Last month we introduced the CC Chapter in Italy to you! This month we’re traveling north to the CC Chapter in The Netherlands! The Creative Commons Global Network (CCGN) consists of 43 CC Country Chapters spread across the globe. They’re the home for a community of advocates, activists, educators, artists, lawyers, and users who share CC’s vision and values. They implement and strengthen open access policies, copyright reform, open education, and open culture in the communities in which they live.

To help showcase their work, we’re excited to continue our blog series and social media initiative: CC Network Fridays. At least one Friday a month, we’ll travel around the world through our blog and on Twitter (using #CCNetworkFridays) to a different CC Chapter, introducing their teams, discussing their work, and celebrating their commitment to open! 

Next up is CC Netherlands!

The CC Dutch Chapter was formed in September 2018. Its Chapter Lead is Maarten Zeinstra and its representative to the CC Global Network Council is Lisette Kalshoven. Since the beginning, the Chapter has been involved in promoting and supporting openly licensed music, open GLAM, open education but over the last year, in particular, it has enhanced its activities covering almost all CCGN Platforms. To learn more about their work, we reached out to CC Netherlands to ask a few questions. They responded in both English and Dutch! 

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CC: What open movement work is your Chapter actively involved in? What would you like to achieve with your work?

CC Netherlands: We like to work together with the whole open sector. Open Licenses are awesome, but even more so when applied to sectors that really benefit knowledge creation and sharing. That’s why we have members from diverse backgrounds. You can see all Open Netherland members here. Are you a person living in the Netherlands? Join us!

CC: Op welke open thema’s is jullie chapter actief? Wat zouden jullie graag willen bereiken?

CC Nederland: Wij werken graag samen met de hele open sector. Open licenties zijn fantastisch, nog meer als ze daadwerkelijk gebruikt worden door de sectoren die kennis creeëren en delen. Daarom hebben we leden van diverse sectoren. Op onze site kun je zien wie er allemaal lid is van Open Nederland. Woon je ook in Nederland! Sluit je dan aan!

CC: What exciting project has your Chapter engaged in recently?

CC Netherlands: We are worried about the implementation of the DSM directive in Dutch copyright law. Exceptions and limitations are paramount in a working copyright system, and automatic filtering threatens those. We have been active in working towards a positive implementation of the new ‘Copyright Directive’ (#DSM) – informing government and parliament on the importance of open knowledge, licenses and broad implementation of exceptions and limitations.

CC: Wat is een project waar jullie chapter recent aan gewerkt heeft?

CC Nederland: Wij maken ons zorgen over de manier waarop de Europese richtlijn voor auteursrechten in Nederland wordt geïmplementeerd. Uitzonderingen en beperkingen op het auteursrecht zijn belangrijk voor een goed werkend stelsel. Automatische filters zijn hier een bedreiging voor. De afgelopen tijd hebben we ons ingezet om de implementatie positief te beïnvloeden, o.a. door de overheid en het parlement te informeren over het belang van open kennis, licenties en een juiste implementatie van de uitzonderingen en beperkingen.

CC: What do you find inspiring and rewarding about your work in the open movement?

CC Netherlands: The Dutch Chapter and @OpenNederland, the association that runs the Chapter, brings people together from all the corners of the open world in NL, open design, healthcare, heritage, education, and more. Thus far this has led to crossovers that did not take place before, like looking at open education from the user experience of a student: what can open education mean for your entire learning path from toddler to adult? 

CC: Wat vinden jullie inspirerend en waar halen jullie voldoening uit bij jullie werk in de open beweging?

CC Nederland: Het Nederlandse chapter en Open Nederland, de vereniging die het chapter ondersteund, brengen mensen bij elkaar uit alle hoeken van de open beweging. Bijvoorbeeld open design, gezondheidszorg, erfgoed, onderwijs en meer. Dit heeft al geleid tot kruisbestuivingen die niet eerder plaats hebben gevonden, zoals het bekijken van open onderwijs vanuit het perspectief van een leerling. Wat kan open onderwijs betekenen voor iemands onderwijs carrière, van kleuter tot volwassene? 

CC: What projects in your country are using CC licenses that you’d like to highlight?

