Tekoälyn pedagoginen murros: opettajan ja opiskelijan suhteen uudelleenmuotoutuminen

Kirjoittaja: Markku Kiikeri (OTT/Dr.iur., eurooppaoikeuden yliopistonlehtori)

Markku Kiikeri

Tekoälyn tulo yliopistopedagogiikkaan ei ole vain uuden teknologian käyttöönottoa – se on epistemologinen ja institutionaalinen käänne, joka muuttaa tietämisen, oppimisen ja opettamisen suhteita. Kun tekoäly kykenee analysoimaan, selittämään, arvioimaan ja jopa opettamaan, yliopiston vuosisatainen rakenne, jossa opettaja on tiedon auktoriteetti ja opiskelija tiedon vastaanottaja, alkaa väistämättä muuttua.

Tiedon jakajasta tulee oppimisen arkkitehti. Tekoälyn aikakaudella opettajan keskeinen tehtävä ei enää ole tiedon välittäminen, sillä tekoälyjärjestelmät kykenevät tarjoamaan lähes rajattoman määrän ajantasaista ja kontekstuaalista tietoa. Tämä siirtää painopisteen tiedon jakamisesta tiedon suodattamiseen, jäsentämiseen ja eettiseen tulkintaan.

Opettaja toimii oppimisen arkkitehtina – hän luo puitteet, joissa opiskelija voi tekoälyn avulla rakentaa omaa ymmärrystään, mutta samalla säilyttää kriittisen etäisyyden koneen tuottamaan tietoon.

Tämä muutos haastaa myös opettajan asiantuntijuuden käsitteen: ei riitä, että opettaja hallitsee oman tieteenalansa, hänen on ymmärrettävä tekoälyn logiikkaa, rajoituksia ja vaikutuksia tiedonmuodostukseen. Pedagoginen auktoriteetti ei enää perustu yksin tietämiseen, vaan kykyyn ohjata tiedon dialogia ihmisen ja koneen välillä.

Myös opiskelijan rooli muuttuu. Hän kehittyy passiivisesta vastaanottajasta aktiiviseksi tiedonrakentajaksi. Tekoäly mahdollistaa yksilöllistetyn oppimisen tavalla, jota perinteinen opetusrakenne ei ole voinut toteuttaa. Opiskelija voi saada reaaliaikaista palautetta, harjoituksia ja oppimispolkuja, jotka mukautuvat hänen osaamiseensa ja kiinnostuksiinsa. Tämä lisää autonomisuutta ja vastuuta omasta oppimisesta – mutta samalla uhkaa katkaista pedagogisen yhteisön sosiaalisen kudoksen, jos tekoälystä tulee opiskelijan ensisijainen vuorovaikutuskumppani.

Opiskelijan tehtävä ei enää ole oppia tietoa, vaan oppia tulkitsemaan ja arvioimaan tekoälyn tuottamaa tietoa. Hänestä tulee tiedon kriitikko, arvioija ja suunnistaja – ei enää vain oppija, vaan reflektiivinen tietotoimija.

Keskeisiksi elementeiksi tulevat pedagoginen vuorovaikutus ja inhimillinen läsnäolo. Tekoäly voi tukea oppimista monin tavoin, mutta se ei kykene korvaamaan inhimillistä läsnäoloa, empatiaa ja moraalista vastuuta, jotka muodostavat opetuksen eettisen ytimen.

Yliopistossa opettajan ja opiskelijan välinen suhde ei ole vain tiedonvälityssuhde, vaan myös arvojen, ajattelun ja identiteetin kasvattamisen prosessi. Kun tekoäly hoitaa yhä suuremman osan tiedon prosessoinnista, juuri inhimillinen kohtaaminen saa uuden merkityksen. Opettajan tehtävä on muistuttaa, että oppiminen ei ole pelkkä tehokkuuden tai tiedon määrän kysymys, vaan syvimmiltään ihmisen ja maailman välisen suhteen muotoutumista.

Tarvitaan uusi akateeminen sopimus. Tekoälyn myötä yliopisto joutuu määrittelemään uudelleen pedagogisen sopimuksen, joka yhdistää vapauden, vastuun ja tiedon eettisen käytön. Opettaja ei enää toimi tiedon omistajana, vaan yhteisön ohjaajana, joka auttaa opiskelijoita käyttämään tekoälyä viisaasti ja kriittisesti. Opiskelija puolestaan ei voi luottaa sokeasti tekoälyn vastauksiin, vaan hänen on opittava arvioimaan algoritmisten prosessien taustalla olevia oletuksia, arvoja ja vinoumia.

Tämä uusi suhde perustuu dialogiin – ei vain opettajan ja opiskelijan välillä, vaan myös heidän ja tekoälyn välisessä kolmiosuhteessa. Pedagoginen tila muuttuu yhteisölliseksi laboratorioksi, jossa ihminen ja kone yhdessä muovaavat tiedon tulevaisuutta.

Tekoälyn aikakaudella opettaja ja opiskelija eivät siis enää toimi hierarkkisessa tiedon välityssuhteessa, vaan dialogisessa ja tutkivassa suhteessa, jossa molemmat ovat oppijoita.

Opettajan tehtävä on varmistaa, että tämä uusi suhde pysyy inhimillisen järjen, vastuun ja sivistyksen piirissä. Yliopisto ei tällöin menetä tehtäväänsä. Se vain muuttaa sen muotoa. Tiedon temppelistä tulee inhimillisen ja tekoälyllisen ajattelun yhteinen kasvualusta.

