For Better or for Worse? A Framework for Critical Analysis of ICT4D for Women

• URL: http://arxiv.org/abs/2108.09947v1
• Date: Mon, 23 Aug 2021 05:42:24 GMT
• Title: For Better or for Worse? A Framework for Critical Analysis of ICT4D for Women
• Authors: Abhipsa Pal and Rahul De'
• Abstract summary: As ICT diffusion widens, there is a persistent threat of widening the gender-based digital divide. This paper develops a critical research framework for a gender-focused examination of ICT4D studies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion of ICTs provide possibilities for women empowerment by greater participation and enhanced gender-based digital equality. However, a critical analysis reveals that as ICT diffusion widens, there is a persistent threat of widening the gender-based digital divide and exposes women to online sexual abuses, predominantly in developing countries characterized by the gendered nature of the social structure. Instead of accepting ICT as the facilitator to women empowerment, in this paper, we develop a critical research framework for a gender-focused examination of ICT4D studies. Critical research methodology provides the appropriate ontology unveiling social realities through challenging the status quo and exposing the deeper societal inequalities. Using the critical research framework developed, we investigate past ICT4D initiatives and artifacts from literature and draw critical conclusions of its benefits and issues. This study would aid future ICT4D research to investigate areas of gender discrimination and understand the role of ICTs in a critical light.

Related papers

• Thinking beyond Bias: Analyzing Multifaceted Impacts and Implications of AI on Gendered Labour [1.5839621757142595]
  This paper emphasizes the need to explore AIs broader impacts on gendered labor. We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work.
  arXiv  Detail & Related papers  (2024-06-23T20:09:53Z)
• A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
  We propose a multitask learning approach to enhance the performance of the misogyny identification systems. We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups. This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
  arXiv  Detail & Related papers  (2024-06-22T15:06:08Z)
• Information Forensics and Security: A quarter-century-long journey [66.16120845232525]
  Information Forensics and Security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes. For over a quarter century since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era.
  arXiv  Detail & Related papers  (2023-09-21T15:13:35Z)
• Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications [0.0]
  The study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of Large Language Models. The paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques.
  arXiv  Detail & Related papers  (2023-07-18T11:38:45Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• Investigating Participation Mechanisms in EU Code Week [68.8204255655161]
  Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and use of digital technologies. The aim of the manuscript is to offer a detailed and comprehensive statistical description of Code Week's participation in the EU Member States.
  arXiv  Detail & Related papers  (2022-05-29T19:16:03Z)
• Assessing Gender Bias in the Information Systems Field: An Analysis of the Impact on Citations [0.0]
  This paper outlines a study to estimate the impact of scholarly citations that female IS academics accumulate vis-a-vis their male colleagues. By doing so we propose to contribute knowledge on a core dimension of gender bias in academia, which is, so far, almost completely unexplored in the IS field.
  arXiv  Detail & Related papers  (2021-08-22T18:18:52Z)
• SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection [9.443571652110663]
  We propose the first Chinese sexism dataset -- Sina Weibo Sexism Review (SWSR) dataset -- and a large Chinese lexicon SexHateLex. SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type. We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models.
  arXiv  Detail & Related papers  (2021-08-06T12:06:40Z)
• Fair Representation Learning for Heterogeneous Information Networks [35.80367469624887]
  We propose a comprehensive set of de-biasing methods for fair HINs representation learning. We study the behavior of these algorithms, especially their capability in balancing the trade-off between fairness and prediction accuracy. We evaluate the performance of the proposed methods in an automated career counseling application.
  arXiv  Detail & Related papers  (2021-04-18T08:28:18Z)
• Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation [32.74513588794863]
  Value Card is an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics.
  arXiv  Detail & Related papers  (2020-10-22T03:27:19Z)
• Beyond STEM, How Can Women Engage Big Data, Analytics, Robotics and Artificial Intelligence? An Exploratory Analysis of Confidence and Educational Factors in the Emerging Technology Waves Influencing the Role of, and Impact Upon, Women [0.0]
  The professional participation of women in technology, big data, analytics, artificial intelligence and information systems related domains remains proportionately low. We identify ways for learning and self-efficacy as key factors, which together lead us to the Advancement of Women in Technology (AWT) insights framework. Based on the AWT framework, we also proposition principles that can be used to encourage higher professional engagement of women in emerging and advanced technologies.
  arXiv  Detail & Related papers  (2020-03-26T05:12:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.