• We are proud of our open music scene, including Tribe of Noise which also purchased the Free Music Archive in 2019. 
• Dutch GLAMs have been active with open licensing for a long time. Beyond our beautiful Rijksmuseum, also have a look at the Re:VIVE project, which invites artists to remix old archival sounds; @benglabs, which aims to make audiovisual heritage open and searchable; or the beautiful collection of the city archive of Den Bosch, with these billiard playing ladies. 
• Kenny Vleugels, a game developer from NL, creates really cool CC0 game assets. 
• We like to party when it is Public Domain Day in the Netherlands. We organise a fun and informative day with lectures about the Public Domain, but also about the creators whose work now entered the Public Domain. See full videos and photos from the 2020 edition here. International coordination takes place through pdday.org.
• Did you know the Dutch government uses CC0 as their standard on all text and data on websites? They have been doing so since 2010, and were—as far as we know—the first to do so. See the notice here. 
• We also have an award for the best re-use of open government data, the Stuiveling Open Data award. The 2019 winners researched fraud in healthcare using open data.
• Sharing government news in the current Corona-crisis is more important than ever, but it can be tough to weed through. The Open State Foundation has made all local government news accessible through one platform, all openly licensed.

CC: Wat zijn projecten die CC licenties gebruiken en die je graag onder de aandacht wil brengen?

• We zijn trots op de Nederlandse muziek scène, waaronder Tribe of Noise dat in 2019 Free Music Archive overgenomen heeft.
• Nederlandse culturele instellingen delen hun collecties al geruime tijd met open licenties. Naast het welbekende Rijksmuseum zijn er ook initiatieven zoals 
• Re:VIVE, een project waarbij kunstenaars en muzikanten uitgenodigd worden om geluiden uit archieven te remixen, 
• @benglabs, dat audiovisuele archieven ontsluit en doorzoekbaar maakt,
• Of de fantastische collectie van het archief van Den Bosch, met deze biljartsters.
• Kenny Vleugels maakt gave CC0 gelicenseerde game componenten,
• We vieren jaarlijks Publiek Domeindag, een leuke en informatieve dag waarbij we aandacht besteden aan de werken die publiek domein zijn geworden en de makers van deze werken. De foto’s en video’s van Publiek Domeindag 2020 zijn hier te zien. Internationale coördinatie van publiek Domeindag vieringen ondersteunen we met pdday.org.
• Wist je dat de Nederlandse overheid standaard CC0 gebruikt voor alle  tekst en data op hun websites? Dit doen ze al sinds 2010, voor zover wij weten waren ze de eerste overheid om dit te doen. Zie ook deze pagina.
• Er is een prijs voor het beste gebruik van open data in Nederland, de Stuiveling Open Data Award (SODA). De winnaars van 2019 deden, op basis van open data, onderzoek naar fraude in de zorgsector.
• Het delen van nieuws van de overheid is zeer belangrijk in de huidige Corona-crisis, maar het kan lastig zijn om de juiste informatie te vinden. Open State heeft al het nieuws op lokaal niveau op één platform gebundeld, onder een open licentie.

CC: What are your plans for the future? 

CC Netherlands: We hope to grow our membership in the coming year, engage more with our community, and do more outward-facing projects.

CC: Wat zijn jullie toekomstplannen?

CC Nederland: We willen nog meer leden aantrekken, onze huidige leden activeren en meer betrekken bij onze werkzaamheden en meer zichtbare projecten doen.

CC: Anything else you want to share?

CC Netherlands: The rise of algorithms determining possible copyright infringement can also have a negative impact on open content, because these algorithms do not take open licensing in account enough. That’s why we’ve started working on “Filter me niet” (Filter me not) in which we look for ways to indicate that you’re purposefully CC licensing to let others remix your work. The first results are in Dutch only, here.

CC: Wat wil je verder nog delen?

CC Nederland: Toenemend gebruik van algoritmes, om potentiële auteursrechtenschendingen te identificeren, heeft negatieve consequenties voor open content. Deze algoritmes houden onvoldoende rekening met open licenties. Daarom zijn we Filter Me Niet begonnen, een project waarin we manieren onderzoeken om actief aan te geven dat je bewust Creative Commons licenties gebruikt om je werk beschikbaar te stellen voor hergebruik. Een eerste resultaat is te zien op www.filtermeniet.nl.

Thank you to the CC Netherlands team, especially Lisette Kalshoven and Sebastiaan ter Burg for contributing to the CC Network Fridays feature, and for all of their work in the open community! To see this conversation on Twitter, click here. To become a member of the CCGN, visit our website!

