Fairness First: What 200 Strangers Taught Me About Responsible AI
When I signed up for a Coursera AI course, I expected to learn the nuts and bolts of Microsoft Azure, study for my AI-900 exam, and maybe trade a few polite introductions with classmates.
What I didn’t expect was to find myself in a global classroom of 2,000 strangers, all being asked the same deceptively simple question:
👉 Which principle of Responsible AI do you think is most important, and why?
Over two weeks, I scrolled through 200 responses. Some were essays. Some were one-word answers. Some were just noise — “ok,” “good,” “hello.” In fact, nearly 40% weren’t substantive at all. And that detail stuck with me. It made me wonder: why do so many learners check the box but not engage? Would this look different in an in-person seminar? That’s a question for another day — but one that matters deeply to those of us thinking about cross-modal belonging in higher education.
Still, in the 120+ posts that did engage, a clear pattern emerged.
📊 What the Data Said
From 200 responses, here’s how the six Responsible AI principles stacked up:
Frequency of Mentions
Fairness → ~55% (majority choice)
Accountability → ~20%
Transparency → ~10%
Privacy & Security → ~7%
Inclusiveness → ~5%
Reliability & Safety → ~3%
🌐 The Themes
Fairness as the Foundation
Learners weren’t abstract here — they named biased hiring algorithms, discriminatory lending, healthcare tools that misdiagnose darker skin tones. One wrote simply: “Without fairness, everything else falls short.”
For me, this resonated deeply. In higher ed and student affairs, fairness is the ground we stand on: access + belonging. If the system isn’t fair, nothing else matters.
Accountability as the Enforcer
The second-most named. Posts framed it as: “Without accountability, the others collapse.” Fairness only holds if someone is answerable when harm occurs.
As a dean, I used to say: “Our responsibility doesn’t end when the program ends. It ends when students are actually safe, supported, and whole.” These learners said the same of AI: responsibility isn’t theoretical. It’s traceable.
Transparency as the Bridge
Learners wrote: “If we don’t know how AI works, we can’t trust it.” Visibility makes accountability possible.Privacy & Security as the Everyday Anxiety
When it did come up, it carried urgency: “Will my data be safe?” That’s visceral, not theoretical.Inclusiveness as Aspiration
A few connected AI to cultural and global diversity. Not frequent, but heartfelt: AI should serve everyone, not just the privileged few.Reliability & Safety as the Quiet Guardian
Rarely mentioned, but when it was, it was about healthcare or finance. Learners seemed to assume reliability — until it breaks.
🧭 Why It Matters
What struck me most?
Learners didn’t rally around efficiency, accuracy, or speed — the things tech marketing usually leads with. They rallied around equity.
Even across languages, professions, and continents, fairness was the heartbeat.
And that gives me hope. Because whether in higher education or in AI, we’re wrestling with the same question:
Who gets left out, and what are we going to do about it?
Maybe, if fairness can be the foundation of AI, it can also help us build what we’ve always been striving for in higher ed: a future Wired for Belonging™.
📌 Limitations (because research brain)
Sample size: 200 responses out of 2,000 learners (~10%).
High rate (~40%) of non-substantive posts.
Not statistically representative, but qualitatively resonant.
Rich enough to notice patterns; limited enough to interpret with caution.
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