Ystävällisin terveisin, Tekoäly

Editorin kynästä*

Kirjoittaja: Mikko T. Huttunen (OTT, kansainvälisen oikeuden yliopistonlehtori)

Mikko T. Huttunen

Erään opettamani opintojakson palautuslaatikkoon tupsahti keväällä generatiivista tekoälyä käsittelevä (sic!) englanninkielinen essee, josta plagiaatintunnistusjärjestelmä TurnItInin mukaan 34 prosenttia oli generatiivisen tekoälyn kirjoittamaa. Tulos on ikävä mutta maltillinen: korkein tähän mennessä näkemäni luku on 80 %.

Opiskelijan selitys? Esseestä oli kaksi versiota, ja hän oli vahingossa lähettänyt "väärän" tiedoston! Huojentavaa. Jostain syystä kuitenkin "oikean" tiedoston löytäminen ja lähettäminen kesti kolme päivää.

Generatiivisen tekoälyn käyttö opintosuorituksissa näyttää toisintavan iänikuista teknologian ja sääntelyn välistä suhdetta, jossa sääntely laahaa teknologisen kehityksen perässä. Mieleen tulee väistämättä Lyria Bennett Mosesin vuonna 2007 julkaiseman, omassa skenessään kuuluisan artikkelin otsikko: Recurring Dilemmas: The Law's Race to Keep Up With Technological Change (Bennett Moses 2007). Korkeakoulujen tekoälyohjeistukset kilpailevat pysyäkseen chatbottien kehityksen tahdissa.

Tehtävä vaikuttaa mahdottomalta. Vielä pari vuotta sitten chatbotit eivät kyenneet tuottamaan tekstiä täsmällisillä alaviitteillä. Teksti oli siis kelvotonta opintosuoritukseksi ainakin oikeustieteen standardeilla. Tiedustellessani asiaa Grok-tekoälyltä, vastaa se/hän iloisesti:

Grokin vastaus saatteeseen "can you create text with footnotes and specific references to journal articles"

Lopputulos, jolle tässä en tohdi antaa palstatilaa, näyttää, kuinkas muutenkaan, tekstiltä, joka läpäisisi ainakin esseesuorituksen minimivaatimukset. Ja jos luet tätä tekstiä vuoden tai parin päästä, vaikuttaa kenties tämäkin aikansa eläneeltä.

***

Tekoälyn käyttöön on reagoitu erilaisilla sääntelymalleilla. Lapin yliopiston ja ammattikorkeakoulun (LUC-konsernin) uusissa tekoälyohjeissa (2025) lähtökohta on seuraavanlainen: käyttö on lähtökohtaisesti sallittua, mutta opettaja voi sen kieltää, jos on riski siitä, että mallien käyttö haittaisi osaamistavoitteiden saavuttamista. (LUC-konserni 2025, 2.a k.)

Mallin perustana lienee ammattikorkeakoulujen rehtorineuvoston (Arenen) liikennevalomalli (Arene 2024):

Malli vaikuttaa selkeältä, mutta siinä on omat ongelmansa. Vihreän liikennevalon käyttökohteet lienevät sellaisia, joissa tekoälyn käytöstä linjaaminen on ylipäätään tarpeetonta. Punaisen liikennevalon noudattamista taas on mahdotonta valvoa: opettajalla ei ole keinoa tarkistaa, onko tekoälyä käytetty jossain vaiheessa esimerkiksi tiedon hankkimiseen tai tekstin tuottamiseen, jos tekstiä on kuitenkin sen jälkeen muokattu omin taidoin.

Myös keltainen liikennevalo herättää hämmennystä: mitä opettaja lopulta tekee sillä tiedolla, että tekoälyä on käytetty, ja mitä merkitystä sille tulisi antaa arvostelussa, jos käyttö on kuitenkin sallittua? Sininen valo taas soveltuu vain tietynlaisiin opintojaksoihin ja oppimismenetelmiin.

Liikennejärjestelyissä tuntuu siis jokin sakkaavan.

Jostain syystä valikoimasta puuttuu etenkin oranssi liikennevalo, joka sallisi tekoälyn käytön tiedon hakemiseen ja jäsentelyyn sekä pohjatekstin tuottamiseen mutta edellyttäisi lopullisen tekstin olevan opiskelijan itsensä tuottamaa tai ainakin muokkaamaa. Juuri tämähän on tapa, jolla tekoälyä on hyödyllisintä ja pedagogisesti mielekkäintä käyttää: tutkimusavustajana, ei työnohjaajana. Ja juuri tämä on se, minkä havaitsemiseen myös TurnItIn luotettavasti kykynee.

Oranssia lähestymistapaa edusti mielestäni perustellusti Lapin yliopiston aiempi tekoälyohjeistus. Sen mukaan "[k]ielimallien käyttö on lähtökohtaisesti sallittua" mutta niitä "ei saa käyttää tuottamaan lopullista tehtävän tai opinnäytetyön sisältöä eikä tällaista kielimallilla luotua sisältöä saa esittää opiskelijan itse tuottamana." (Lapin yliopisto 2023, 1 & 3 k.)

***

Lopulta kuvattu sääntely – tekniset ratkaisut ja korkeakoulun sisäiset ohjeet – on kuitenkin ongelman ratkaisemista pintatasolla. Sen alle hukkuvat perustavanlaatuiset kysymykset. Teknologia oikeastaan paljastaa ja haastaa sen, mitkä ovat opiskelemalla tavoitellut tiedot ja taidot sekä keinot, joilla kyseisiä seikkoja todennetaan.

Tekoäly siis paljastaa ne peruspilarit, joiden varaan opintojakson, opintokokonaisuuden, opetussuunnitelman, koulutusohjelman, tiedekunnan, yliopiston ja korkeakoulutuksen tiedon ja oppimisen filosofia rakentuvat.

