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AI Ethics - Critical Reflections on Embedding Ethical Frameworks in AI Technology

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Culture and Computing. Design Thinking and Cultural Computing (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12795))

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Abstract

Embedding ethical frameworks in artificial intelligence (AI) technologies has been a popular topic for academic research for the past decade [1,2,3,4,5,6,7]. The approaches of the studies differ in how AI technology, ethics, role of technical artefacts and socio-technical aspects of AI are perceived. In addition, most studies define insufficiently what the connection between the process of embedding ethical frameworks to AI technology and the larger framework of AI ethics is. These deficiencies have caused that the concept of AI ethics and the construct of embedding ethical parameters into AI are used in an ambiguous, rather than in a complementary manner.

One reason for the ambiguity within this field of research is due to a lack of a comprehensive conceptual framework for AI ethics in general. I intend to fill this void by grounding AI ethics as a subfield of philosophy of technology and applied ethics and presenting its main issues of study by examining recognized spheres of activities through the method of levels of abstraction [8]. I put forward an initial hierarchical conceptual framework for AI ethics as an outcome. After this, I discuss the connection between the process of embedding ethical frameworks in AI and the larger AI ethics framework, leading to presenting basic requirements for the sphere of activity hereafter known as embedded ethics.

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Notes

  1. 1.

    Even though Floridi and Taddeo focus on the information sphere, they examine it as phenomena emerging from the combination of data, algorithms and hardware and software applications [10].

  2. 2.

    Floridi and Taddeo most likely understand this risk, since they propose that practices related to responsible research and innovation should be considered when examining important practices related to data ethics.

  3. 3.

    The goals are always defined by human operators.

  4. 4.

    This is the case for Moral Machines (2008), which I will elaborate in the third chapter of this paper.

  5. 5.

    The research community has a responsibility to explicitly elaborate what their research considers. Not stating what the current development phase of technology is and not informing when one’s paper considers theoretically possible, but unlikely scenarios, researchers legitimize pseudo problems, such as the closeness of singularity.

  6. 6.

    Human activities always take place in social contexts, which are shaped by varying cultural, historical and political backgrounds [2, 24].

  7. 7.

    The design phase is about bringing abstract ideas to exist in the real world, but to understand it only as a phase of a product’s lifecycle is not enough for applied ethics.

  8. 8.

    Technical artifacts consist of the artifact (tangible or intangible) and its use-plan[30,31,31]. This definition shows how the artefacts are always a means to instantiate human intentions.

  9. 9.

    By wider, I mean that the concept of sociotechnical stretches to refer to relations outside of mere user(s) - technical artefact(s) relation.

  10. 10.

    Even in the situation that the few people have good intentions, the strong narrative that technology development is a morally neutral activity [13, 14], and the fact that a few people cannot in any shape perceive the needs and desires of large populations water down the possibility of accepting that kind of power to a small group of people [13, 23, 35]. Therefore, enabling meaningful societal discourse is one of the corner stones of (AI) deployment ethics [12, 32].

  11. 11.

    It is important to notice that actors with different roles require different level of intelligibility [2, 28]. For example, operators of an AI system require understandable information explaining states relevant for the used basic functions, whereas engineers taking part in the AI systems redesign require explanations that reach the underlying phases of information processing behind the functions.

  12. 12.

    A typed variable is a variable that can hold only certain explicitly stated data. Data in this case can refer to either symbols of empirical perception or symbols related to purely conceptual theories [8].

  13. 13.

    There are two types of information that are possible to depict by the levels of abstraction method: analogous and discrete. Analogous information refers to information used in natural sciences to depict the basis of natural phenomena. In analogous information the observables can take infinite number of values and their behavior is described with differential equations. The other type of information is discrete, meaning that the observables have a finite number of values they can take [8]. This research considers discrete information.

  14. 14.

    Surjective means that an abstract observation can be traced back to at least one concrete counterpart. Its strict meaning would allow only a single concrete counterpart per abstract observation but as Floridi points out, abstract information in the field of humanities is often traced back as a connection between several concrete counterparts.

  15. 15.

    AI4people is an Atomium European Institution for Science, Media, and Democracy (EISMD) initiative which pursues to produce frameworks for a good AI society. For more information see https://www.eismd.eu/ai4people/.

  16. 16.

    Opportunities can turn to missed opportunities if AI is underused for the sake of misleading argumentation.

  17. 17.

    Some aspects of the governance ethics LoA may be perceived to best serve its meaning if they were regulated nationally or by an intergovernmental covenant [40], but that is a whole other discourse and out of the scope of this article.

  18. 18.

    It would be more accurate to talk about prejudice discrimination, since discrimination in its broad meaning refers to distinguishing groups of information from a mass of data. Therefore, discrimination in its broad meaning is a non-separational function of AI.

  19. 19.

    Compare to the concept of meaningful human control as a HCI grand challenge [46].

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  1. Cognitive Science, Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Henrikki Salo-Pöntinen

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    Matthias Rauterberg

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Salo-Pöntinen, H. (2021). AI Ethics - Critical Reflections on Embedding Ethical Frameworks in AI Technology. In: Rauterberg, M. (eds) Culture and Computing. Design Thinking and Cultural Computing. HCII 2021. Lecture Notes in Computer Science(), vol 12795. Springer, Cham. https://doi.org/10.1007/978-3-030-77431-8_20

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