We do care what our children learn, but we do not care yet about what our robots learn from. One key idea behind trustworthy AI is that you verify what data sources your machine learning algorithms can learn from. As we have emphasised in our forthcoming academic paper and in our experiments, one key problem that goes wrong when you see too few small country artists, or too few womxn in the charts is that the big tech recommendation systems and other autonomous systems are learning from historically biased or patchy data.
Reprex's project, the automated Demo Music Observatory will be represented by Daniel Antal, co-founder of Reprex among other building bridges projects. This project offers a different approach to the planned European Music Observatory based on the principles of open collaboration, which allows contributions from small organizations and even individuals, and which provides higher levels of quality in terms of auditability, timeliness, transparency and general ease of use.
Our paper argues that fair competition in music streaming is restricted by the nature of the remuneration arrangements between creators and the streaming platforms, the role of playlists, and the strong negotiating power of the major labels. It concludes that urgent consideration should be given to a user-centric payment system, as well as greater transparency of the factors underpinning playlist creation and of negotiated agreements.
I was selected into 2021 Fellowship program of JUMP, the European Music Market Accelerator. Jump provides a framework for music professionals to develop innovative business models, encouraging the music sector to work on a transnational level. The European Music Market Accelerator composed of MaMA Festival and Convention, UnConvention, MIL, Athens Music Week, Nouvelle Prague and Linecheck support him in the development of our two, interrelated projects over the next nine months.
Our startup, Reprex is committed to develop its data platforms, or automated data observatories, and its Listen Local system in a trustworthy manner. Our startup participates in various scientific collaborations that are researching ideas on future regulation of copyright and fair competition with respect to AI algorithms, and joined the Dutch AI Coalition to position the company and the Netherlands at the forefront of knowledge and application of AI for prosperity and well-being, respecting Dutch and European values.
While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. Preprint of our article with copyright researchers.
We needed a database of Slovak music to show how that national repertoire is seen by media and streaming platforms, how can we give it greater visibility in radio and streaming platforms, and what are the specific problems why certain artists and music is almost invisible.
Regulating black box, private algorithms and data monopolies is only a first step to damage control. Deploying white, transparent algorithms and building collaborative or open data pools can only guarantee fairness in the digital platforms, in recommendations, and generally in the use of AI.
Connecting local bands with local fans, joining scenes across the globe.