Perinteisesti on kysytty, tavoitellaanko oppimisella sitä, että opiskelija muistaa keskeisiä seikkoja opittavasta asiasta (esimerkiksi: Millä edellytyksillä valtiosopimus on pätemätön?) vai sitä, että opiskelija osaa alansa taitoja (esimerkiksi: opiskelijalle esitetään kuvaus valtiosopimuksen laatimiseen johtaneesta menettelystä tai sen sisällöstä ja kysytään, onko valtiosopimus pätemätön). Tiedoksi on mielletty se, että opiskelija muistaa valtiosopimusoikeutta koskevan Wienin yleissopimuksen (SopS 33/1980) V osan määräykset, kun taas taidoksi se, että opiskelija osaa löytää kyseisen asiakirjan ja soveltaa sitä.

Pian kuitenkin voi olla aiheellista kysyä: tavoitellaanko oppimisella sitä, että opiskelija muistaa alallaan keskeisten tekoälytyökalujen toimintaperiaatteet ja osaa arvioida niiden tuottamien näkemysten paikkansapitävyyttä?

Tällainen muutos merkitsisi sitä, että sekä oppimisen sisällöllisiä tavoitteita että menetelmiä olisi arvioitava kauttaaltaan uudestaan.

Kuvatussa skenaariossa opettajan päänvaiva siitä, että generatiivista tekoälyä käsittelevä essee on n-prosenttisesti tekoälyn kirjoittamaa, vaikuttaa kaukaiselta. Selvityksenomaisia esseitä tuskin kirjoitettaisiin yliopistoissa lainkaan. Niiden laatiminen jäisi niiden alkuperäisen luonteensa – pohdiskeleva tekstilaji, joka on jossain kauno- ja tietokirjallisuuden välimaastossa – mukaisesti (edesmenneen) Erno Paasilinnan ja Antti Nylénin kaltaisten taiturien vastuulle.

Tai kenties jokaisen opiskelijan olisi ainakin eräillä opintojaksoilla löydettävä sisäinen filosofinsa ja esseistinsä ja antauduttava pohtimaan vaikkapa sitä, millä tavalla Jacques Ellulin kuvaus teknologisesta yhteiskunnasta (Ellul 1964) toteutuu oikeustieteen opinnoissa ja oikeudenkäytössä.

*Editorin kynästä -tunnuksella julkaistavat tekstit eivät ole blogin "pääkirjoituksia". Tunnus viestii, että tekstin kirjoittaja on tekstin julkaisuhetkellä blogia toimittava henkilö.

Lähteet:

Arene (2024) Arenen suositukset tekoälyn hyödyntämisestä ammattikorkeakouluille. https://arene.fi/julkaisut/raportit/arenen-suositukset-tekoalyn-hyodyntamisesta-ammattikorkeakouluille/ (luettu 25.6.2025).

Bennett Moses, Lyria (2007) Recurring Dilemmas: The Law's Race to Keep Up With Technological Change. University of Illinois Journal of Law, Technology and Policy, Fall, s. 239–285.

Ellul, Jacques (1964) The Technological Society. Vintage Books. Alkuteos (1954) La Technique ou l'Enjeu du siècle. Armand Colin.

Lapin yliopisto (2023) Tekoälypohjaisten työkalujen käyttö Lapin yliopistossa. https://www.ulapland.fi/loader.aspx?id=fe44e120-82a4-4365-a63d-dcf81ac7692c (luettu 25.6.2025).

LUC-konserni (2025) Tekoälyn käyttämistä opetuksessa sekä tutkimuksessa koskevat ohjeet lapin yliopistossa ja lapin ammattikorkeakoulussa https://ulapland.fi/wp-content/uploads/sites/3/2025/09/LUC-Tekoalyn-kaytto-opetuksessa-ja-tutkimuksessa.pdf (luettu 12.11.2025).

Valtiosopimusoikeutta koskeva Wienin yleissopimus (SopS 33/1980)

Mission Impossible: The Judge’s Role in Defining the RTBF regarding AI Applications

Author: Fouad Abdelrazek (LLD Candidate)

Research Group: Law, Technology and Design Thinking

Fouad Abdelrazek

Nowadays, the economic growth and prosperity of nations are increasingly linked to the deployment and efficacy of artificial intelligence (AI). The effectiveness of AI is directly proportional to the volume and quality of data available to it. As such, the more personal data that is fed into an AI system, the greater its accuracy and efficiency.[1]

Thus, the deletion of such data could significantly impact the efficiency and effectiveness of AI models. Accordingly, it could have severe implications for the economy in the long run.[2]

On the other hand, the issue of protecting personal data is a matter of utmost importance, as it is considered a fundamental human right.[3] Hence, the regulation of technology is a crucial aspect to ensure the protection of individuals. Nevertheless, it is important to guarantee that such regulations do not become an obstacle to development but rather support it.

Achieving the balance between these two interests is a complex matter that requires careful consideration and implementation of appropriate policies and regulations.

In my opinion, it also relies on the role of judges in interpreting the text of regulations. This ensures that regulations can be effectively applied to emerging technologies while also providing a level of flexibility necessary to promote innovation and development.

This significant role clearly appears in interpreting the right to be forgotten (RTBF), especially regarding AI applications. The RTBF is one of the most powerful rights that the General Data Protection Regulation (GDPR) has given under the name of the right to erasure (Article 17). This right gives EU and EEA residents the power to control their personal data.[4]

However, the concept of the RTBF presents a significant challenge in terms of its definition and implementation in relation to AI applications. This is because the requirement for data deletion, which is a fundamental aspect of the RTBF, is not easily applicable to AI systems. Unlike humans, AI systems and applications do not “forget” data in the same way, and the data deletion process in AI contexts is far more complex.[5] As a result, various conflicts and debates have emerged concerning the interpretation of the RTBF in the context of AI, making it a topic of significant academic interest.

