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Building Trust in Data Science: Interdisciplinary Insights from Rohan Alexander

Join Associate Professor Rohan Alexander from the University of Toronto as he explores the intersection of economic history, data science, and artificial intelligence.

In this engaging talk, Rohan shares his interdisciplinary journey, collaborative research efforts, and the challenges of building trustworthy, reproducible workflows. From sparse matrices to the evolving role of large language models, he reflects on how AI is reshaping coding practices and the broader implications for society. Whether you’re a researcher, technologist, or simply curious about the future of data science, this conversation offers thoughtful insights into where we stand on the AI frontier – and where we might be headed.

Transcipt

“My background is in economic history, and I currently hold an appointment in both the Faculty of Information and the Statistics Department, which makes my work highly interdisciplinary. That was one of the main attractions of coming to Sydney – the strong culture of interdisciplinary collaboration here.

I think CrowdTangle did a good job in the labor space, and there was a lot of overlap with Francesco’s interests. As a result, we’ve been working together to apply for grants, conduct joint research.

For a long time, we’ve been writing code and putting it online, which is great. But we’ve come to realise that doing so can lead to errors and mistakes.

I’m really interested in how we can build trustworthy data science. How can we develop reproducible workflows that allow us to trust our analytical results? That involves improving our coding practices, writing tests, and taking advantage of advances in computer science.

These days, it’s all about AI and large language models, which we’re exploring to help improve our coding. Right now, I see it as an emerging technology, and we’re trying to figure out its appropriate role in data science. Can AI actually perform autonomous analysis of a project? And beyond data science, what is its appropriate role in society more broadly?

Ultimately, we’re trying to understand: where are we on the AI frontier, and what does that boundary look like?”

Image credit: Jamillah Knowles & Digit / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

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