: Featured image has icons by Guilherme Furtado and Vectors Point via Noun Project (CC BY 3.0).

The post Meet CC Netherlands, Our Next Feature for CC Network Fridays! appeared first on Creative Commons.

The wrap-up party for the annual CC Global Summit is always incredible, featuring local artists and musicians who send us off in style. Of course, things are a little different this year as we’ve transformed our in-person event to an entirely virtual one—but that doesn’t mean we can’t find a way to party together like we usually do!  

This year, we want to close the CC Global Summit (19-24 October 2020) by celebrating with musical performances showcasing the artistic talent of our global community. We’re looking for musicians, singers, DJs, dancers, or performance artists! Some things to keep in mind:

• The performances must be set to CC-licensed music
• We’re prioritizing diversity in languages and are actively seeking non-English performances
• You can perform in any genre but it must be in line with our Code of Conduct

The deadline to submit your application is Friday, August 28. 

If selected, you’ll work with the CC Summit Production team to record a one-song video performance that will be included in our CC Summit Closing Concert. The concert will be pre-recorded and released at the closing of our virtual event and shared afterward on Youtube. Join us! 

Apply by August 28!

: Featured image contains icons “Modern Dance” and “Singer” by Gan Khoon Lay (CC BY) via Noun Project.

The post Your Chance to Perform for the CC Global Summit! appeared first on Creative Commons.

In the second part of our series on artificial intelligence (AI) and creativity, we get immersed in the fascinating universe of AI in an attempt to determine whether it is capable of creating works eligible for copyright protection. Below, we present two examples of an AI system generating arguably novel content through two different methods: Markov Chains and Artificial Neural Network. We then apply the copyright eligibility criteria explained in “Artificial Intelligence and Creativity: Why We’re Against Copyright Protection for AI-Generated Output” to each example. 

Here’s the gist: Through the Jane Austen examples below, it’s clear that the seemingly “creative” choices made by the AI system are not attributable to any causal link between a human and the result, nor is it a human that defines the final form or expression of the work. The randomness elements incorporated in an AI program is what gives the illusion of creativity—and the closer one gets to a semblance of a creative work created by a human, the higher the similarity, thus the lower the originality. All this leads us to conclude that the copyright protection requirements of authorship and originality are not satisfied.

Method 1 – Markov Chains

Suppose you wanted to develop an AI system that could write like English novelist Jane Austen (1775-1817). To do this, one might model writing a sentence as a Markov chain. First discovered by Andrey Markov, a Markov chain is a stochastic model describing a sequence of events where the distribution of possibilities for the next event is dependent only on the current state of the sequence up to that point. These models were first applied to language by Claude Shannon in his groundbreaking paper, A Mathematical Theory of Communication.

For example, the word “Mrs.” (capitalized and with punctuation) occurs 2,157 times in the complete works of Jane Austen, and words following “Mrs.” are “Annesley,” “Gardiner,” “F.”, etc.  The AI system would then randomly select from the list of words that follow “Mrs.” to get a possible continuation of a sentence starting with “Mrs.” By leaving the repeated elements in the list and selecting from it uniformly at random, a preference for selecting words that occur more frequently after the “seed” (or initial) word is ensured. 

Let’s say the AI system randomly selects “Annesley” from the list to follow “Mrs.” This process can then be repeated with the list of words that follow “Annesley.” The word “Annesley” is less common (occurring only two times) and is followed by “to” and “is.” This process can be repeated multiple times to create a growing sentence stub and eventually construct something that resembles a sentence, like:

• Mrs. Annesley to it, to be mistress as the room for drawing room to be at the best known intimately judge wisely.

This “sentence” uses real words, which are chosen from Austen’s works, but doesn’t make much sense linguistically or grammatically. In order for the AI system to have more context when choosing words, a standard idea is to try to find words that follow multi-word snippets, rather than single words. In this example, you might look at the list of words that follow the two-word snippet “Mrs. Annesley,” which include “to” and “is.” Note: These are the same words that follow the one-word snippet, “Annesley.”

If you randomly select “to” to follow “Mrs. Annesley,” then you have to find the list of words following the snippet “Annesley to,” and so on. Continuing in this manner, you could construct a sentence like:

• Annesley to Miss Thorpe, and then only think! I found that I am sure you would think tall, and on this second interview, such she did not doubt your inclination.