There is an ongoing debate about interpreting “erasing” data differently, each with varying levels of difficulty to implement. A strict interpretation would demand erasing all copies of the data and removing them from any derived or aggregated representations to the extent that it is impossible to recover the data by any known technical means. This may not be feasible with some technologies. A more nuanced and pragmatic interpretation could permit encrypted data copies to persist as long as they remain indecipherable to unauthorized parties. A gentler and even more pragmatic interpretation could permit unencrypted data copies to last as long as they are no longer publicly visible in indices, database queries, or search engine results.[6]

Here, the judges have an essential role in interpreting the definition of the RTBF and directing the organization about how it should execute the verdict. This interpretation will directly impact AI.[7]

Roughly speaking, there are two methods of interpreting a legal text: the first is textualism, and the second is purposivism. Textualism is to stick to the statute's text in interpretation, whereas purposivism (or intentionalism) considers text-external purposes and legislator intentions.[8]

In this context, can judges’ emotional bias affect their interpretation of the RTBF to either decrease or increase the deletion of personal data to improve the economy?

People might unconsciously favour evidence that aligns with their existing viewpoints while disregarding or devaluing evidence that contradicts them.[9] From a classical legal realist perspective, the judge's decision can be biased without the judge knowing.[10] Despite judges' claims that their emotions do not impact their decisions,[11] it's unlikely that emotions cease to exist when they act in court. Emotions are a significant source of intuition, and their impact on decision-making is robust and valuable.[12] One judge has expressively stated, "Judges, being flesh and blood, are subject to the same emotions and human frailties as affect other members of the species."[13]

Hence, the issue of how judges interpret the RTBF in the context of AI is a complex and multifaceted one. The judgment of the European Court of Justice’s (ECJ) Google Spain case (C‑131/12) suggests that each case of the RTBF should be interpreted in its own context (judgement addressing Question 3, para. 99). This provides judges with much interpretive leeway in determining the meaning of the RTBF in the context of every case. However, this leeway may lead to different interpretations in similar cases.

Judges have to emphasize either of the two methods of interpreting a legal text to define the RTBF. However, interpretations of these two methods will raise different challenges for implementing the RTBF regarding AI.

On the one hand, under textualism, where the judge must adhere strictly to the statute's text, the text unequivocally calls for the erasure of the individual’s personal data. This may seem to have a harmful impact on the economy. It may lead to the erasure of a massive amount of data, which AI depends on in its efficiency, which will significantly impact the economy. However, are such verdicts technically executable in the first place? In some cases, it is very difficult to ensure that the personal data is erased from the model.[14] However, naturally, such an interpretation will increase trust in the judicial system, encouraging individuals, in turn, to give their personal data to these organizations.

On the other hand, a purposive interpretation might lead to a very broad interpretation of the text, which may negatively impact the trust between individuals and the judicial system. Through the lens of purposive interpretation, the RTBF may be interpreted such that data is not necessarily physically destroyed or overwritten; rather, it is merely made inaccessible or not readily retrievable through normal means. This could imply that, in practical terms, data marked for deletion in databases may still exist in some form and is merely concealed, awaiting potential overwriting in the future.[15] This will not lead to the actual erasure of personal data. Consequently, this will make individuals more reluctant to give their personal data to these organizations, which will affect the efficiency and accuracy of AI and also negatively impact the economy.

In conclusion, implementing the RTBF in the context of AI requires a nuanced and balanced approach. Considering this challenge, it would be useful if the Court of Justice of the European Union (CJEU) established clearer guiding criteria for judges to follow when interpreting the RTBF and its implementation, aiming to reach a balance between people's interests and the economy, especially in the context of AI. Although the ECJ presented its opinion, in practice, it is still debatable whether it was right or not. From this perspective, the lack of clear criteria for the RTBF, coupled with the rising number of cases and varying circuits that handle them, will result in a significant difference in interpretations of the RTBF in similar cases.

The existence of clear criteria would ensure that judgments are unified and consistent, ensuring trust and fairness, and avoiding conflicts and negative economic impacts.

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[1] Mangini, V., Tal, I., & Moldovan, A. N. (2020, August). An empirical study on the impact of GDPR and right to be forgotten  organisations and users perspective. In Proceedings of the 15th international conference on availability, reliability and security (pp. 1–9).

[2] Salami, E. (2023). Artificial Intelligence: The end of Legal Protection of Personal Data and Intellectual Property?: Research on the countering effects of data protection and IPR on the regulation of Artificial Intelligence systems.

[3] Rodotà, S. (2009). Data protection as a fundamental right. In Reinventing data protection? (pp. 77–82). Dordrecht: Springer Netherlands.

[4] Post, R. C. (2017). Data privacy and dignitary privacy: Google Spain, the right to be forgotten, and the construction of the public sphere. Duke LJ, 67, 981.

[5] Villaronga, E. F., Kieseberg, P., & Li, T. (2018). Humans forget, machines remember: Artificial intelligence and the right to be forgotten. Computer Law & Security Review, 34(2), 304–313.

[6] Sandra, I. A. The enforcement of right to be forgotten at the EU level by using search engines.

[7]Aghion, P., Jones, B. F., & Jones, C. I. (2018). Artificial intelligence and economic growth. In The economics of artificial intelligence: An agenda (pp. 237–282). University of Chicago Press. It is stated on the business Bank of America site that “AI will contribute more than $15 trillion to the global economy by 2030” https://business.bofa.com/en-us/content/economic-impact-of-ai.html#

[8] Aalto-Heinilä, M. (2016). Fairness in statutory interpretation: Text, purpose or intention?. International Journal of Legal Discourse, 1(1), 193–211.

