Over the past few weeks, we’ve been taking a look at the members of CUNY PIT Lab’s inaugural advisory board: six individuals brought together to help strengthen our capacity and continue to pursue our mission of making technology accessible, sustainable, and equitable for all.

This week, we spoke to renata gerecke. renata works on responsible technology adoption, with a focus on AI, at the NYC Office of Technology and Innovation. She produces NYC’s algorithmic tool registry, which is one of the most comprehensive and long-standing reports on public sector AI use. We spoke to renata about her path in the fields of data science, public policy, and govtech.

Can you give us a sense of your backstory and how you got interested in the work you’re doing now?

I was originally a math major in undergrad, but I had a lot of interest in public policy, even though I didn’t know to call it that. I didn’t really understand public policy as a field. I graduated from college, and I became a data scientist at a market research firm working with a lot of survey data, and also was witnessing this burgeoning data science and data analytics movement. I was interested in the growth of R, in particular as a coding language, and predictive analytics.

I loved the work that I was doing in market research, but I wanted to do more than sell a product, and wanted to work on things that had a more tangible impact and were addressing policy questions that I had been exposed to through extracurricular work I did in college. And so I ended up going back to NYU to get my Masters of Science in Public Policy from NYU Wagner, where I was really trying to learn about how that field was using quantitative methods to address policy issues. While I was doing that, I got a role at the Mayor’s Office of Operations working on vaccine rollout for COVID — this was in 2021. I was really interested in the ways that data and data science algorithms and nascent technology work as a part of city government and who was doing that work. One of the great things about interning is that you get to talk to a ton of people — and even though it was all remote, people were happy to talk to me, and New York City is really a bastion of folks who think about data in all kinds of different contexts. So I got to talk to folks who were on the information privacy team and who were really focused on thinking about personal data, how the city collects that information, how they store that information, and how they act as good stewards for that information.

And I also learned about algorithmic tools reporting, which is sort of at the nexus of everything that I was interested in without even knowing it existed. I started working full-time at the city in 2021, and it’s totally transformed with the introduction of generative AI tools. And it’s interesting: government by its nature is a slow adopter and broadly speaking, you can say a lot about the pains of bureaucracy, but I think that mostly it’s a good thing. It would be bad for public trust if the interface for your benefits platform was changing with every iteration of JavaScript. So in some ways, the antiquated nature of our technology can be helpful, because it needs to be stable, it has to work, and it has to work the same way every time. And that is not at all what AI tools that are currently on the market can promise! But there’s also a lot of potential. And so I spend a lot of time now thinking about how to help agencies… understand these tools and figure out how to use them in a way that improves access to services, or de-duplicates work, or creates meaningful automation, but does not compromise trust.

In your opinion, how do we “create the internet we want to see”?

Well, you have to define “what is the internet you want to see”! And people are going to disagree about what that is, because our needs are so diverse, right? I don’t know the answer to that question, but I will say that if we’re going to answer it, you need a really wide and invested group of stakeholders. To tie it back to the CUNY PIT Lab, you need people who are aware and excited about technology, who are coming together from all parts of the community. You need folks who are from every marginalized background. You need folks from different religious cultures, from different age groups, from different abilities — accessibility is a huge footnote on the internet sometimes. And getting more people excited about the potential of technology and focused on what they can build is more interesting to me than chasing away all the parts that are bad. And I think that’s a huge area of potential for the CUNY PIT Lab: harnessing that excitement and interest and giving people touchpoints with these new things, so that more people join that conversation.

Over the past few weeks, we’ve been taking a look at the members of CUNY PIT Lab’s inaugural advisory board: six individuals brought together to help strengthen our capacity and continue to pursue our mission of making technology accessible, sustainable, and equitable for all.

This week, we spoke to Ruby Justice Thelot, a designer, artist & cyberethnographer based in New York City. He is a professor of design and media studies at NYU whose work focuses on digital phenomenology, virtual ontology and the implications of being-on-line. He writes about virtual realms, digital communities, and artificial intelligence, and his work and research have been presented and published in journals, magazines, and conferences all over the world. He is the author of the books “A Cyberarchaeology of Checkpoints” and “A Few Essays of Taste” (Metalabel’s 2025 zine of the year). He is the founder of 13101401 inc,  a design and research studio.

In your lecture “Why Are There No New Aesthetics?”, you say: “To build new mass aesthetics we need a shared vision of the future and a philosophy.” So much of what eventually comes to dominate culture often starts in subcultural or countercultural forms. Do you think it’s important in itself to have “mass aesthetics”?

It is important, insofar as we believe that having a shared vision of the future is important for nation building. Now, what do I mean by this? Part of the underlying belief is that some monoliths are good. Some monoliths, culturally, allow us to share common cultural ground. They allow us to see what the nation will become, the path it can take, the future we want to build for our constituents, our children, our grandchildren. Today, you can kind of feel conflicting visions of the future. Before the lecture, the students had read this essay of mine called Mining the Future that I wrote for Art Forum, in which I describe how the monocultural events of a movie like Star Wars aligned a generation of future technologists into building some of the things that they saw in those movies. Famously, Mark Zuckerberg read Neal Stephenson’s Snow Crash; that became a vision of the future he wanted to build with his company, formerly known as Facebook, [which he] then called Meta for “Metaverse,” because that book, which was science fiction, led him to want to build that vision of the future. And so I think that political conversation is very important as we’re making these foundationally important decision as to what technologies we will build. Who gets to build it? Who gets to own it? Right now, we are at a pivotal moment with AI. And we’re faced with something that could be highly transformational. And the fact that we do not have a shared vision of the future, I think, is causing a lot of fraughtness around the technology, because we don’t know whether that technology will be used for curing cancer, or taking away jobs from my family who lives in Georgia. Is the future one of total automation, where the wealth is concentrated in the hands of a few tech oligarchs? Or is the future one where the spoils of this incredible, extraordinary technology do get not trickled down, but passed around to the whole of Americans. And that, in itself, is a decision as to what [kind of] future we want. And that future is promulgated, it’s disseminated, through mass aesthetics. There can be conflicting ones, and that’s always happened. But having that allows us to align and know what we are building for the future.