Although this sentence is fragmented, it makes more sense than the previous sentence constructed from one-word snippets. Now let’s look at an example constructed using three-word snippets, starting with “Mrs. Annesley to”:

• Mrs. Annesley to Miss Darcy had been standing near enough for her to refuse the correspondence; it was impossible for me not to hear it.

At this level of context, we’re starting to see correct grammar. The sentence almost seems like it could have been written by Austen herself (on a bad day). However, this sentence is completely machine-generated. The longest snippet of words in this sentence that also appear consecutively in Austen’s works is:

• …for her to refuse the correspondence; it was impossible for…

However, the context around that snippet is different from Austen’s original work and is actually composed of different sections from several of her works. Here’s an example using four-word snippets, starting with “Mrs. Annesley to Miss”:

• Mrs. Annesley to Miss Darcy had been given, to remind her of what anxiety was; but when she saw, on her frequent and minute examination, that every symptom of recovery continued, and saw Marianne at six o’clock sink into a quiet, steady, and to all appearance comfortable, sleep, she silenced every doubt.

In this case, the method is beginning to lose originality. In fact, this sentence is composed of two snippets directly from Austen’s original works:

• Mrs. Annesley to Miss Darcy had been given, to remind her of…
• …to remind her of what anxiety was; but when she saw, on her frequent and minute examination, that every symptom of recovery continued, and saw Marianne at six o’clock sink into a quiet, steady, and to all appearance comfortable, sleep, she silenced every doubt.

These snippets are stitched together at “to remind her of.” The first snippet is from Austen’s novel Pride and Prejudice and the second is from Sense and Sensibility.

From these examples, it’s clear that expanding the “context” (i.e., the snippet length) increases the probability that the AI system will produce something akin to proper English, but it also decreases the originality of the output. To increase originality, the system requires more text from the original author’s works to be given as input. Even with this simple method, a system can produce fairly realistic English prose. In fact, the actual limit on the quality of content generated by this method turns out to be processing power, computation time, and storage. Also, since the goal is to generate prose only in the style of Jane Austen, the set of possible input text is limited to her works.

The Markov chain described above is just one example of a more general concept called a language model. In technical terms, language models are probability distributions over sequences of words in a language. In our case, we are interested in the probability that a word will occur as the next word, given a sequence of words up to some point. In this model, selecting at random from the probability distribution of possible words following the sequence up to the current point allows us to generate “prose.” As of this writing, one of the most recent large language models is called GPT-3, and was produced by an organization called OpenAI.  

Method 2 – Artificial Neural Network

GPT-3 is a considerably more sophisticated model than the Markov chain. In fact, it’s an example of an Artificial Neural Network (ANN). An ANN model is quite complicated, but here’s the gist: it’s a computational model based on the neural networks of the human brain.¹  Just as our brains are composed of interconnected processing elements (i.e. neurons) to process information, this artificial system also consists of a neural network that works together to solve a specific problem. Further, just as humans learn when given more information and subsequently change their actions to solve a problem, this artificial system also learns based on its inputs and outputs.

For example, to train an ANN model to predict the next word in a sequence, we make many predictions from different snippets of text per second and use a mathematical process to adjust the ANN model after each incorrect prediction. The adjustments are in the form of slightly changing the values of different numerical parameters in the model. Because the same parameters are used for each snippet, we need many of them to make a general enough model so that we can make predictions based on any arbitrary input sequence. (The large version of GPT-3 has around 175,000,000,000 parameters!) After several iterations of the process above to improve the model, we can generate new text by feeding the model existing text, appending whatever word it predicts next, and finally feeding the result back into the model. In reality, this process is a bit more complicated than described above but the general idea is that it allows us to generate a novel output on each run, rather than the same thing over and over.

Unfortunately, Brent (CC’s data engineer) couldn’t run the large model on his laptop, so he settled for using GPT-3’s predecessor GPT-2, which only has 117,000,000 parameters. The model comes “trained” out of the box, meaning it has already gone through many iterations of the process described above on English text. A user can “fine-tune” the model by performing further iterations on a sample of the English text of their choosing. Here is an example of the output after training the model for around 10 minutes on Jane Austen’s work:

• “Yes, I suppose,” replied Emma, “but I do not think she does a great deal of good, of course, I dare say; but if she could, she might, I must say, but she is a great lady at heart, I do not know whether we know that Mr. Elliot is his kindest sister.”

