized that platforms must take responsibility in preparing workers for these shifts, or risk widespread economic consequences.
According to a 2023 Pew Research study, over half of American workers familiar with AI say they are concerned about its impact on their jobs. And while some roles may evolve or be replaced by new ones, those transitions often come with major barriers—like upskilling, relocation, or lack of access to education.
If we’re going to say AI is making work “better,” we have to ask: Better for whom? The company’s bottom line—or the people doing the work?
Real “better” would mean policies that ensure workers aren’t just casualties of progress, but participants in it. That includes:
- Transparent automation plans
- Reskilling programs and access to continued education
- Worker-centered design of AI tools
- Public and private sector partnerships to ensure equitable job transitions
Ignoring the human impact of automation undercuts trust in AI. Centering people in the conversation doesn’t slow progress—it ensures it’s shared.
Who’s Defining “Better”? Institutions Shaping AI Policy
Thankfully, many organizations are working to guide the ethical use of AI and set clear guardrails. These are some of the most active voices:
- UNESCO – Ethics of AI: Global standard promoting dignity, inclusion, and sustainability in AI.
- European Union – AI Act: A risk-based legal framework for AI with bans, audits, and transparency requirements.
- NIST – AI Risk Management Framework: U.S. standard for trustworthy, accountable AI.
- White House: Shaping the use of AI in schools
- FTC – AI Oversight: Enforcement body cracking down on bias and deceptive AI practices.
- Congressional AI Caucus: Bipartisan group shaping U.S. AI policy on safety, jobs, and rights.
- Partnership on AI: Nonprofit alliance of tech firms, academics, and NGOs focused on AI’s societal impact.
- IEEE Global Ethics Initiative: Engineering-centered effort to embed ethics into AI product design.
- AI Now Institute (NYU): Research hub exploring power, labor, and equity in AI deployment.
Most of these groups raise consistent concerns around data privacy, copyright infringement in AI training, algorithmic discrimination, and the lack of inclusive design in mainstream AI systems.
Redefining “Better” Together
We must:
- Invite diverse voices to shape what gets built—from the start.
- Embed ethics into every stage of development—not as an afterthought.
- Build local frameworks that reflect community needs and values.
- Teach AI literacy so the public can participate—not just consume.
Texans, especially, have an opportunity here. With our growing tech sector and strong educational institutions, we can lead the way in defining what ethical, human-first AI looks like. Not just in labs or venture pitches—but in classrooms, clinics, courtrooms, and city halls.
Final Thought
“Better” isn’t just a metric—it’s a mirror. It reflects what we value, who we prioritize, and how we imagine the future.
Let’s stop assuming we all mean the same thing when we say AI will make life better. And let’s start asking: Better for whom? Better by whose definition? Better with whose input?
Because if we don’t define “better” with purpose, someone else will define it for us—with code we didn’t write and systems we don’t control.
