After a very thorough modernization of the package’s exception handling, documentation, and code dependencies that I did in the last week, the spotifyr package has passed again the peer-review standards and it is back on CRAN. The package is an excellent starting to point for R newbies to try their hands on musicology analysis with a few keystrokes. And of course, it is an essential part of the research infrastructure of musicology worldwide in far more advanced applications.
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.
Our new study opens the question of the local music promotion within the digital environment. The Slovak Performing and Mechanical Rights Society (SOZA), the State51 music group in the United Kingdom, and the Slovak Arts Council commissioned Reprex to created a feasibility study which provides recommendations for better use of quotas for Slovak radio stations and which also maps the share and promotion of Slovak music within large streaming and media platforms such as Spotify.
Why are the total market shares of Slovak music relatively low both on the domestic and the foreign markets? How can we measure the market share of the Slovak music in the domestic and foreign markets? We offer some answers and solution based on …
At last, Reprex has its own company website, leaving the two flagship project sites, the Demo Music Observatory and the Listen Local separate. We are back to blogging after a particularly difficult lockdown period.
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.
Katarzia's latest album tour was postponed indefinitely because of Covid-19. She signs in Slovak, and would like to stick around – as soon as the pandemic is over, she would like to return to the clubs and play for her fans.
You need to have 1000x more followers to double the routes leading to your artist profile and recordings with the current algorithms. We need new recommendation engines to dig out the local artists buried under a pile of international hits.
Our Demo Music Observatory started the release of the national markets shares on Spotify’s National Top 50 years with 15 months of history in a few select European countries. In some countries, the market share of locally produced music remains critically low, or decreasing, but there are notable exceptions. Getting on the top of the US market is becoming more difficult from the rest of the world.
The problem of the music industry is not too little, but too much data. Music is drowning in numbers, and it has too little resources to turn much data into valuable information. We have shown that we open collaboration is the key to success.