[9] Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology, 2(2), 175–220.

[10] Wistrich, A.J. and Rachlinski, J.J., 2017. Implicit bias in judicial decision making how it affects judgment and what judges can do about it. Chapter, 5, pp.17–16; Epstein, D.Z., 2014. Rationality, legitimacy, & the law. Wash. U. Jurisprudence Rev., 7, p.1.

[11] Maroney, T. A. (2011). Emotional regulation and judicial behavior. Calif. L. Rev., 99, 1485.

[12] Wistrich, A. J., & Rachlinski, J. J. (2017). Implicit bias in judicial decision making how it affects judgment and what judges can do about it. Chapter, 5, pp. 17–16

[13] Maroney, T. (2016). The emotionally intelligent judge: A new (and realistic) ideal. Revista Forumul Judecatorilor, 61.

[14] Graves, L., Nagisetty, V., & Ganesh, V. (2020). Does AI Remember? Neural Networks and the Right to be Forgotten.

[15] Villaronga, E. F., Kieseberg, P., & Li, T. (2018). Humans forget, machines remember: Artificial intelligence and the right to be forgotten. Computer Law & Security Review, 34(2), 304-313.

Copyright Must Win

Author: Artha Dermawan (Indonesia-qualified Lawyer, Doctoral Student at the University of Lapland (Finland), funded by the Max Planck Institute for Innovation and Competition (Germany), and Visiting Researcher at the Australian Intellectual Property Institute, the University of Melbourne)[1]

Research Group: Law, Technology and Design Thinking

In the rapidly evolving landscape of technology and creativity, the advent of generative artificial intelligence (GenAI) has presented unprecedented challenges and opportunities to the domain of copyright law.[2] 

Artha Dermawan

At the heart of this technological revolution lies a critical question: does copyright, a centuries-old legal framework designed to protect the rights of creators, still hold its ground in encouraging and safeguarding creative works? This blog post argues affirmatively, positing that copyright is not only relevant but essential for the sustenance of human authors and the vibrancy of the economy, drawing lessons from the transformative impact of copyright in China's economic resurgence.[3] 

Copyright law was conceived as a delicate balance between the rights of creators to control and benefit from their creations and the public's interest in accessing knowledge and culture.[4]  This principle, rooted in the notion that incentivizing creativity through exclusive rights would lead to a richer cultural tapestry, has been the cornerstone of copyright since its inception.[5] 

However, the rise of GenAI, with its ability to produce original works reminiscent of human creativity, has stirred a debate on the relevance of copyright in the digital age. Critics argue that the ubiquity and efficiency of GenAI in generating outputs might render human creativity obsolete, undermining the economic rationale for copyright.[6] However, this perspective overlooks the intrinsic value of human authorship, which encompasses not only the creation of content but also the expression of human experience, emotions, and cultural nuances that GenAI cannot replicate. Copyright plays a pivotal role in ensuring that human authors are recognized and rewarded for their contributions, thereby motivating continued creative endeavors.[7]

The economic argument for copyright is further reinforced by the experience of China, where the introduction and enforcement of copyright laws have been instrumental in propelling the country's economy.[8] Prior to the adoption of robust copyright frameworks, piracy and the unauthorized use of intellectual property were rampant, stifling innovation and creativity. The enactment of copyright laws catalyzed a cultural and economic renaissance, fostering an environment where creativity could flourish, and innovators could reap the rewards of their labor. This transformation underscores the economic benefits of copyright in stimulating growth, encouraging investment in creative industries, and enhancing the country's competitive edge on the global stage.[9]

Moreover, copyright is not a zero-sum game that stifles innovation in the name of protectionism. On the contrary, it provides a structured framework within which innovation could thrive. The law allows for fair use, exceptions, and limitations that ensure the public's access to creative works while protecting creators' rights.[10] This equilibrium is crucial in the digital age, where the dissemination and remixing of content can serve as a catalyst for further creativity and innovation.

In the context of GenAI, copyright must evolve rather than be discarded. Legal frameworks need to adapt to the nuances of AI-generated outputs, distinguishing between outputs that are genuinely independent creations of GenAI and those that are derivative of human creativity. This differentiation is vital in ensuring that copyright continues to protect human authors without stifaring the potential of GenAI as a tool for augmenting human creativity.

In conclusion, the assertion that copyright must win in the era of GenAI is not a call for resistance against technological progress but a recognition of the enduring value of human creativity and its role in driving economic prosperity. As we navigate the complexities of the digital age, copyright remains a crucial mechanism for safeguarding human authors, ensuring that they are at the heart of the creative process, and sustaining the economic vitality of creative industries. Therefore, the preservation and adaptation of copyright laws are imperative for the continued flourishing of human creativity and economic development in the age of GenAI.

Copyright must win, and in the symphony of technological innovation, copyright remains our cultural compass, guiding the fusion of human creativity and economic prosperity.

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[1] This blog post is inspired by the policy framework proposed by the author in ‘AI v Copyright: How Could Public Interest Theory Shift the Discourse?’ published in volume 19(1) of the Journal of Intellectual Property Law & Practice in 2024 and ’Text and Data Mining Exceptions in the Development of Generative AI Models: What the EU Member States Could Learn From the Japanese “nonenjoyment” Purposes?’ Forthcoming in Journal of World Intellectual Property in 2024. The latter article was awarded second place in the 2022 Essay Competition organized by The International Association for the Advancement of Teaching and Research in Intellectual Property (ATRIP), sponsored by the International Federation of Intellectual Property Attorneys (FICPI). Unless specified otherwise, all internet references were last accessed on February 10, 2024.