Note that while it’s not making much sense as a story, there are no real grammatical mistakes, and the “voice” does seem to closely echo Jane Austen’s. In general, every AI method for generating novel content, written or otherwise, involves developing a (potentially quite sophisticated) mathematical model that emulates some intelligent behavior. Then, content can be generated by selecting randomly from a probability space defined by that model.  

Applying copyright theories to our AI-generated Jane Austen sentences

On a theoretical level, ideas regarding “authorship” and “originality” as we examined them in the first post of this series appear to be at odds with any conception of AI (i.e. non-human) creativity. As we’ve seen in our Jane Austen example, the seemingly “creative” choices made by the AI system are not attributable to any causal link between a human and the result, nor is it a human that defines the final form or expression of the work. Where humans (such as AI programmers or users) are indeed involved in the creation of AI-generated output in the models described above, this involvement is solely mechanical, and not authorial or creative. The randomness elements incorporated in an AI program is what gives the illusion of creativity—and the closer one gets to a semblance of a creative work created by a human, the higher the similarity, thus the lower the originality. All this leads us to conclude that the copyright protection requirements of authorship and originality are not satisfied.

All said, as much as AI has advanced in the past few years, there exists no clarity, let alone consensus, over how to define the nascent and uncharted field of AI technology. Any attempt at regulation is premature, especially through an already over-taxed copyright system that has been commandeered for purposes that extend well beyond its original intended purposes. AI needs to be properly explored and understood before copyright or any intellectual property issues can be properly considered. That’s why AI-generated outputs should be in the public domain, at least pending a clearer understanding of this evolving technology.

Notes

1.  In more technical terms, an ANN can be defined as a class of functions that take vectors in from some vector space and map those vectors to a different vector space. Transitions between functions within the class are defined via an operator which is itself a mathematical function. The operator is designed to “train” the ANN model by minimizing some cost function associated with the output.  

The post Artificial Intelligence and Creativity: Can Machines Write Like Jane Austen? appeared first on Creative Commons.

Should novel output (such as music, artworks, poems, etc.) generated by artificial intelligence¹ (AI) be protected by copyright? While this question seems straightforward, the answer certainly isn’t. It brings together technical, legal, and philosophical questions regarding “creativity,” and whether machines can be considered “authors” that produce “original” works.

In search of an answer, we ran an admittedly unscientific Twitter poll over five days in June. Interestingly, almost 70% of a total of 338 respondents indicated that novel outputs from an AI system belong in the public domain, while 20% weren’t sure. For example, one commentator said that “since an AI will (given the same inputs and the same model) produce the same output every time, it’s hard to argue it’s unique and creative,” another succinctly argued: “system-generated activities = no creative input, therefore, no copyright,” while another respondent noted that it “depends on the nature of the AI, and the source materials used…I don’t think you could make a blanket rule for all AI.” This question was also debated at the World Intellectual Property Organization’s (WIPO) Conversation on Intellectual Property and Artificial Intelligence (Second Session) held from 7-9 July 2020. To share our general policy views on this topic from a global perspective, Creative Commons submitted a written statement and made two oral interventions (here and here). 

In this blog post, the first in a series on AI and creativity, we explore some of the fundamentals of copyright protection in an attempt to determine whether AI is capable of creating works eligible for copyright protection. In the second blog post, “Artificial Intelligence and Creativity: Can Machines Write Like Jane Austen?“ we walk you through two practical examples of an AI system generating arguably novel content and apply copyright eligibility criteria to them. By doing so, we hope to shed light on some of the copyright issues arising around the nascent field of AI technology.

What works can benefit from copyright protection? 

In order to determine what constitutes a creative work eligible for copyright protection, most national copyright regimes rely on the concepts of authorship and originality, among others. 

The concept of authorship

For a work to be protected by copyright, there needs to be creative involvement on the part of an “author.” At the international level, the Berne Convention stipulates that “protection shall operate for the benefit of the author” (art 2.6), but doesn’t define “author.” Likewise, in the European Union (EU) copyright law,² there is no definition of “author” but case-law has established that only human creations are protected.³ This premise is reflected in the national laws of countries of civil law tradition, such as France, Germany, and Spain, which state that works must bear the imprint of the author’s personality. As AI systems do not have a personality that they could imprint on what they produce, authorship is beyond limits for AI. 

