As artificial intelligence transforms public sector operations, questions of ethics and responsibility become increasingly critical. Tech strategist Swathi Young brings her expertise to the complex challenge of creating responsible AI solutions for federal agencies. With mounting concerns over AI applications in public services, her frameworks offer practical guidance for government leaders seeking to harness AI’s potential while protecting citizens from unintended consequences.
Five Keys to Building Ethical Government AI
Getting AI right in government isn’t just about having cool tech. Swathi cuts through the hype with practical advice. “When it comes to implementing ethical AI for the federal government or any public sector agency like state, local, municipal, et cetera, it’s very important to remember the frameworks of ethical AI,” she says. Her approach breaks down into five areas: bias, responsible use cases, interpretability, fairness, and algorithm interpretation.
Spotting Bias in Your Data
Every AI system starts with data. That’s where the problems begin. “Since AI is built on mostly historical data,” Swathi explains, “if you’re talking about machine learning, you use historical data to write predictive algorithms.” Same goes for the newer stuff: “If you talk about generative AI, you are using unstructured text and image data from across the internet.”
Here’s the problem. People create that data, and people have biases. Those biases don’t just disappear when you feed the data into an algorithm. “Humans create these elements and humans have biases. These biases seep into the data,” Swathi points out. She recommends getting your hands dirty with the data before trusting what comes out the other end. Look for warning signs. Run what-if scenarios. Question skewed results. And don’t ignore the gaps. “If there is no data present in some areas, you want to understand the backstory of why no data is present,” she advises. Sometimes what’s missing tells you more than what’s there.
Explaining AI Decisions Clearly
Nobody trusts what they can’t understand. That’s especially true when it comes to government decisions. Swathi calls this “interpretability” — a fancy way of saying AI should explain itself. “What it simply means is understanding the decisions that AI is making to the extent that it is possible for anybody to understand,” she says. You don’t need a math PhD. “We don’t need to know the math behind the algorithms, but most importantly, what are the parameters and drivers that make a decision?” Take criminal justice. If an algorithm helps decide whether someone gets bail, you better know why it made that call. “You want to understand what is driving that decision,” Swathi insists. “What are the parameters that went into making the decision?” Without those answers, you might as well flip a coin.
Disclosing AI in Public Services
AI is everywhere now. That’s fine, but don’t hide it. “As generative AI has exploded, more and more organizations are utilizing AI solutions. But it’s very important to be transparent about the use of AI,” says Swathi. Put your cards on the table. “If you’re using AI for recruitment, it’s important to declare to candidates that you are using AI,” she explains. Got a chatbot? “It’s important for customers to be aware that they are speaking to an AI-generated bot.” Nobody likes finding out they’ve been talking to a machine when they thought it was a person.
Choosing Responsible AI Use Cases
Not every government function needs AI. Swathi mentions responsible use cases as another key piece of the puzzle. Some applications just aren’t worth the risk. Before building anything, agencies should ask: Does this actually help people? Could it accidentally hurt vulnerable groups? Sometimes the answer is to walk away, no matter how cool the technology sounds.
Ensuring Fairness in AI Outcomes
Government serves everyone. That means AI systems need to be fair across the board. While Swathi doesn’t dig deep into fairness techniques in this talk, she includes it as a crucial element of her framework. It’s about making sure the algorithm treats people equitably, whether they’re rich or poor, urban or rural, or any other demographic difference. The stakes are high when it comes to government AI. Get it wrong, and real people suffer. Get it right, and public services improve for everyone. Swathi’s framework gives agencies a roadmap to stay on the right side of that line.
As federal agencies rush to adopt AI, Swathi’s framework offers a practical roadmap through ethical minefields. The responsibility falls on government leaders to ask hard questions about their AI systems before deploying them. Tools that make decisions affecting citizens’ lives deserve extra scrutiny. With bias lurking in data and algorithms that sometimes can’t explain themselves, caution isn’t just advisable, it’s essential. The government’s AI journey is just beginning. Getting the ethics right now will pay dividends later.
Follow Swathi Young on LinkedIn to explore her latest work in ethical AI for public impact.
