trustworthy AI | Antal Dániel honlapja

Dutch AI Coalition Working Group Culture and Media

Thu, 22 Sep 2022 19:30:00 +0200

Reprex presented its Digital Music Observatory and Cultural Creative Sectors Industries Data Observatory as platforms for developing and evaluating trustworthy AI in the cultural domains. We hope to find new partners within the NLAIC community to join our open, collaborative projects.

We have reviewed more than 80 data observatories in the world, and we are building five modern ones.

It was particularly important for us to get away from the Hague, and meet organizations like DEN and the KB to find out how our ambitious plans could connect to their excellent work. Reprex is a finalist in the Hague Innovators Challenge 2022, and we would like to bring at least one global observatory, the planned European Music Observatory, into our beautiful and smart city. While knowledge graphs are virtual and live in the web 3.0, the Dutch AI Coalition and the country’s future competitiveness need to ensure that essential knowledge graphs will be managed by the ecosystem of Netherlands-based researchers, institutions, and startups. The ethical consciousness shown by the members of our Culture AI Lab shows that it is probably the best for future human generations globally, too.

The SABIO is one of the most interesting in the world and couuld be connected easily with our Cultural Creative Sectors Industries Data Observatory prototype.

The Culture AI Lab presented a handful of very interesting, ethical and interesting projects. Pressing Matter responds to growing concerns in the Netherlands and Europe about how to deal with the legacies of colonialism in museums and builds innovative tools for museums (and broader society) to address the question of ownership of objects collected in the colonial period. Dr Emily Hansell Clark, former editor of our Data&Lyrics blog, presented the Polyvocal Interpretation of Contested Colonial Heritage project.

Both projects are conceptually and technologically relevant to our Listen Local project. Our project aims to prevent the colonization or start the de-colonization of the local music ecosystem and make local artists of Utrecht, Vilnius, or Sarajevo visible and audible in their own cities’ public spaces or on the smartphones of their town.

The most compelling use case of Listen Local project is finding out why music recommender systems do not recommend some music at all. Or why is it so hard to connect Utrecht-based artists with fans living or visiting Utrecht on the Spotify or YouTube platform? The Responsible Recommenders in the Public Library Sector is looking for similar answers for librarians to avoid all recommendations to visitors pointing to U.S. authors and publishers.

Savvina Daniil’s excellent presentation raised very similar questions to our Feasibility Study On Promoting Slovak Music In Slovakia & Abroad.

Our deep dive into legislative and regulatory issues of AI highlighted that the past decade unleashed global web-based tools that have the potential to undermine our democratic and cultural cohesion. Reassuringly, we have seen that our thinking about the dangers of AI on European culture and the technological solutions to combat them are very widely shared by NLAIC Culture and Media members. We hope that our partners’ policy work in the Digital Music Observatory and our forming new observatories will support policy design and decision-making that will protect the Netherlands and the EU from some of these threats.

Learn more about the Dutch AI Coalition’s Cultur and Media Working Group (in Dutch:)

Recommendation Systems: What can Go Wrong with the Algorithm?

Thu, 06 May 2021 07:10:00 +0000

Traitors in a war used to be executed by firing squad, and it was a psychologically burdensome task for soldiers to have to shoot former comrades. When a 10-marksman squad fired 8 blank and 2 live ammunition, the traitor would be 100% dead, and the soldiers firing would walk away with a semblance of consolation in the fact they had an 80% chance of not having been the one that killed a former comrade. This is a textbook example of assigning responsibility and blame in systems. AI-driven systems such as the YouTube or Spotify recommendation systems, the shelf organization of Amazon books, or the workings of a stock photo agency come together through complex processes, and when they produce undesirable results, or, on the contrary, they improve life, it is difficult to assign blame or credit.

This is the edited text of my presentation on Copyright Data Improvement in the EU – Towards Better Visibility of European Content and Broader Licensing Opportunities in the Light of New Technologies - download the entire webinar’s agenda.

Assigning and avoding blame.

If you do not see enough women on streaming charts, or if you think that the percentage of European films on your favorite streaming provider—or Slovak music on your music streaming service—is too low, you have to be able to distribute the blame in more precise terms than just saying “it’s the system” that is stacked up against women, small countries, or other groups. We need to be able to point the blame more precisely in order to effect change through economic incentives or legal constraints.

This is precisely the type of work we are doing with the continued support of the Slovak national rightsholder organizations, as well as in our research in the United Kingdom. We try to understand why classical musicians are paid less, or why 15% of Slovak, Estonian, Dutch, and Hungarian artists never appear on anybody’s personalized recommendations. We need to understand how various AI-driven systems operate, and one approach would at the very least model and assign blame for undesirable outcomes in probabilistic terms. The problem is usually not that an algorithm is nasty and malicious; Algorithms are often trained through “machine learning” techniques, and often, machines “learn” from biased, faulty, or low-quality information.

Outcomes: What Can Go Wrong With a Recommendation System?

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. Some apparent causes may in fact be “blank cartridges,” and the real fire might come from unexpected directions. Systematic, robust approaches are needed in order to understand what it is that may be working against female or non-cisgender artists, long-tail works, or small-country repertoires.