[2] From the training data perspective, see, Artha Dermawan, ’Text and Data Mining Exceptions in the Development of Generative AI Models: What the EU Member States Could Learn From the Japanese “nonenjoyment” Purposes?’ (Journal of World Intellectual Property, 2024). On the egenral observations, see also, Annemarie Bridy, ‘Coding Creativity: Copyright and the Artificially Intelligent Author (5 Stanford Technology Law Review 1-28, 2012); Emmanuel Salami, ‘AI-generated Works and Copyright Law: Towards a Union of Strange Bedfellows’ (16(2) Journal of Intellectual Property Law & Practice 124-135, 2020); Artha Dermawan and Péter Mezei, ‘Artificial Intelligence and Consensus-Based Remuneration Regime in Southeast Asia’ (2023). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4625850; Mark Lemley and Bryan Casey, ‘Remedies for Robots’ (86 University of Chicago Law Review 1311-1396, 2019); Peter Yu, ‘The Algorithmic Divide and Equality in the Age of Artificial Intelligence’ (72 Florida Law Review 331-388, 2020); Vincenzo Iaia, ‘To Be, or Not to Be … Original Under Copyright Law, That Is (One of) the Main Questions Concerning AI-Produced Works’ (71(9), GRUR International, 793-812, 2022); Pamuela Samuelson, ‘Allocating Ownership Rights in Computer-Generated Works’ (47 University of Pittsburgh Law Review 1185-1188, 1986); Péter Mezei, ‘You AIn’t Seen Nothing yet' – Arguments against the Protectability of AI-generated Outputs by Copyright Law’ in Maurizio Borghi and Roger Brownsword (eds.), Informational Rights and Informational Wrongs: A Tapestry for Our Times (Routledge, Abingdon, 126-143, 2023).

[3] See, World Intellectual Property Organization (WIPO), ‘The Impact of Copyright on the National Economy Should not be Underestimated’ (2022). Available at: https://www.wipo.int/about-wipo/en/offices/china/news/2022/news_0027.html.

[4] Artha Dermawan, ‘AI v Copyright: How Could Public Interest Theory Shift the Discourse?’ (19(1) Journal of Intellectual Property Law and Practice, 2024) p. 56. The article underscores the necessity of defining clear objectives for copyright law, advocating for an evidence-based, consensus-driven, and morally grounded approach to balance creators' rights with public access to creative works.

[5] See, Christophe Geiger, ‘Freedom of Artistic Creativity and Copyright Law: A Compatible Combination?’ (8(3) UC Irvine Law Review, 2018).

[6] See, e.g., Anna Shtefan, ‘Creativity and Artificial Intelligence: A View from the Perspective of Copyright’ (Journal of Intellectual Property Law & Practice, 2021) pp. 720-728; Daniel J. Gervais, ‘The Human Cause’ in Ryan Abbott (ed.), Research Handbooks on Intellectual Property and Artificial Intelligence (Edward Elgar, 2022); Mark Lemley, ‘How Generative AI Turns Copyright Law on its Head’ (2023). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4517702.

[7] Artha Dermawan, n.4. See, Martin Senftleben, ‘Generative AI and Author Remuneration.’ (54 International Review of Intellectual Property and Competition Law, 2023). See, Nicola Lucchi, ‘ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems.’ (European Journal of Risk Regulation, 2023) pp. 17-21. See also, Christophe Geiger, To Pay or Not to Pay (for Training Generative AI), That is the Question, (JOTWELL, 2023) (reviewing Martin Senftleben, Generative AI and Author Remuneration, 54 International Review of Intellectual Property and Competition Law, 2023). Available at: https://ip.jotwell.com/to-pay-or-not-to-pay-for-training-generative-ai-that-is-the-question/.

[8] The relevance of copyright laws in boosting the Chinese economy is well-documented and multifaceted. According to the WIPO, innovation, creativity, and intellectual property, including copyright, are pivotal for economic recovery and sustainable development. The copyright industry significantly contributes to the GDP and employment, with China's copyright industry ranking among the top five globally. This industry not only plays a crucial role in China's innovation-driven development but has also positioned China from a follower to a leader in the global copyright arena. The development of industries with local characteristics, such as textiles and ceramics, showcases China's experience and contributions to global copyright governance, offering unique Chinese solutions to copyright challenges​. WIPO, ibid.

[9] Moreover, the theoretical rationales for IP protection emphasize the encouragement and reward for creative work. Copyrights and related rights cover literary and artistic works, granting legal protection to creators, thus fostering a culture of innovation and creativity. This protection is deemed crucial for stimulating inventive activities, with the protection of IP rights ensuring that creators can reap the benefits of their investments. In China, modern IPR regulation has evolved significantly since the 1980s, with improvements in legal frameworks and enforcement mechanisms aimed at fostering a conducive environment for the protection of intellectual property​. Muehlfeld Katrin and Wang Mei, ‘Intellectual Property Rights in China—A Literature Review on the Public's Perspective’ (7 Frontiers in Sociology, 2022). Available at: https://www.frontiersin.org/articles/10.3389/fsoc.2022.793165/full.

[10]  Artha Dermawan, n 4.

ChatGPT: A peek into the future of practical AI regulation

Author: Emmanuel Salami (Doctoral Researcher)

Research group: Law, Technology and Design Thinking 

Emmanuel Salami

Artificial Intelligence (AI) systems have received much attention from key participants in the global economy because of the unprecedented change apparent from their adoption. From a legal perspective, there is no paucity of legislative, judicial, regulatory, academic, and stakeholder position(s) on the topic. Even though there is evidence of AI use in various sectors of the global economy, it might be too early to describe such adoption as mainstream. [1] However, it would appear that this is about to change with the launch of ChatGPT.