In countries of common law tradition (Canada, UK, Australia, New Zealand, USA, etc.), copyright law follows the utilitarian theory, according to which incentives and rewards for the creation of works are provided in exchange for access by the public, as a matter of social welfare. Under this theory, personality is not as central to the notion of authorship, suggesting that a door might be left open for non-human authors. However, the 2016 Monkey selfie case in the US determined that there could be no copyright in pictures taken by a monkey, precisely because the pictures were taken without any human intervention. In that same vein, the US Copyright Office considers that works created by animals are not entitled to registration; thus, a work must be authored by a human to be registrable. Though touted by some as a way around the problem, the US work-for-hire doctrine also falls short of providing a solution, for it still requires a human to have been hired to create a work, whose copyright is owned by their employer.

As AI systems do not have a personality that they could imprint on what they produce, authorship is beyond limits for AI. 

Nevertheless, some countries (e.g. United Kingdom, Ireland, and New Zealand) do grant copyright-like protection to computer-generated works. The UK Copyright Designs and Patents Act 1988, for example, creates a legal fiction for computer-generated works where there is no human author. Section 9(3) states that “the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken.” An important nuance is that this provision assumes some form of creative intervention by a human and not autonomous, human-less generation by a computer program alone.  

The originality requirement

Common law jurisdictions generally have a low threshold for originality, requiring only a minimal level of creativity or intellectual labor and independent creation for a work to be protectable. The word “originality” in that context refers to the author as being the “origin” of a work, rather than to any creativity standard.⁴ Some other countries, like Brazil, approach originality from the negative, and state that all works of the (human) mind that do not fall within the list of works that are expressly defined as “unprotected works” can be protected. 

Under EU law and case-law, a work is original if it reflects the “author’s own intellectual creation,”⁵ i.e. the expression of the author’s personal touch and the result of free and creative choices. In short, both EU and US law establish the need for the work to be the proximate (direct) causal result of human action. This implies that AI, as it is currently understood as intelligence completely implemented via computational means, cannot make free and creative choices on its own and that the concept of creativity is not applicable to machines. 

Economics of AI-generated outputs: incentives, markets, and monopolies of exploitation 

Leaving aside theories of copyright protection and the rather abstract concepts of authorship and originality (and the even more hypothetical issue of machines having a personality and owning intellectual property rights), the real question we should ask ourselves relates to the economic environment around AI-generated content. Is there any market for AI-generated content? Do people really want to listen to Nirvana-esque algorithm-produced music or Google’s Deep-mind AI piano prowess, get immersed in the writings of a literary robot, or hang a computer-generated Rembrandt, a nightmarish Van Gogh-reminiscent Starry Night or a blurry portrait of a fictional aristocrat in their living room, not to mention to have to pay for any of that? And if so, would AI-generated products truly compete with artistic and literary works produced by humans, as substitute goods? Would the billions of AI-generated outputs produced faster than any human could produce or even consume, need any exclusivity (which is artificially inseminated in the market by means of a copyright “monopoly” of exploitation) to avoid market failure? 

Of course, AI-technology developers might expect to be incentivized to invest in innovation, research, and development to help solve the world’s problems and to make AI as useful to society as possible. But copyright protection of the “artistic” outputs by an AI system is not the appropriate mechanism to stimulate this development. Unfair competition and patent law (and to a certain extent, existing copyright law protecting software as literary works) are far better suited to stimulate innovation and ensure a return on investment for the development of AI technology. 

AI needs to be properly explored and understood before copyright or any intellectual property issues can be seriously considered.

All said, as much as AI has advanced in the past few years, there exists no clarity, let alone consensus, over how to define the nascent and uncharted field of AI technology. Any attempt at regulation is premature, especially through an already over-taxed copyright system that has been commandeered for purposes that extend well beyond its original intended purposes. AI needs to be properly explored and understood before copyright or any intellectual property issues can be seriously considered. That’s why AI-generated outputs should be in the public domain, at least pending a clearer understanding of this evolving technology.

In the second part of this series, “Artificial Intelligence and Creativity: Can Machines Write Like Jane Austen?” we look at two practical examples of an AI system generating “novel” content and apply the copyright eligibility criteria explained above.

