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- Laaksoharju, Mikael, 1977-, et al.
(författare)
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What is the problem to which AI chatbots are the solution? : AI ethics through Don Ihde's embodiment, hermeneutic, alterity, and background relationships
- 2024
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Ingår i: Ethics and Sustainability in Digital Cultures. - Oxon : Routledge. - 9781032434643 - 9781003367451 ; , s. 31-48
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Bokkapitel (refereegranskat)abstract
- Almost overnight at the end of 2022, ChatGPT became a hot topic – a technology that seemed to “change everything.” In this essay, we analyze the ethical implications of artificial intelligence (AI) chatbots, such as ChatGPT, by circling around the question “how do we live well together with AI chatbots and its ilk?”. We focus on how technology mediates relationships between humans, technology, and the world, drawing on Ihde’s embodiment, hermeneutic, alterity, and background relationships. Rather than placing AI chatbots into one of the categories, we see how they can be seen as belonging to all four forms of relationships with their corresponding ethical issues. In embodiment relationships, AI chatbots can be used to relate to the world, for example as a digital assistant. In hermeneutic relationships, AI chatbots can let you read the world and provide a constant diagnosis of statements, courses of action, etc. In alterity relationships, AI chatbots can become your always available speaking partner. In background relationships, AI chatbots contribute to introducing generalized doubt and highlighting the world as something to be prompted. An observation is also that while there are many benefits of chatbots within embodiment, hermeneutic, and alterity relationships, there are significant drawbacks within the background relationships.
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- Oestreicher, Lars, 1962-, et al.
(författare)
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From Visual Forms to Metaphors : Targeting Cultural Competence in Image Analysis
- 2022
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Ingår i: Proceedings of the 6th Digital Humanities in the Nordic and Baltic Countries Conference (DHNB 2022). ; , s. 343-351
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Konferensbidrag (refereegranskat)abstract
- Image analysis has taken a large step forward with the development within machine learning. Today, recognizing images as well as constituent parts of images (faces, objects, etc.) is a relatively common task within machine learning. However, there is still a big difference between recognizing the content of a picture and understanding the meaning of the image. In the current project we have chosen an interdisciplinary approach to this problem, including art history, machine learning and computational linguistics. Current approaches pay large attention to details of the image when trying to describe what is in the picture, resulting, e.g., in that smiling faces will support the interpretation of the image as “positive” or “happy”, even if the picture itself is a scary scene. Other problematic issues are irony and other polyvalent messages with a large amount of ambiguity that enables for example humorous interpretations of a picture. As a starting point, we have chosen to identify visual agency, i.e., how and why pictures, when regarded as acting agents, effectively may catch the attention of the viewer. Our objective for this first phase of the project is to investigate multi-modal models’ capacity for recognizing such high-level image content as, for example, context, agency, visual narration, and metaphors. Ultimately, the goal is to improve cultural competence and visual literacy of neural networks through art-historical and humanities expertise. In the paper we will describe our current approach, the general ideas behind it, and the methods that will be used.
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