ChatGPT is a state-of-the-art natural language processing model developed by OpenAI. It is a variant of the GPT-3 (Generative Pertained Transformer 3) model, which has been trained on a massive amount of text data to generate human-like responses to a given input.[2] ChatGPT uses unsupervised machine-learning techniques to create responses. In other words, it can generate responses without the machine learning algorithm being trained to respond in any particular way.

This notwithstanding, human input is needed to curate the information and thereby guide its output. Furthermore, ChatGPT makes AI readily accessible to the public, thereby creating a potential avenue for unravelling, on a large scale, some critical legal concerns previously expressed about AI systems. Therefore, this blog post focuses on some Intellectual Property Rights (IP, IPR) and data protection law concerns that might arise in using ChatGPT.

ChatGPT might raise some exciting IP considerations concerning its output. This is because the datasets used to train the AI system at the machine learning phase must have been generated through the works of authors who are most probably unaware of it. Though ChatGPT is proving to be very good at mixing and matching, time will tell if we potentially have a copyright action on our hands, should it replicate works attributable to other authors.

A relatable concern has been raised by Australian artists who accused an AI system that creates art of infringing on their artwork. [3] Their rationale is that their artwork had been used to train the AI system, and its elements are evident in the AI-generated art. It is arguable that, at some point, something like this might be possible, especially when it comes to AI-generated literary works such as (non) fiction books.

One of the schools of thought justifying IPR posits that its purpose includes the incentivisation of authors and the encouragement of innovation. [4] ChatGPT’s (potential) use of copyrighted works without adequate consideration for the incentivisation of authors can potentially hinder the ‘author incentivisation’ objective of IPR. Furthermore, IPR accords all authors moral rights in their works, which is an inalienable right to be consistently recognised as the author of the work. [5] ChatGPT, and by extension, AI’s (potential) use of copyrighted works, threatens this IPR principle. In addition, OpenAI has created an avenue in its terms and conditions for infringed copyrighted works to be taken down from the platform. [6] However, this is neither a sufficient attempt to incentivise authors nor resolve the IPR concerns identified above.

Despite the output of ChatGPT essentially being non-personal and publicly available data, data protection law remains relevant in its use. However, it would appear that processing (potentially sensitive) personal data does not take the data retention principle into proper consideration. To avoid doubt, the data retention principle (also known as the storage limitation principle) simply requires that personal data should neither be retained nor capable of identifying natural persons longer than necessary in relation to the purpose(s) of processing. [7]

The retention of personal data in ChatGPT raises two concerns for the data retention principle: firstly, when users delete their account on the platform, all information about the account is deleted, and they will be unable to reopen another account. Users are therefore encouraged to deactivate their accounts, making their data available on the platform. [8] The implication is that users might be forced to keep their data on the platform to avoid being prevented from creating an account in future.

Of course, the platform may claim that the legal basis for retaining the account details upon deactivation (instead of deletion) is the user’s consent. However, such consent is invalid because it is non-voluntary. After all, the user has no other option. [9] It is frivolous to assert that such retention is justifiable by the performance of a contract since it is not necessary for such performance. [10] Secondly, some data categories entered into the ChatGPT system cannot be deleted. [11] Although users are advised not to enter sensitive data into the system, this does not resolve the data retention concern, especially because such erroneously entered data will likely be available for machine learning purposes.

The scenarios highlighted above also raise some interesting concerns from the perspective of the data minimisation and purpose limitation principles which cannot be fully addressed within the scope of this blog post. Flowing from the concerns identified above, one can say that the terms and conditions and the privacy policy of ChatGPT are quite superficial and non-transparent and do not sufficiently address these concerns. If ChatGPT is to become a mainstream application, these concerns (and more) must be addressed, particularly in the EU, due to its extensive (proposed) legislation regulating personal data and AI. The lack of transparency becomes even more worrisome, given how well ChatGPT has been received and the proposed intention of some global tech players to incorporate it into their products. [12]

From an epistemic perspective, ChatGPT (like most other AI systems today) only recreates information from other existing data and is incapable of creating new knowledge. This is because it lacks the human consciousness needed for knowledge creation. As Zittrain notes, AI systems (including Chat GPT) “don’t uncover causal mechanisms, they are at best statistical correlation engines”, unlike human intelligence, which is needed for the investigation of problems and their causal effects. [13] ChatGPT’s status as a “statistical correlation engine” is one reason behind some of the superficial and wrong answers it has been known to provide. In addition, it cannot discern and verify the validity/correctness of the information, although this may also result from training the system with error-prone data. This highlights the risks of importing real-world errors and biases into the realm of AI, resulting in the propagation of misinformation. Therefore, it is necessary to ensure that some form of human review is mandatory in using ChatGPT.

As identified above, ChatGPT has some hurdles to surpass if it is to be adopted without legal and regulatory challenges. It is necessary to carefully consider these issues so that AI adoption does not result in the erosion of user rights. While the frenzy surrounding AI is understandable, developers will do well to sustain this excitement by ensuring that their products comply with applicable laws.

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^[1]  See for instance - Next Rembrandt. <https://www.nextrembrandt.com/> accessed 06/01/2023.

^[2]  Shripad Kulkarni, Generative Pre-trained Transformer 3 by OpenAI. <https://link.medium.com/Rcb57QuWpwb> accessed 08/01/2023.

^[3] Cait Kelly, Australian artists accuse popular AI imaging app of stealing content, call for stricter copyright laws, (The Guardian.com, 11/12/2022). <https://www.theguardian.com/australia-news/2022/dec/12/australian-artists-accuse-popular-ai-imaging-app-of-stealing-content-call-for-stricter-copyright-laws?CMP=share_btn_link> accessed 08/01/2023.

^[4] Annette Kur and Thomas Dreier, European Intellectual Property Law: Text, Cases and Materials (Edward Elgar Publishing 2013) 5–10.