Notes

1. There is as yet no widely accepted definition of “artificial intelligence.” We thus discuss this matter in general terms, and consider, strictly for the sake of discussion, that artificial intelligence is intelligence, or a simulation of intelligence, which is implemented via an automated machine, such as a digital computer.
2. Information Society Directive, 2001/29/EC.
3. Case C-145/10, Eva-Maria Painer v Standard Verlags GmbH 1 December 2011, Court of Justice of the European Union (CJEU).
4. For US case law on the concept of originality, see Alfred Bell & co. v. Catalda Fine Arts, Inc. 191 F2nd, Baltimore Orioles Inc. v. Major League Baseball Players Association, 805 F2nd 663 (7th Cir. 1986) and Feist Publications, Inc. v. Rural Tel. Serv. Co., 499 US 340 (1991).
5. Council Directive 2009/24/EC, Art 1(3), protection of computer programs as “the author’s own intellectual creation”; Database Directive 96/9/EC, Art 3(1); Case C‐5/08, Infopaq, ECLI:EU:C:2009:465; Information Society Directive, 2001/29/EC.

: Featured image is “Love Art Science 95” by Kollage Kid, licensed CC BY-NC-SA 2.0.

The post Artificial Intelligence and Creativity: Why We’re Against Copyright Protection for AI-Generated Output appeared first on Creative Commons.

This post was co-authored by CC’s Open Policy Manager Brigitte Vézina and Legal and Policy Intern Alexis Muscat.

Tomorrow is International Day of the World’s Indigenous Peoples, a day that seeks to raise awareness of and support Indigenous peoples’ rights and aspirations around the world. We at Creative Commons (CC) wish to highlight this important celebration and acknowledge that, internationally, measures need to be taken to protect Indigenous peoples’ rights and interests in their unique cultures. One measure, which intersects with our policy work at CC on Open GLAM, addresses the open, online sharing of Indigenous cultural heritage cared for within cultural heritage institutions. 

Creative Commons and the Open GLAM movement

Many galleries, libraries, archives, and museums (known collectively as “GLAMs”) work hard to make cultural heritage collections available to the public. For these institutions, providing access to knowledge and culture is a core aspect of their duty and public interest mission. Many institutions are digitizing and making cultural heritage collections available online in an effort to both preserve and openly share cultural heritage materials. The Open GLAM movement acknowledges this mission and actively promotes this premise, helping GLAMs make the most out of CC licenses and tools to communicate what users can do with digitized material. At CC, we strongly advocate for open access to public domain material held in GLAM collections for the benefit of all. CC firmly believes that digital reproductions of public domain material within these collections should remain in the public domain and be accessible online as openly as possible.

Indigenous cultural heritage and Open GLAM

Reuse freedoms associated with public domain materials, and fostered through digitization, can create tension when it comes to Indigenous cultural heritage. Existing copyright law, steeped in Western concepts and values, does not adequately protect Indigenous traditional cultural expressions, nor does it sufficiently reflect or account for Indigenous cultural values. By default, many forms of Indigenous heritage or “traditional cultural expressions” (which may include secret, sacred, or sensitive content) are inequitably deemed public domain under conventional copyright law.¹ One of the challenges is that the copyright system does not properly account for the ways in which traditional cultural expressions are created, collectively held, and transmitted through the generations. The copyright eligibility criteria, such as originality and authorship, are often at odds with Indigenous notions of creativity and custodianship over a community’s cultural heritage. As a result, it may seem that such heritage is freely available for use and reuse, when in truth this may not be the case. Permitting this level of access and use raises ethical concerns which must be fully considered.²

Existing copyright law, steeped in Western concepts and values, does not adequately protect Indigenous traditional cultural expressions, nor does it sufficiently reflect or account for Indigenous cultural values.

The notion of the “public domain” is relevant within the confines of the copyright system. So, while Indigenous cultural heritage may be regarded as public domain under copyright rules, and thus free to use, other rights and interests may still attach to it, stemming from various sources. These include other legal restrictions like privacy rights, other intellectual property rights (including sui generis rights to protect traditional cultural expressions), and personality rights, as well as Indigenous customary laws and protocols. In practice, this means that access to and use of Indigenous materials may be limited, and justified, on grounds found outside of the copyright system. Because these rights and interests are not protected under copyright law, they are not licensed under CC’s licenses and tools, which operate solely within the copyright system. This means that specific terms or conditions on access and use that are based on Indigenous rights, interests, or wishes are not fully addressed when applying CC licenses and tools only and that additional measures might be advisable to correctly reflect the conditions associated with access and use of traditional cultural expressions. Local Contexts, a labeling system inspired by Creative Commons, was designed to address this issue by alerting reusers to local protocols established by communities.

