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In this episode, we sit down with Marco Antonio Stranisci, a postdoctoral researcher in Natural Language Processing at the University of Turin and founder of aequa-tech, an AI startup focused on social impact. Marco shares his journey from humanities to computer science, his activism against hate speech, and the creation of Debunker-Assistant, a tool designed to combat misinformation.
We explore the ethical dimensions of AI, the challenges of launching a startup in a crowded tech landscape, and the importance of participatory design in building inclusive technologies. Marco also offers advice to young researchers navigating academia and industry, and invites listeners to contribute to his open-source initiative, the Citizen Dataset Lab.
We express our gratitude to University of Turin and personally Lucia Salto for the guest of the podcast Marco Antonio Stranisci.
🔑 Key Topics Covered
- Marco’s academic journey: from humanities to computational linguistics
- The intersection of activism and AI: detecting hate speech
- Founding aequa-tech and building Debunker-Assistant
- Challenges of entrepreneurship in the AI space
- Ethical concerns in AI development and data collection
- Participatory design and citizen involvement in tech
- Open-source vs. closed-source models in AI
- Advice for PhD students and early-career researchers
- The future of NLP and interdisciplinary AI
- The Citizen Dataset Lab initiative
⏱️ Question Timestamps
- 01:33 – Marco’s academic journey: switching from humanities to IT
- 02:57 – How activism led to a PhD in computer science
- 03:53 – Translating research into startup innovation
- 05:12 – Why Marco chose entrepreneurship
- 06:00 – Emotional highs and lows of startup life
- 07:26 – Benefits and drawbacks of leaving academia
- 08:45 – The crowded AI landscape post-ChatGPT
- 11:56 – Marco’s elevator pitch for aequa-tech
- 13:45 – Debunker-Assistant and participatory design
- 15:02 – Challenges of analyzing social media data
- 16:38 – Open-source values and transparency
- 17:58 – Future plans: interdisciplinary and efficient AI
- 19:00 – Advice for PhD students using AI
- 21:45 – Ethical concerns and the importance of learning
- 23:01 – Humanities in STEM: is something missing?
- 26:18 – How listeners can help: Citizen Dataset Lab
- 27:36 – Multilingual participation and open collaboration
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