Ethics in AI

James Mitchell

Hey there, and welcome back to Syntax Error. I’m James Mitchell, and today, we’re doing something I’ve been itching to have a real talk about — the ethics of AI. If you caught our first episode, you know we geeked out about how AI tools and platforms are making audio creation wild and accessible. But today, we’re flipping that lens. It’s not all about what’s possible, but what’s actually right.

James Mitchell

Let’s kick things off with privacy, which might just be the thorniest thread in this AI tapestry. If you live in a city, you’ve probably walked right by a facial recognition camera without blinking — these systems are everywhere, used in public spaces, airports, stadiums, and who-knows-where else. And it’s not just an overseas thing; companies in the U.S. are hungry for data, scraping everything from your internet searches to, like, your driving habits. True story: General Motors actually sold driving data — things like trip lengths and speed — to brokers who then used it to set people’s insurance premiums. Wild, right? Makes you think if your next road trip playlist is the only thing tracking you.

James Mitchell

Here’s the dilemma: AI needs insane amounts of data to function well, but that means companies have these huge incentives to collect and hold onto information about you and me. And anonymization? I mean, in theory it keeps things private, but in practice, these algorithms can sometimes stitch together anonymous datasets and still figure out it’s, well, you. Watch out for the predictive analytics firms quietly figuring out “patterns of life” from your phone movements or purchase histories.

James Mitchell

Of course, regulators are scrambling to catch up. I gotta mention the European Union’s GDPR — if you haven’t heard of it yet, it’s basically the gold standard for data privacy, giving residents serious control over their personal info and even the right to opt out of certain automated decisions. On top of that, the new AI Act in the EU bans some of the nastiest uses of AI, like predictive policing and emotion recognition at work or in schools. Meanwhile, in the U.S., it’s a patchwork. California’s CCPA gives folks some power, but we’re still missing that one big federal law. We got a White House Executive Order and guidelines like the AI Bill of Rights, but honestly, as a nation, it feels like we’re in beta mode for privacy.

James Mitchell

I once worked with a retail client trying to integrate an AI-powered customer support system. They were excited — and who wouldn’t be? But the second we started designing it, we hit a wall with privacy requirements. Balancing great support with the responsibility not to over-collect customer data was like walking a tightrope. We had to choose which data the system could access, and more importantly, what it couldn’t ever see. GDPR compliance demanded we build “privacy by design” — making sure the system defaulted to using only what was essential. It slowed things down, sure, but at the end of the day, trust trumps a quick rollout. And let’s be real, you don’t want to be the company known for leaking folks’ home addresses because your chatbot got too nosy.

James Mitchell

So, as AI gets more and more a part of our daily lives, that baseline of privacy — well, it’s gotta be more than just fine print in a user agreement. And right now, we’re still not quite there, especially stateside. But let’s move from just data to what happens when the systems themselves aren’t as neutral as we expect...

James Mitchell

Alright, so let’s talk bias — and no, not just the unconscious kind we bring to dinner tables. AI systems are famous, or maybe infamous, for amplifying existing biases in data. You’ve probably heard about Amazon’s hiring algorithm. The system, trained on resumes from mostly male engineers, decided that — guess what — men were more likely to be good hires. Ended up downgrading female candidates before they even had a chance. That wasn’t some evil AI overlord; it’s just the data reflecting old patterns right back at us.

James Mitchell

Same thing with facial recognition systems. Studies found they work way worse for people with darker skin tones — and that’s not a bug, that’s the system learning from skewed training sets. And the fallout isn’t just embarrassing for the company; it can mean people getting misidentified, or denied opportunities, or flagged for stuff they didn’t do. We’re not just automating jobs here — we might be automating discrimination.

James Mitchell

So where does this bias creep in? Honestly, almost every step: which data gets used, how it’s labeled, even the design choices. Often teams aren’t diverse enough to spot blind spots. I mean, most engineers don’t wake up and say “let’s build an unfair system today”—it just…happens if nobody’s actively looking out for it.

James Mitchell

Now, here’s the million-dollar question: Can tech actually fix bias, or is this a bigger societal thing? You can build tools to test for bias or add layers like differential privacy, but as Gary Marcus pointed out in his Senate testimony, these systems are like bulls in a china shop — powerful but not predictable. We don’t fully know how they’ll behave. Even their creators can’t always explain the outcomes, and that’s a problem.

James Mitchell

In my experience, you need more than just tech fixes. I might be wrong, but if the data itself is broken, or if the outcomes reinforce inequities, it takes a lot more than an audit or a dashboard to course-correct. Some companies are starting to take this seriously, setting up regular bias assessments, or bringing in outside experts to poke holes in their logic before anything gets shipped. But it’s a messy, slow process. And there’s always the temptation to launch first, worry later—which is risky.

James Mitchell

Ultimately, combating bias means changing the culture of how we build these systems, not just patching bad code. This is where real accountability kicks in — and yeah, it isn’t just up to coders or even just the companies. Regulators, independent scientists, the whole ecosystem’s gotta be involved. And that takes us to a pretty natural next stop: how do we actually hold anyone responsible when things go sideways?

James Mitchell

So, on to accountability — the topic everyone loves to say they care about, but, man, getting commitments in writing is a whole other story. Right now, there’s a mountain of “AI principles” out there: Google’s AI Principles, for instance, promise things like fairness, privacy, and user benefit. Or the OECD AI Guidelines endorsed globally. These are steps in the right direction, but without teeth — meaning, if someone breaks these, what actually happens?

James Mitchell

The U.S. White House at least put out the Blueprint for an AI Bill of Rights, which calls for things like data minimization and meaningful user consent — and that’s echoed by the new UK regulatory body aiming for sector-wide accountability. But according to IT Pro’s research, more than half of companies have either very limited governance or none at all over their AI. So yeah, there’s a will, but not always a way.

James Mitchell

I’ve seen good examples at the startup level, though. There was this fintech team I advised — they decided not to wait for governments to tell them what to do and set up their own ethics board. Every time they rolled out a new credit risk algorithm, it went through a review to check for bias and fairness. They invited outside experts, not just their own people, to try to break the model. And I gotta say, it made their product stronger, because the intent wasn’t just to tick a compliance box, but to actually do right by their customers.

James Mitchell

Gary Marcus brought up something I keep going back to: independent oversight is crucial. We can’t just “trust Big Tech” to police itself — incentives are out of whack. The sums of money are huge, and, let’s be honest, nobody wants to be the company that slowed down innovation, right? But if independent scientists, governments, and maybe even a neutral international body could audit these systems before they hit the market, we’d have a shot at real safety and transparency.

James Mitchell

Look, none of this is moving slowly. AI’s evolving way faster than the regulation. If we’re not careful, we’re gonna lock in mistakes that’ll last for decades — maybe more. But that’s why talking through these tough issues actually matters. So, thanks for letting me nerd out with you about AI ethics today. I’ll wrap up here, but trust me, this is just the start of a bigger conversation. Next time, we’ll get into who’s building the checks and balances for these wild AI systems — and what’s still missing. Don’t miss it.