Some examples of “undesirable outcomes” in recommendation engines might include:

Recommending too small a proportion of female or small country artists; or recommending artists that promote hate and violence.
Placing Slovak books on lower shelves.
Making the works of major labels easier to find than those of independent labels.
Placing a lower number of European works on your favorite video or music streaming platform’s start window than local television or radio regulations would require.
Filling up your social media newsfeed with fake news about covid-19 spread by some malevolent agents.

These undesirable outcomes are sometimes illegal as they may go against non-discrimination or competition law. (See our ideas on what can go wrong – Music Streaming: Is It a Level Playing Field?) They may undermine national or EU-level cultural policy goals, media regulation, child protection rules, and fundamental rights protection against discrimination without basis. They may make Slovak artists earn significantly less than American artists.

Metadata problems: no single bullet theory

In our work in Slovakia, we reverse engineered some of these undesirable outcomes. Popular video and music streaming recommendation systems have at least three major components based on machine learning:

The users’ history – Is it that users’ history is sexist, or perhaps the training metadata database is skewed against women?
The works’ characteristics – are Dvorak’s works as well documented for the algorithm as Taylor Swift’s or Drake’s?
Independent information from the internet – Does the internet write less about women artists?

In the making of a recommendation or an autonomous playlist, these sources of information can be seen as “metadata” concerning a copyright-protected work (as well as its right-protected recorded fixation.) More often than not, we are not facing a malicious algorithm when we see undesirable system outcomes. The usual problem is that the algorithm is learning from data that is historically biased against women or biased for British and American artists, or that it is only able to find data in English language film and music reviews. Metadata plays an incredibly important role in supporting or undermining general music education, media policy, copyright policy, or competition rules. If a video or music steaming platform’s algorithm is unaware of the music that music educators find suitable for Slovak or Estonian teenagers, then it will not recommend that music to your child.

Furthermore, metadata is very costly. In the case of cultural heritage, European states and the EU itself have been traditionally investing in metadata with each technological innovation. For Dvorak’s or Beethoven’s works, various library descriptions were made in the analogue world, then work and recording identifiers were assigned to CDs and mp3s, and eventually we must describe them again in a way intelligible for contemporary autonomous systems. In the case of classical music and literature, early cinema, or reproductions of artworks, we have public funding schemes for this work. But this seems not to be enough. In the current economy of streaming, the increasingly low income generated by most European works is insufficient to even cover the cost of proper documentation, which then sends that part of the European repertoire into a self-fulfilling oblivion: the algorithm cannot “learn” its properties and it never shows these works to users and audiences.

Until now, in most cases, it was assumed that it is the artists or their representative’s duty to provide high quality metadata, but in the analogue era, or in the era of individual digital copies, we did not anticipate that the sales value will not even cover the documentation cost. We must find technical solutions with interoperability and new economic incentives to create proper metadata for Europe’s cultural products. With that, we can cover one area out of the three possible problem terrains.

But this is not enough. We need to address the question of how new, better Algorithms can learn from user history and avoid amplifying pre-existing bias against women or hateful speech. We need to make sure that when Algorithms are “scraping” the internet, they do so in an accountable way that does not make small language repertoires vulnerable.

Incentives and investments into metadata

In our paper we argue for new regulatory considerations to create a better, and more accountable playing field for deploying Algorithms in a quasi-autonomous system, and we suggest further research to align economic incentives with the creation of higher quality and less biased metadata. The need for further research on how these large systems affect various fundamental rights, consumer or competition rights, or cultural and media policy goals cannot be overstated. The first step is to open and understand these autonomous systems. It is not enough to say that the firing squads of Big Tech are shooting women out from charts, ethnic minority artists from screens, and small language authors from the virtual bookshelves. We must put a lot more effort on researching the sources of the problems that make machine learning Algorithms behave in a way that is not compatible with our European values or regulations.

This blogpost was first published on our general interest blog Data & Lyrics

Music Streaming: Is It a Level Playing Field?

Tue, 23 Feb 2021 11:00:00 +0000

Our article, Music Streaming: Is It a Level Playing Field? is published in the February 2021 issue of CPI Antitrust Chronicle, which is fully devoted to competition policy issues in the music industry.

The dramatic growth of music streaming over recent years is potentially very positive. Streaming provides consumers with low cost, easy access to a wide range of music, while it provides music creators with low cost, easy access to a potentially wide audience. But many creators are unhappy about the major streaming platforms. They consider that they act in an unfair way, create an unlevel playing field and threaten long-term creativity in the music industry.

Our paper describes and assesses the basis for one element of these concerns, competition between recordings on streaming platforms. We argue that fair competition 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.

You can read the entire issue and the full text of our article on Competition Policy International in pdf.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 18:10:00 +0200

The majority of music sales in the world is driven by AI-algorithm powered robots that create personalized playlists, recommendations and help programming radio music streams or festival lineups. It is critically important that an artist’s work is documented, described in a way that the algorithm can work with it.

