To ensure that AI-focused women academics and others receive the recognition they deserve, TechCrunch is launching a series of interviews highlighting remarkable women who have made significant contributions to the AI revolution. The series will continue throughout the year, shedding light on important work that often goes unnoticed. Read more profiles here.
Emilia Gómez is a principal investigator at the European Commission’s Joint Research Centre and scientific coordinator of AI Watch, the EC initiative to monitor the advancements, uptake, and impact of AI in Europe. Her team contributes scientific and technical knowledge to EC AI policies, including the recently proposed AI Act.
Gómez's research is rooted in the computational music field, where she explores how humans describe music and the methods used to digitally model it. Starting from the music domain, Gómez investigates the effects of AI on human behavior, particularly in relation to jobs, decisions, and child cognitive and socioemotional development.
Q&A
Briefly, how did you get your start in AI? What attracted you to the field?
I began my research in AI, specifically in machine learning, by developing algorithms for the automatic description of music audio signals in terms of melody, tonality, similarity, style, or emotion. I was drawn to the field for its modeling capabilities and the shift from knowledge-driven to data-driven algorithm design.
From my experience as a machine learning researcher, I realized the significant impact that algorithms have on people and shifted my focus to AI evaluation, studying how AI influences human behavior and evaluating systems in terms of fairness, transparency, and human oversight.
What work are you most proud of (in the AI field)?
I am proud of my contributions to music-specific machine learning architectures that have advanced the state of the art, particularly in tonality extraction and emotion modeling in music. Projects like Banda Sonora Vital and PHENICX, which focused on personalized music recommendation and enriched symphonic music experiences, are highlights of my career.
I am also proud of my team's work in supporting the EU AI liability directive, where we studied the inherent risks of AI systems and challenges in proving causation.
How do you navigate the challenges of the male-dominated tech industry, and, by extension, the male-dominated AI industry?
I navigate the male-dominated AI research and policy field by avoiding frustration and enjoying collaboration with excellent women and men in the field. I believe in seeking out mentorship and support from other women, nonbinary colleagues, and male allies.
What advice would you give to women seeking to enter the AI field?
I would encourage women to enter the AI field to bring diversity of vision and ideas. Seek out support from other women and allies in the field, and join affinity groups that promote diversity and inclusion.
What are some of the most pressing issues facing AI as it evolves?
In my opinion, there needs to be a balance between AI development and evaluation. Proper assessments and audits are necessary to address issues like bias and ensure the responsible use of AI.
What are some issues AI users should be aware of?
Users of AI-powered tools should understand the working principles and limitations of AI algorithms to use them responsibly. They should also be informed about the quality and certification of AI products to make informed decisions.
What is the best way to responsibly build AI?
Responsible AI development involves thorough evaluation, assessment of social impact, and mitigation of risks before deploying AI systems. Transparency, fairness, and human oversight should be prioritized from the design phase to build trustworthy AI that benefits both businesses and society.
