Tinomutendayi (Tino) Muzondidya, a Master’s student in Industrial Engineering (special focus on Data Science) and Research Assistant at the Policy Innovation Lab, recently had the opportunity to serve as a volunteer at the Deep Learning IndabaX South Africa conference. The event, held this year in Stellenbosch and renowned for fostering collaboration and knowledge sharing within the African AI ecosystem, provided Tino with an opportunity to contribute to its organisation while advancing his personal and professional goals.

“IndabaX was a chance to immerse myself in South Africa’s growing machine learning and artificial intelligence community and created a vibrant space for collaboration and learning”, was Tino’s reflection shortly after the event. In addition to helping ensure the smooth running of the event, Tino actively participated in workshops, hackathons and networking sessions.

One of the most valuable lessons he gained from the conference was the importance of thoroughly understanding data before developing and deploying models. As an aspiring data scientist, he is often eager to experiment with cutting-edge techniques, but workshops such as ‘Building Product Recommendations for Takealot.com’ reinforced the critical need to prioritise exploratory data analysis first.

This principle is especially relevant to Tino’s current research on analysing unstructured feedback in recommender systems using Transformer models, a project he is pursuing as part of his master’s programme. The workshop provided practical insights into real-world recommendation systems and introduced techniques that complemented his academic work, while broadening his toolkit for developing personised AI solutions.

The event’s focus was not limited to technical development. The ‘Building Science Communication Capacity in Africa’ workshop, led by Wilda Fourie-Basson, emphasised the importance of storytelling in science. This complemented the technical sessions by highlighting that effective AI practice extends beyond model building to clear and responsible communication. “I left with a renewed understanding of how vital communication is in the field of AI. The field is not just about building models but also explaining their purpose, limitations and real-world implications,” Tino reflected.

At the Policy Innovation Lab, Tino’s work focuses on ethical and responsible data use, particularly in the context of AI and machine learning. The themes explored at IndabaX 2025 such as the necessity of interdisciplinary collaboration, the ethical handling of data and the need for clear communication resonate strongly with projects he contributes to. His participation in the event directly supports his mission to contribute to AI for social good, aligning with the lab’s broader objectives.