In our research paper – soon to be published – made for the Listen Local Initiative we found that 15% of Dutch, Estonian, Hungarian, or Slovak artists had no chance to be recommended, and they usually end up on Forgetify, an app that lists never-played songs of Spotify. In another project with rights management organizations, we found that about half of the rightsholders are at risk of not getting all their royalties from the platforms because of poor documentation.

But how come that distributors give streaming platforms songs that are not properly documented? What sort of information is missing for the European repertoire’s visibility? Reprex is exploring this problem in a practical cooperation with SOZA, the Slovak Performing and Mechanical Rights Society, and in an academic cooperation that involves leading researchers in the field. A manuscript co-authored Martin Senftleben, director of the Institute for Information Law in Amsterdam, and eminent researchers in copyright law and music economics, Reprex’s co-founder makes the case that Europe must invest public money to resolve this problem, because in the current scenario, the documentation costs of a song exceed the expected income from streaming platforms.

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. 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. A trade-off between data harmonisation and interoperability on the one hand, and transparency and accountability of content recommender systems on the other, could pave the way for successful new initiatives. Download the manuscript from SSRN

Our Slovak Demo Music Database project is a best example for this. We started systematically collect publicly available information from Slovak artists (in our write-in process) and ask them to give GDPR-protected further data (in our opt-in process) to create a comprehensive database that can help recommendation engines as well as market-targeting or educational AI apps.

We believe that one of the problems of current AI algorithms that they solely or almost only work with English language documentation, putting other, particularly small language repertoires at risk of being buried below well-documented music mainly arriving from the United States.

We are looking for rightsholders and their organizations, artists, researchers to work with us to find out how we can increase the visibility of European music.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 11:00:00 +0000

This article, published in JIPITEC in 2022, remains one of our most cited works on copyright, metadata, and cultural policy.

The paper shows how fragmented copyright metadata undermines the visibility of European creative works, causes royalty losses for artists, and limits the ability of European industries to compete globally in emerging areas like AI training and recommender systems.

Using the music industry as a central case study, the article highlights why improved metadata and licensing infrastructures are vital. Its findings directly connect to our current projects on trustworthy AI, cultural data spaces, and fair remuneration systems.

📄 Read the published version in JIPITEC: Full text PDF
📄 Preprint version available on SSRN: SSRN abstract

Feasibility Study On Promoting Slovak Music In Slovakia & Abroad

Sun, 27 Dec 2020 11:00:00 +0000

Download the study in Slovak or in English.

In 2015, realizing the low visibility and income-generating potential of Slovak music, the legislation introduced an amendment to the broadcasting act to regulate local content in radiostreams. The Slovak content promoting policy was well-intended but not based on any impact assessment, and it reached its goal only partially.

The Slovak broadcasting quotas in comparison with other national quotas a very simple, and they are impossible to measure, which makes both compliance and enforcement very difficult. Radio editors do not get any help to find music that fits into the playlists and fulfil the quota obligations – in many cases, it is impossible for them to find out if a song actually meets the quota requirements. For the same reason, neither is enforcement possible.

Another deficiency of the broadcasting quotas is that because of its fuzzy target, it is not clear whom it tries to help, and it has few friends. It is unclear how performers, composers or Slovak music producers can benefit from the system. Furthermore, it only helps a few genres, and it decreases the chances of other Slovak music in instrumental and non-Slovak language genres (for example, classical, jazz, rock) to be heard.

And at last, radio is losing its importance in music discovery. New generation find the music during their music discovery age on YouTube and digital streaming platforms. A Slovak content promoting policy that does not work on digital streaming platforms will be obsolete when radio content providers will switch to digital streaming in the foreseeable future.

Our Feasibility Study follows the following logic: In the first chapter we introduce various music recommendation systems in the context of local content promotion polices, like local mandatory content quota regulations.

In the second chapter, we consider the market-based or creative industry economy supporting policy goals, measurements, and potential support given to artists and producers.

We then turn in the third chapter to content-based local regulations promoting the use of the Slovak language or Slovak music content, irrespective of the performers and producers nationality, residence or ethnicity.

We introduce the idea of the Slovak Music Database, a comprehensive, mainly opt-in, opt-out database that of Slovak artists and Slovak music that should be supported by the local content regulation and other policies. We also create a Demo Slovak Music Database to understand the problem and scope of the creation of the comprehensive version.

The project website contains the Demo Slovak Music Database.

We also created a Demo Recommendation System. We explain here why.

Research questions

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?
How can we measure the value gap between what some media platforms, most particularly the biggest YouTube, does not pay out to the Slovak stakeholders within Slovakia?
What is the interplay of the various definitions on market share and national quota targets?
How ‘shadow-markets’ of home copying and unlicensed media platforms, such as YouTube impact market shares directly and national quotas indirectly?
How can modern data science, predictive microeconomics and statistics help increase the market share of Slovak music in Slovakia and abroad?

Thanks for the entire Reprex team who contributed to the English version:

Dr. Emily H. Clarke, musicology
Stef Koenis, musicologist, musician
Dr. Andrés Garcia Molina, data scientist, musicologist, editor
Kátya Nagy, music journalist, research assistant;

and the Slovak version:

Dominika Semaňáková, musicologist, editor
Dáša Bulíková, musician, translator.