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Women in AI: EU’s Emilia Gómez started her AI career in music

In an effort to give female academics and others focused on AI well-deserved and long-overdue spotlight time, TechCrunch has launched a series of interviews focusing on the remarkable women contributing to the AI ​​revolution. As the AI ​​craze continues, we will publish articles throughout the year highlighting critical work that is often overlooked. Read more profiles here.

Emilia Gómez is Principal Investigator at the European Commission Joint Research Center and Scientific Coordinator of AI Watch, the European Commission’s initiative to monitor the progress, adoption and impact of artificial intelligence in Europe . Her team contributes scientific and technical knowledge to EU AI policy, including the recently proposed AI Bill.

Gomez’s research is based in the field of computational music, where she works to understand the way humans describe music and its digital modeling methods. Starting in the field of music, Gomez studies the impact of artificial intelligence on human behavior, specifically on work, decision-making, and the cognitive and socio-emotional development of children.


In a nutshell, how did you get started in the field of artificial intelligence? What drew you to this field?

I started research in artificial intelligence, specifically machine learning, as a developer of algorithms that automatically describe musical audio signals, including melody, pitch, similarity, style or emotion, which are used in different applications from music platforms to education. I started investigating how to design novel machine learning methods to handle different computational tasks in the music domain, and the relevance of data pipelines, including dataset creation and annotation. What I liked about machine learning at the time was its modeling capabilities and the shift from knowledge-driven to data-driven algorithm design—for example, instead of designing descriptors based on our knowledge of acoustics and music, we now use our expertise to design datasets, architectures, and training and evaluation procedures.

From my experience as a machine learning researcher and seeing my algorithms “at work” in different fields (from music platforms to symphony concerts), I realized the huge impact these algorithms have on people (e.g. listeners, musicians), And guided my research toward AI evaluation rather than development, specifically studying the impact of AI on human behavior and how to evaluate systems in terms of fairness, human oversight, or transparency. This is a current research topic of my team at the Joint Research Center.

What work (in artificial intelligence) are you most proud of?

On the academic and technical side, I am proud of the Barcelona Music Technology Group’s contributions to music-specific machine learning architectures that have advanced the state-of-the-art in the field, as reflected in my citation record. For example, during my Ph.D. I proposed a data-driven algorithm to extract pitch from an audio signal (e.g. if the musical piece is in the key of C major or D minor), which has become a key reference in the field and I later co-designed a method for automatic description Machine learning methods for modeling the melody of music signals (e.g., for searching songs by humming), rhythm, or emotion in music. Most of these algorithms are currently integrated into Essentia, an open source library for audio and music analysis, description, and synthesis, and have been exploited in many recommendation systems.

I’m particularly proud of Banda Sonora Vital (LifeSoundTrack), a Red Cross Humanitarian Technology Award-winning project where we developed a personalized music recommender for elderly Alzheimer’s patients. There is also PHENICX, a large project funded by the European Union (EU) where I coordinate the use of music; and artificial intelligence to create rich symphonic music experiences.

I love the music computing community and I am delighted to be the first female president of the International Society for Music Information Retrieval, to which I have contributed throughout my career and am particularly interested in increasing diversity in the field.

I joined the Commission in 2018 as Chief Scientist, where I currently provide scientific and technical support for the AI ​​policy developed by the EU, in particular the Artificial Intelligence Act. Judging from this recent work (which is less obvious in terms of publications), I am proud—I say “humble”, because you can probably guess here—of my modest technical contribution to the AI ​​Act. There are a lot of people involved! For example, I work a lot on the harmonization or translation between legal and technical terminology (e.g. proposing definitions based on existing literature) and on assessing the practical implementation of legal requirements, such as high transparency or technical documentation. Risky AI systems, general AI models, and generative AI.

I am also very proud of the work my team has done in support of the EU Responsible Artificial Intelligence Directive, in which we looked at the specific characteristics that lead to inherent risks in AI systems, such as lack of causality, opacity, unpredictability or their self- and Continuity – the ability to learn and assess the associated difficulties that arise in proving cause and effect relationships.

How do you deal with the challenges of the male-dominated tech industry and the male-dominated artificial intelligence industry?

It’s not just technology – I’m also exploring the male-dominated world of AI research and policy! I have no techniques or strategies because this is the only environment I know. I don’t know what it’s like to work in a diverse or female-dominated work environment. “Wouldn’t it be nice?” like in the Beach Boys song. To be honest, I try to avoid depression and have fun in this challenging situation, work in a world dominated by very confident men, and enjoy working with amazing women in the field.

What advice would you give to women seeking to enter the field of artificial intelligence?

I would tell them two things:

We desperately need you—please come into our space because we desperately need diverse visions, approaches, and ideas. For example, according to the divineAI project (a project I co-founded that monitors diversity in the field of artificial intelligence), only 23% of author names at the 2023 International Machine Learning Conference and the International Joint Conference on Artificial Intelligence were female; At the International Joint Conference on Artificial Intelligence, only 23% of author names were female, regardless of gender identity.

You’re not alone—there are many women, non-binary colleagues, and male allies in the field, even though we may not be as visible or recognized. Find them and get their guidance and support! Against this background, many affinity groups have emerged in the research field. For example, when I became president of the International Society for Music Information Retrieval, I was very active in the Women in Music Information Retrieval Initiative, which was a pioneer in diversity efforts in music computing and had a very successful mentoring program.

What are the most pressing issues facing artificial intelligence in its development?

I believe that researchers should put as much effort into AI development as AI evaluation because there is a lack of balance. The research community is so busy advancing the latest technology in AI capabilities and performance, and so excited to see their algorithms being used in the real world, that they forget to conduct proper evaluation, impact assessments, and external audits. The smarter the AI ​​system, the smarter its assessments should be. The field of AI evaluation is under-researched, which is responsible for many of the incidents that have led to AI getting a bad rap, such as the presence of gender or racial bias in data sets or algorithms.

What issues should artificial intelligence users pay attention to?

Citizens who use AI tools, such as chatbots, should know that AI is not magic. Artificial intelligence is the product of human intelligence. They should understand how AI algorithms work and have limitations so that they can challenge them and use them responsibly. It is also important for citizens to understand the quality of AI products and how they are evaluated or certified so they know which products can be trusted.

What is the best way to build artificial intelligence responsibly?

In my opinion, the best way to develop AI products (that produce good social and environmental impacts in a responsible way) is to spend the resources needed to assess, assess social impacts and mitigate risks – for example, fundamental rights – before putting an AI system on the market. This is good for the interests of the company and trust in the product, and also good for society.

Responsible AI or trustworthy AI is an approach to building algorithms where aspects such as transparency, fairness, human oversight, or social and environmental well-being need to be addressed from the very beginning of the AI ​​design process. In this sense, the Artificial Intelligence Act not only sets the standard for the regulation of AI globally, but also embodies Europe’s emphasis on credibility and transparency – promoting innovation while protecting citizens’ rights. I think this will increase citizens’ trust in products and technology.

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