GLAMs are in a pivotal position to take active steps in support of Indigenous cultural interests and values. Through thoughtful, intentional, and respectful decision making, GLAMs can enable the ethical treatment of cultural heritage materials, going beyond the application of conventional copyright law and the determination of a work’s public domain status. GLAMs should take account of Indigenous peoples’ rights and interests, particularly regarding digitization, access, and reuse of Indigenous cultural heritage. 

A study of GLAM policies on Indigenous cultural heritage

In an effort to better understand how GLAMs are tackling this tension, we undertook desk-based research aimed at surveying and analyzing GLAM policies and practices dealing with the treatment of Indigenous cultural materials.³ After collecting a diverse range of resources from various GLAMs located in different world regions, we studied them to find common trends, best practices, strategies and rationales. ⁴

We found that some institutions attempt to strike a balance between their aim to share collections openly and the need to prioritize Indigenous peoples’ interests in their cultural heritage. The policies in place at Auckland War Memorial Museum (discussed here with Open GLAM on Medium), Museum of New Zealand Te Papa Tongarewa, and Museum of Applied Arts and Sciences are great examples of institutions working to strike this balance. 

Additionally, we were able to identify three key themes in the surveyed policies: 

1. Acknowledgment—GLAMs should recognize and affirm the interests Indigenous peoples have in their cultural and intellectual property, existing both inside and outside conventional copyright law.
2. Consultation—GLAMs should form authentic and meaningful relationships with source communities, understanding customary law and protocols, and determining community needs and wishes with regard to their cultural heritage. 
3. Guardianship—GLAMs should actively respect community decisions regarding digitization, access, and use, giving Indigenous communities full agency over how their cultural material is treated.

While this research provides us with initial insight, it is only the first step in understanding the important but complex interrelations between the goals of the Open GLAM movement and the celebration of the public domain on the one hand, and the ethical, and at times legal, obligation to respect Indigenous cultural heritage. Looking at institutional policies probes a narrow aspect of a much larger conversation. More work needs to be done, and CC will continue to explore ways to bring attention to this issue. In the meantime, we remain convinced that as far as Indigenous cultural heritage is concerned, GLAMs should acknowledge that access and reuse restrictions might be justified in certain situations. With continued efforts, we hope to better inform the Open GLAM movement of best practices when digitizing and making material available online, accounting for more than just the “public domain” status of Indigenous cultural heritage. 

We remain convinced that as far as Indigenous cultural heritage is concerned, GLAMs should acknowledge that access and reuse restrictions might be justified in certain situations.

Moving forward, we at Creative Commons intend to explore paths to find ways to resolve this tension in the GLAM space and beyond. Ideally, we would like to conduct further research to develop informed policy options, hold open conversations and consultations with relevant stakeholders on these important issues based on the principles of collaboration, inclusivity, and transparency, and continue to clarify how CC licenses and tools work and develop ways to better reflect and account for Indigenous rights and interests in their cultural heritage.

Notes

1. Some countries have sui generis (tailor-made) systems of protection in place designed specifically to protect traditional cultural expressions from misappropriation and misuse. For further information, see WIPO’s “Compilation of Information on National and Regional Sui Generis regimes for the Intellectual Property Protection of Traditional Knowledge and Traditional Cultural Expressions.” However, no such regime exists at the international level. The Intergovernmental Committee of the World Intellectual Property Organization is the forum in which negotiations take place to develop a sui generis international legal instrument for the protection of traditional cultural expressions.
2. In the case of museums, the International Council of Museums (ICOM) Code of Ethics provides one basis for recognizing Indigenous cultural interests as an ethical consideration.
3. For the sake of compatibility, we modeled our approach on the Open GLAM survey.
4. Note that the sample of policies reviewed was relatively small next to the large number of GLAMs. As such, the results are not comprehensive nor are they necessarily representative of GLAM practices more broadly.

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