Today, we’re thrilled to sit down with Donald Gainsborough, a political savant and the visionary leader behind Government Curated. With his deep expertise in policy and legislation, Donald offers unparalleled insight into the intersection of technology and governance. In this conversation, we dive into innovative proposals like using AI to streamline hiring processes in major cities, as well as the complexities of digital privacy policies surrounding web technologies such as cookies. Join us as we explore how these advancements could reshape public administration and user experiences online.
How does the idea of using AI to accelerate hiring in a city as large as New York resonate with you, and what potential benefits do you see in this approach?
I think it’s a bold and necessary move for a city like New York, where bureaucratic processes can often slow down critical functions like hiring. AI has the potential to analyze vast amounts of data quickly, match candidates to roles based on skills and qualifications, and cut through the red tape that often delays onboarding. The benefit here is efficiency—getting the right people into critical public sector roles faster can improve city services, from sanitation to public safety. If implemented thoughtfully, it could also reduce human error and streamline repetitive tasks like resume screening.
What specific aspects of the hiring process do you think AI could transform most effectively in a municipal setting?
AI could really shine in areas like initial candidate screening and job matching. For instance, it can sift through thousands of applications in a fraction of the time it takes a human recruiter, identifying those who meet baseline qualifications. It could also help standardize evaluations by focusing on objective criteria, which might reduce inconsistencies across departments. Additionally, AI tools can manage scheduling interviews or follow-ups, freeing up staff to focus on more nuanced tasks like cultural fit or in-person assessments.
What are some of the biggest hurdles you foresee in integrating AI into a public hiring system like this?
One major hurdle is ensuring fairness. AI systems are only as good as the data they’re trained on, and if that data reflects past biases—say, in hiring demographics—it could perpetuate inequities. There’s also the challenge of transparency; applicants and employees need to understand how decisions are being made. On top of that, you’ve got logistical issues like integrating AI with existing systems and training staff to use it effectively. Public trust is another big one—people might worry about a machine deciding their career fate without a human touch.
How can a city address concerns about bias or unfairness in AI-driven hiring decisions?
It starts with rigorous auditing of the AI tools before and during their use. Cities need to partner with experts to test these systems for bias and ensure they’re trained on diverse, representative data. Transparency is key—publicly explaining how the AI works and what criteria it uses can build trust. There should also be an appeals process where candidates can challenge decisions and get human review. Regular updates to the algorithms to correct any flagged issues would be critical, as would involving community stakeholders in the oversight process.
Shifting gears to digital privacy, can you break down the different types of cookies used on websites and their purposes in enhancing or monitoring user experience?
Absolutely. Cookies are small data files websites store on your device to remember things about you. Strictly Necessary Cookies are the backbone—they make the site work, like remembering your login or privacy settings. You can’t opt out because without them, basic functions break. Functional Cookies enhance usability, storing preferences like language or layout so the site feels tailored to you. Performance Cookies track how the site is doing, collecting anonymous data on page load times or user navigation to help developers improve speed and design. Then there are Targeting and Social Media Cookies, which personalize ads and content based on your behavior, often sharing data with third parties for marketing.
For cookies tied to personal data and advertising, how does opting out impact what users see online, and what are the limitations of that choice?
Opting out of Targeting or Social Media Cookies means you’re limiting how much of your data is used for personalized ads. You’ll still see advertisements, but they won’t be as tailored to your interests or browsing history. The limitation is that this choice often only applies to the specific browser and device you’re using at that moment. Since tracking isn’t typically synced across different devices or platforms, you might need to opt out repeatedly on each one. Plus, some underlying data collection for site performance might still occur—it’s just not used for ad personalization.
What’s your forecast for the future of AI in public sector hiring over the next decade?
I believe we’re on the cusp of a major shift where AI becomes a standard tool in public sector hiring, not just in big cities but across all levels of government. Over the next ten years, I expect we’ll see more sophisticated algorithms that can handle complex roles while addressing bias more effectively through better data practices. There will likely be a push for federal guidelines to standardize ethical AI use in hiring, ensuring consistency and fairness. But the real game-changer will be public acceptance—if trust is built through transparency and proven results, AI could redefine how governments build their workforce, making them more agile and responsive to community needs.
