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.
In complex systems there are hardly ever singular causes that explain undesired outcomes; in the case of algorithmic bias in music streaming, there is no single bullet that eliminates women from charts or makes Slovak or Estonian language content less valuable than that in English.
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.
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.