Datafication, Artificial Intelligence and Images: The Dominant Paradigm in the Representation of Knowledge in Images

被引:0
|
作者
Rosa, Pedro Cremonez [1 ]
Barizon Filho, Antonio Lucio [1 ]
Valentim, Raquel Torrao [2 ]
Tognoli, Natalia [2 ]
机构
[1] Univ Estadual Londrina, Grad Program Informat Sci PPGCI UEL, Londrina, PR, Brazil
[2] Fluminense Fed Univ, Grad Program Informat Sci PPGCI UFF, Niteroi, RJ, Brazil
来源
KNOWLEDGE ORGANIZATION | 2024年 / 51卷 / 02期
关键词
Artificial intelligence; image; knowledge representation; dominant paradigm;
D O I
10.5771/0943-7444-2024-2-117
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
This paper aims to verify whether Generative Artificial Intelligence tools for image generation replicate biases and social stereotypes present in the dominant paradigm. A case study was carried out using the Leonardo.Ai tool, which generated images using simple combined terms, namely: "Scientist, person"; "Cook, person"; "Doctor, person"; "CEO, person"; "Housekeeper, person"; and "Nurse, person". The images were analyzed using Rodrigues' (2007) image documentary analysis methodology and Gemma Penn's (2008) contributions. The analysis criteria included gender, age group, ethnicity, body type, clothes, and circumscribed elements. The images generated by the Leonardo.Ai tool were found to have a series of characteristics that perpetuate bias and social stereotypes. Given the likely impact that generative Artificial Intelligence will have on the production and sharing of information, we conclude that, in addition to the ethical issues related to the creation of the tool itself, there is a need to discuss ways of making it more inclusive and responsible for the representation of information.
引用
收藏
页码:117 / 126
页数:10
相关论文
共 50 条
  • [1] Demographic Representation of Generative Artificial Intelligence Images of Physicians
    Lee, Sang Won
    Morcos, Mary
    Lee, Dong Won
    Young, Jason
    [J]. JAMA NETWORK OPEN, 2024, 7 (08)
  • [2] Supporting artificial intelligence with artificial images
    Aurdal, Lars
    Brattli, Alvin
    Glimsdal, Eirik
    Klausen, Runhild Aae
    Lokken, Kristin Hammarstrom
    Palm, Hans Christian
    [J]. TARGET AND BACKGROUND SIGNATURES IV, 2018, 10794
  • [3] An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems
    Al-Tahhan, F. E.
    Fares, M. E.
    [J]. MICROSCOPY RESEARCH AND TECHNIQUE, 2024, : 3089 - 3106
  • [4] Multiple Knowledge Representation of Artificial Intelligence
    Pan, Yunhe
    [J]. ENGINEERING, 2020, 6 (03) : 216 - 217
  • [5] Evaluating the Diversity and Representation of Artificial Intelligence-Generated Images of Radiologists: An Observational Study
    Onuh, Ifeanyi
    Yi, Paul H.
    Bui, Molinna
    Kuckelman, Ian J.
    Anderson, Jade A.
    Ross, Andrew B.
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2024, 21 (09) : 1497 - 1500
  • [6] Artificial Intelligence and the Problem of Computer Representation of Knowledge
    Inozemtsev, Vladimir
    Ivleva, Marina
    Ivlev, Vitaly
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON CONTEMPORARY EDUCATION, SOCIAL SCIENCES AND HUMANITIES (ICCESSH 2017), 2017, 124 : 1151 - 1157
  • [7] ARTIFICIAL-INTELLIGENCE AND PHILOSOPHY - THE KNOWLEDGE OF REPRESENTATION
    DASCAL, M
    [J]. SYSTEMS RESEARCH, 1989, 6 (01): : 39 - 52
  • [8] ARTIFICIAL-INTELLIGENCE, HISTORY AND KNOWLEDGE REPRESENTATION
    LEE, G
    LELOUCHE, R
    MEISSONNIER, V
    ORNATO, M
    ZARRI, GP
    ZARRIBALDI, L
    [J]. COMPUTERS AND THE HUMANITIES, 1982, 16 (01): : 25 - 34
  • [9] ARTIFICIAL-INTELLIGENCE, THE PROBLEM OF KNOWLEDGE REPRESENTATION
    DREYFUS, H
    [J]. VIA, 1988, 9 : 90 - 101
  • [10] Artificial intelligence generated leukemia cell images
    Fan, Bingwen Eugene
    Chen, David Tao Yi
    Ponnudurai, Kuperan
    Abdul Latiff, Siti Thuraiya Binte
    Wong, Moh Sim
    Ong, Yi Xiong
    Lim, Kian Guan Eric
    Wong, Wei Yong Kevin
    Chiang, Pik Wan Erica
    Lim, Shu Ping
    Shanmugam, Hemalatha
    Neo, Yuan Shan
    Kwok, Ngai Tung Eric
    Winkler, Stefan
    [J]. AMERICAN JOURNAL OF HEMATOLOGY, 2023, 98 (07) : 1160 - 1162