You never fake alone. Creative AI in action

ABSTRACT

Creative AI (notably GANs and VAEs) can generate convincing fakes of video footage, pictures, graphics, etc. In order to conceptualize the societal role of creative AI a new conceptual toolbox is needed. The paper provides metaphors and concepts for understanding the functioning of creative AI. It shows how the role of creative AI in relation to FAT ideals can be enriched by a dynamic and constructivist understanding of creative AI. The paper proposes to use Greimas’ actantial model as a heuristic in the operationalization of this type of understanding of creative AI.

KEYWORDS:

• Generative adversarial networks (GANs) and variational autoencoders (VAEs)
• fairness
• accountability and transparency in machine learning (FATML)
• generative machine learning
• deepfakes
• synthetic data

Acknowledgements:

I would like to express my deepest gratitude to the two anonymous reviewers whose thoughtful comments have been invaluable to this paper. I presented an early draft of this paper in December 2018 at the Data, Security, Values: Vocations and Visions of Data Analysis – Annual Conference of the Nordic Centre for Security Technology and Societal Values (NordSTEVA). A big thank you goes to Mareile Kaufman for inviting me as a keynote, for her unwavering commitment as an editor and her supportive comments. I also would like to express my gratitude for the financial and collegial support I received from the Sociology of Law Department, Lund University (postdoc grant, Cre-AI project; 01/02/2019-01/09/2019) and the Swedish Law and Informatics Research Institute, Stockholm University (postdoc grant, Cre-AI project; 01/09/2019-01/06/2020). Last but not least, I would like to thank my colleague Liane Colonna for taking the time to read and comment during the pressured circumstances caused by covid-19.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributor

Katja de Vries is a postdoctoral legal researcher and philosopher of technology focusing on Machine Learning and Artificial Intelligence. Currently she is working on CRE-AI. CRE-AI is a two year postdoc project (Department of Law, Stockholm University, Sweden, September 2019–2021) on the legal and societal implications of so-called creative or generative Artificial Intelligence, notably Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). She is also affiliated to Lund University (Sociology of Law) and the Vrije Universiteit Brussel (Center for Law, Science, Technology and Society) “[email: [email protected]]”.

Notes

1 See initiatives such as https://weverify.eu/ (last accessed 31 January 2020).

2 Not all supervised learning is discriminative, nor is all unsupervised learning is generative, but a dominant fraction is.

3 It should be noted though that supervised learning is more labor-intensive in terms of labeling than unsupervised learning. The labeling of data requires a lot of human input, exemplified by the emergence of labeling farms where human workers spent all day tagging ML input images.

4 The notion of ‘variation’ is well-developed in music and genetics. Both these notions of variation can provide interesting analogies with the variations produced with creative AI.

5 http://refikanadol.com/works/archive-dreaming/ (last accessed 20 January 2020).

6 Annual FATML conference: https://www.fatml.org/ (last accessed 2 February 2020).