Zoekresultaten

The hackable city

Staging the Oriental Other

For German-speaking tourists, an Oriental market (in Arabic: souq) is an exotic place representing the ‘Otherness’. Referring to this Oriental context, the article aims to answer the following questions: What are the tourists’ imaginaries and social narratives and what is the role that cultural brokers play? Gaining insight into the imaginaries and on-site performances of German-speaking tourists of a mega-cruise liner will contribute to the discussion of imaginaries and embodied performances in general as well as the mediation and the construction of space. The research reported upon in the article is part of a larger field study (2012–2014) in Souq Muttrah, the oldest and formerly main market in Oman. Participant observation, photography and in-depth interviews with different types of tourists, local customers, cultural brokers and on-board employees were conducted and marketing material was analysed. Results indicate that in the marketing material, the tourists are already beginning to travel backwards in time. During their visit to the souq, the multi-sensory performances and embodied imaginaries are enhanced by stories of the Arabian Nights. Cultural brokers play an essential role in ‘localizing’ the tourist experience. They adjust their own identities and direct the tourists’ performances at different stages, similar to an Oriental theme park, for example, they stop at a frankincense shop.

Centre for Design Informatics: Blockchain City

A lab of labs

Coding, prompting, “deprompting”

Prompt design can be understood similarly to query design, as a prompt aiming to understand cultural dimensions in visual research, forcing the AI to make sense of ambiguity as a way to understand its training dataset and biases ( Niederer, S. and Colombo, G., ‘Visual Methods for Digital Research’). It moves away from prompting engineering and efforts to make “code-like” prompts that suppress ambiguity and prevent the AI from bringing biases to the surface. Our idea is to keep the ambiguity present in the image descriptions like in natural language and let it flow through different stages (degrees) of the broken telephone dynamics. This way we have less control over the result or selection of the ideal result and more questions about the dynamics implicit in the biases present in the results obtained.Different from textual or mathematical results, in which prompt chains or asking the AI to explain how it got the result might be enough, images and visual methods assisted by AI demand new methods to deal with that. Exploring and developing a new approach for it is the main goal of this research project, particularly interested in possible biases and unexplored patterns in AI’s image affordances.How could we detect small biases in describing images and creating based on descriptions when it comes to AI? What exactly do the words written by AI when describing an image stand for? When detecting a ‘human’ or ‘science’, for example, what elements or archetypes are invisible between prompting, and the image created or described?Turning an AI’s image description into a new image could help us to have a glimpse behind the scenes. In the broken telephone game, small misperceptions between telling and hearing, coding and decoding, produce big divergences in the final result - and the cultural factors in between have been largely studied. To amplify and understand possible biases, we could check how this new image would be described by AI, starting a broken telephone cycle. This process could shed light not just into the gap between AI image description and its capacity to reconstruct images using this description as part of prompts, but also illuminate biases and patterns in AI image description and creation based on description.It is in line with previous projects on image clustering and image prompt analysis (see reference links), and questions such as identification of AI image biases, cross AI models analysis, reverse engineering through prompts, image clustering, and analysis of large datasets of images from online image and video-based platforms.The experiment becomes even more relevant in light of the results from recent studies (Shumailov et al., 2024) that show that AI models trained on AI generated data will eventually collapse.To frame this analysis, the proposal from Munn, Magee and Arora (2023) titled Unmaking AI Imagemaking introduces three methodological approaches for investigating AI image models: Unmaking the ecosystem, Unmaking the data and Unmaking the outputs.First, the idea of ecosystem was taken for these authors to describe socio-technical implications that surround the AI models: the place where they have been developed; the owners, partners, or supporters; and their interests, goals, and impositions. “Research has already identified how these image models internalize toxic stereotypes (Birnhane 2021) and reproduce forms of gendered and ethnic bias (Luccioni 2023), to name just two issues” (Munn et al., 2023, p. 2).There are also differences between the different models that currently dominate the market. Although Stable Diffusion seems to be the most open due to its origin, when working with images with this model, biases appear even more quickly than in other models. “In this framing, Stable Diffusion becomes an internet-based tool, which can be used and abused by “the people,” rather than a corporate product, where responsibility is clear, quality must be ensured, and toxicity must be mitigated” (Munn et al., 2023, p. 5).To unmaking the data, it is important to ask ourselves about the source and interests for the extraction of the data used. According to the description of their project “Creating an Ad Library Political Observatory”, “This project aims to explore diverse approaches to analyze and visualize the data from Meta’s ad library, which includes Instagram, Facebook, and other Meta products, using LLMs. The ultimate goal is to enhance the Ad Library Political Observatory, a tool we are developing to monitor Meta’s ad business.” That is to say, the images were taken from political advertising on the social network Facebook, as part of an observation process that seeks to make evident the investments in advertising around politics. These are prepared images in terms of what is seen in the background of the image, the position and posture of the characters, the visible objects. In general, we could say that we are dealing with staged images. This is important since the initial information that describes the AI ​​is in itself a representation, a visual creation.

Socio-cultural impacts of large-scale cruise tourism in Souq Mutrah, Sultanate of Oman

The following paper explores socio-cultural impacts of large-scale cruise liner tourism on the traditional bazaar (souq) in the district of Mutrah. The souq is located opposite the port in the Omani capital Muscat. Large-scale cruise tourism in Muscat started only in 2004 and has increased in scale and numbers in the past years. 24 cruise vessels with around 7600 passengers arrived in Muscat in 2005. Seven years later 135 cruise liners carrying 257,000 tourists docked in Muscat. Due to this dramatic rise of international cruise ships, the socio-cultural impacts have increased for local residents, shop vendors/owners and tourists alike. To capture those socio-cultural impacts on Souq Mutrah, a survey of cruise tourists was conducted by a questionnaire. In addition, the researcher used participatory observation, counting, and in-depth interviews with different stakeholders of the local community and different types of tourists during the cruise seasons 2012/13 and 2013/14. Moreover, content analysis of statistics and local media publications were used. Results indicate that the souq has become "the core of a tourist bubble", where crowding is a major problem and local residents avoid the place. The social carrying capacity of the souq has been reached. Omani vendors are leaving their businesses and renting their shops out to expatriates. Since contemporary cruise tourists are low spenders, expatriate shop sellers have become more aggressive.