^[5] Art 6bis Berne convention.

^[6] OpenAI, Terms of Use, paragraph 3(d). <https://openai.com/terms/> accessed 9/01/2022.

^[7] Art 5(1) (e) GDPR.

^[8] Chat GPT FAQ, paragraph 7 <https://help.openai.com/en/articles/6783457-chatgpt-faq> accessed 9/01/2022.

^[9] Art 4(11) and Art 7 GDPR.

^[10] Art 6 (1) (b) GDPR.

^[11] Chat GPT FAQ, (n 8) paragraph 8.

^[12] Ryan Browne, Microsoft reportedly plans to invest $10 billion in creator of buzzy A.I. tool ChatGPT, (January 10, 2023, CNBC). <https://www.cnbc.com/2023/01/10/microsoft-to-invest-10-billion-in-chatgpt-creator-openai-report-says.html> accessed 11 January 2023.

^[13] Jonathan Zittrain, ‘The Hidden Costs of Automated Thinking’ (The New Yorker, 23 July 2019) <https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking> accessed 11 January 2023.

 

eHealth and AI: How to control our sensitive personal data?

Author: Rob van den Hoven van Genderen (Professor in Artificial Intelligence and Robot Law)

Research group: Law, Technology and Design Thinking

In a growing, worldwide increase of aging population and a fundamental lack of suitable medical personal, eHealth  AI technology, can be a considerable help to support the flaws in care and medical support. eHealth is considered to be the next step in medical industry and medical communication on every level, from lifestyle advice to surgery and communication of medical data between professionals as well as between patients or governmental health authorities. But what happens with those sensitive data?

In this next step of the health industry the use of AI will speed up the pace of all those applications. Massive amounts of data can be analyzed for diagnosis of diseases and ways to cure them, but AI also can be used to profile certain groups within the population to qualify them for cheaper or more expensive health insurance or – on the negative side- even could result in expelling people from necessary care. Also, it could be possible that choices and decisions for treatment between patients will be based on the outcome of AI analysis where the necessary human factor will not be present, resulting in doubtful ethical results. The combination of AI in robotics for medical assistance and treatment, although considered useful, can create doubts about the de-humanization and the required attention for meaningful human control. AI will certainly increase the efficiency in healthcare but is that the most important aspect of healthcare? Will the proposed AI Regulation be a stimulus or an objection to use AI for medical applications?

The medical profession is bound by the Hippocratic oath to follow the ethical as well as practical rules to do no harm. It even gives rules to protect the privacy of patients:

“Whatever I see or hear in the lives of my patients, whether in connection with my professional practice or not, which ought not to be spoken of outside, I will keep secret, as considering all such things to be private.”

These basic values should also be part of practicing medical professions in a wide sense using new technologies as artificial intelligence (AI) and robotics. Pandora’s Box is opened to an unlimited and intrusive number of applications concerning eHealth. AI will be available on different levels, for professionals and supporting patients who need care by telemetry, telemedicine, connected to caretakers and virtual or human doctors and specialists. It can be used to follow elderly people to mitigate risks or analyze movements of people in case of contagious diseases (Covid!). AI generated Apps can recognize tumors, nano- robots can remove them. AI will propose medical care actions. 

As eHealth covers a wide spectrum of smart applications. the EU recognizes that AI will have an immense influence on eHealth in all its aspects. Social care, medical services, medtech industry.[1]

Next to the positive effects: direct actions and control for the people who need them there can be detrimental effects on loss of independency, physical integrity and privacy.

These data are specified in the General Data Protection Regulation (GDPR) as sensitive data and data-subjects need to give severe and explicit permission for those data to be processed, based on requirements of transparency, explainability and informed consent. Is that even possible using AI?

In the EU white paper on AI[2] these worries were also ventilated on the present AI developments, requiring an independent supervisory system and liability rules for developing and using AI taking into account the risks and dangers concerning misuse and vulnerability of personal data and possible bias and discrimination.

These fears also came up with the use of ehealth data processed by the use of the ‘green-covid-19 certificate’ (European Covid passport) on international and national base.

The proposed AI Regulation, a solution?

It will be very hard to supervise all use of eHealth data in conformity with the requirements of the GDPR but also the proposed European AI-regulation will make it very difficult to exchange AI generated ehealth data by governmental agencies  as well as other actors in the medical sector, in particular insurance, as risk-based impact use of AI is forbidden in article 5 of the AI Regulation.[3]

Creating practices and regulations to share more ehealth data with the use of AI can be the solution. Processing and sharing of ehealth data should be made possible taking into account the information sovereignty of the sensitive data subject. This also would be in line with another draft Regulation on Data sharing (Data Governance Act), in which it a.o. is stated to stimulate data sharing a low intensity regulatory intervention would require that individual public sector bodies allowing re-use of data to be technically equipped to ensure that data protection, privacy and confidentiality are fully preserved.[4] Let us see how that will work out.

Notes:

[1] Digital health and care refers to tools and services that use information and communication technologies (ICTs) to improve prevention, diagnosis, treatment, monitoring and management of health and lifestyle. Digital health and care have the potential to innovate and improve access to care, quality of care, and to increase the overall efficiency of the health sector.

[2] White Paper on Artificial Intelligence -A European approach to excellence and trust(COM(2020) 65 final, available at: https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf

[3] Artificial Intelligence Act Com/2021/206 Final, available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

[4] COM(2020) 767 final 2020/0340(COD), available at: https://eur-lex.europa.eu/procedure/EN/2020_340

STAR, Smart Tissue Autonomous Robot Children's National Health System, copyright NOS, available at: https://nos.nl/artikel/2103207-onder-het-mes-bij-dokter-robot