A recent focus of your work and discussions has been that of “slop,” and you also point towards “machinic taste”: “a phenomenon where digital content is increasingly shaped by non-human preferences rather than human desire.” Here’s a quote from you: “We are witnesses to a wonderful waltz between audience capture, algorithmic capture and machine capture.” What do you think are the negative or positive impacts this has on culture?

Well, the first one is this bit of a morbid curiosity: what do automated systems do when left on their own behalf? What are their aesthetic proclivities? And what do they make? And as I said in the essay, we’re seeing it with the slop. I think the reason why it’s so strange is because it’s not really meant for us. It’s kind of meant for us, because we’re still part of that “waltz,” but we’re slowly being eclipsed. And once we are truly eclipsed, then I’m genuinely curious to see what kind of weird, truly strange things we see. I think we learn a lot about the values of these systems as we see the kind of images they produce. It’s gonna reveal a lot of what [they] want to be rewarded by — I don’t want to say the word “want,” but… what they’re rewarded by as a part of their automated system.

The second thing is, as we stop relying on these recommendation systems, my sense is [that] there’s a reversal. Once you get to the point [where] you feel like you’re no longer seeing recommendations made by humans, then you return to your neighbor, you know? To me, there’s a hyper-locality that steps in. You start to take a look at your neighbor: you ask them, what’s their blender? Instead of going on a TikTok where maybe half of the likes and half of the comments are bots. But if Amelia likes her blender, maybe I’ll get the same one. And so we go back to what I’ve been calling a more tribal, more oral way of defining aesthetic affiliation or purchase decisions: all things that were very much influenced by the algorithm. As the algorithm gets further and further away from us, we need to find new modes of understanding the cultural space, and therefore we go back, in my opinion, to our neighbor. This is a process I call hyper-local global skepticism. The more things are on the platform, the more things are doubted: is this AI-generated, is this AI-influenced? Is this real, generally? Then the global, the thing that is macro, the thing that is big, gets doubted. That becomes a new epistemological route. That’s how we build knowledge, through tribal modes, oral modes, local modes.

Over the coming weeks, we’ll be taking a look at the members of CUNY PIT Lab’s inaugural advisory board: six individuals brought together to help strengthen our capacity and continue to pursue our mission of making technology accessible, sustainable, and equitable for all. 

This week, we spoke to Katie Bailey. Katie Bailey is Senior Director at HearstLab, where she works with founders, investors, and industry leaders to help scale startups addressing real-world challenges. Over the past decade, she has led initiatives spanning product, innovation, strategy, and business development across Hearst’s B2B software and venture businesses. A longtime advocate for technology as a force for positive impact, Katie is particularly interested in how technology, entrepreneurship, and human-centered design can improve outcomes for individuals and communities. She holds a degree in Urban & Environmental Planning from the University of Virginia and serves on the UVA School of Architecture Dean’s Advisory Board.

What is the background you’re bringing to this and what do you hope to bring to the board?

I work for a corporate venture team that invests in early stage technology startups. Obviously, when you approach technology with the lens of venture, it’s about making money and getting return on investment. What I have had the fortune of experiencing and seeing firsthand and proving out as a thesis is that you can do well and do good. So we have companies in our portfolio making millions and millions of dollars, but also fundamentally changing the foundation of local economies, of women’s health, of the future of infrastructure for the electric grid. And so I am especially excited to bring that business perspective, best practices, even bringing some of our portfolio companies to the forefront as real living examples of scalable business technology being applied toward making communities better.

Given the extremely dynamic and frequently disrupted state of tech, how can one best keep an eye to the horizon and try to anticipatethe unknowns, the corners that we’ve yet to turn?

I think that is the ultimate question. I think one thing that we try to stay true to our core about is: you know the technology is always going to be evolving and changing, but at the end of the day, I think it really comes down to two things. One being: what problem are you solving for, and is it a real problem? In our case: is it a problem that people are willing to pay to solve? And then second of all is, is this the right team to solve it? So we put a lot of emphasis on the founder, the founding team, the people behind the technology, just as much as the technology itself. A lot of people have access to AI, a lot of people can build apps these days, but if they don’t have the right sort of core expertise, if they don’t know about the industries they’re building for, they’re not going to get very far. And so I think those are two things we continue to double down on, even as technology modes are changing pretty rapidly.

AI is making a huge impact on work and on emerging careers. Do you see any industries that are potentially able to absorb these new technologies while stillremaining robust?

I think there are a few! One thing that we’re seeing that I think is actually quite interesting is the renaissance of “IRL.” There’s so much noise about technology — people are on screens and devices everywhere all the time. So we’re actually seeing a lot of interest and pullback to the human connection, and so I’m interested to see specifically how that plays out, especially for younger generations who have seen the older generations kind of mess that up. So I’m actually pretty optimistic that the answer for the future is somewhere in the middle of having technology as a partner to help accelerate good solutions, but at the end of the day, human judgment and humans still sort of in charge. I tend to lean towards a future vision where technology and humans actually work together instead of [being] replaced.