Search results

The purpose of this paper is to explore the challenges of implementing accountable artificial intelligence (AI) systems in India, focusing on the need for algorithms to justify their decisions, especially in subjective and complex scenarios. By analyzing various government projects, documented biases and conducting empirical case studies and experiments, the study highlights the limitations of AI in recognizing the nuances of India’s unique social landscape. It aims to underscore the importance of integrating political philosophy to ensure that AI systems are held accountable within India’s sociopolitical context, urging policymakers to develop frameworks for responsible AI decision-making.

The research adopts a mixed-methods approach to address the five research questions. It begins with an extensive literature review, focusing on AI’s transformative potential, algorithmic bias and accountability in the Indian context. Data is collected from 15 AI use cases in health care, education and public safety, 13 government automated decision tools and five bias cases, including facial recognition and caste-based discrimination. Additionally, ten case studies and three experiments on ChatGPT are analyzed. Content analysis is used to interpret and categorize the data, identifying patterns and themes. Specific case studies and experiments on autocompletion in search engines further support the findings.

The study revealed significant limitations in current AI systems when applied to India’s complex socio-cultural landscape. Analyzing 15 AI applications and 13 government projects, the research identified multiple instances of algorithmic bias. Experiments with Google’s autocomplete and ChatGPT showed that these systems often reinforce social stereotypes and struggle with nuanced, subjective situations. The findings emphasize the accountability gap in AI-driven decisions, highlighting the need for rigorous oversight, particularly in welfare projects where errors could lead to severe consequences. The study recommends developing regulatory frameworks, improving AI design and raising public awareness to address these challenges.

In the context of complex societies like India, a pressing concern arises: who should assume responsibility for the repercussions stemming from algorithmic failures to comprehend subjective complexities? To this end, there exist no serious scholarly works toward which present paper tries to shed new insights. It draws upon insights from the corpus of political philosophy literature, encompassing both classical and contemporary notions of responsibility, and seeks to establish connections between these concepts and the unique sociopolitical structure of India. The work is unique in the focus of the paper and is original in the direction projected.

This article explores the use of natural language processing (NLP) techniques and machine learning (ML) models to discover underlying concepts of gender inequality applied to artificial intelligence (AI) technologies in female social media conversations. The first purpose is to characterize female users who use this platform to share content around this area. The second is to identify the most prominent themes among female users’ digital production of gender inequality concepts, applied to AI technologies.

Social opinion mining has been applied to historical Twitter data. Data were gathered using a combination of analytical methods such as word clouds, sentiment analyses and clustering. It examines 172,041 tweets worldwide over a limited period of 359 days.

Empirical data gathered from interactions of female users in digital dialogues highlight that the most prominent topics of interest are the future of AI technologies and the active role of women to guarantee gender balanced systems. Algorithmic bias impacts female user behaviours in response to injustice and inequality in algorithmic outcomes. They share topics of interest and lead constructive conversations with profiles affiliated with gender or race empowerment associations. Women challenged by stereotypes and prejudices are likely to fund entrepreneurial solutions to create opportunities for change.

This study does have its limitations, however. First, different keywords are likely to result in a different pool of related research. Moreover, due to the nature of our sample, the largest proportion of posts are from native English speakers, predominantly (88%) from the US, UK, Australia and Canada. This demographic concentration reflects specific social structures and practices that influence gender equity priorities within the sample. These cultural contexts, which often emphasize inclusivity and equity, play a significant role in shaping the discourse around gender issues. These cultural norms, preferences and practices are critical in understanding the individual behaviours, perspectives and priorities expressed in the posts; in other words, it is vital to consider cultural context and economic determinants in an analysis of gender equity discussions. The US, UK, Australia and Canada share a cultural and legal heritage, a common language, values, democracy and the rule of law. Bennett (2007) emphasizes the potential for enhanced cooperation in areas like technology, trade and security, suggesting that the anglosphere’s cultural and institutional commonalities create a natural foundation for a cohesive, influential global network. These shared characteristics further influence the common approaches and perspectives on gender equity in public discourse. Yet findings from Western nations should not be assumed to apply easily to the contexts of other countries.

From a practical perspective, the results help us understand the role of female influencers and scrutinize public conversations. From a theoretical one, this research upholds the argument that feminist critical thought is indispensable in the development of balanced AI systems.

The results also help us understand the role of female influencers: ordinary individuals often challenged by gender and race discrimination. They request an intersectional, collaborative and pluralistic understanding of gender and race in AI. They act alone and endure the consequences of stigmatized products and services. AI curators should strongly consider advocating for responsible, impartial technologies, recognizing the indispensable role of women. This must consider all stakeholders, including representatives from industry, small and medium-sized enterprises (SMEs), civil society and academia.

This study aims to fill critical research gaps by addressing the lack of a socio-technical perspective on AI-based decision-making systems, the shortage of empirical studies in the field and the need for a critical analysis using feminist theories. The study offers valuable insights that can guide managerial decision-making for AI researchers and practitioners, providing a comprehensive understanding of the topic through a critical lens.

Drawing upon the authors’ experiences as minoritised academic scholars within leadership roles of a Black, Asian, Minority Ethnic (BAME) Network in the United Kingdom (UK) academe, the authors explored the research question “In what ways do racially minoritised academics use coping techniques and strategies to counter racism and inequality in the higher education environment”.

The authors used a collective autoethnography approach accompanied by storytelling, underpinned by a qualitative interpretative process, supported by inductive, data-driven theorising. The authors’ approach is supplemented by the usage of content analysis (Schrieier, 2012) to analyse the data and generate findings.

The research findings specifically highlight (1) collectivism, solidarity and belonging, (2) knowledge expansion and critical consciousness, (3) disarming approaches and emotional labour, (4) resistance through setting boundaries and (5) intersectionality and BAME men allyship, as specific approaches for taking forward anti-racism.

Autoethnographic research has encountered challenges around verification, transparency and veracity of data, and issues have been debated due to its subjective nature (see Jones, 2010; Keeler, 2019; Méndez, 2013). Additional complications arise regarding neutrality and objectivity associated with the researchers' identities and experiences being represented in autoethnographic accounts. The authors acknowledge that the accounts provided are subjective, and have influenced the research process and product.

Research on the experiences of minoritised academics leading staff equality networks constitutes a research gap. This article offers an original analysis through outlining the authors’ lived experiences in leadership positions of a BAME Network and hope to other minoritised employees undertaking anti-racist work.

The objective of this paper is to explore the development of the Blackmail-as-a-Service business model within the Digital Thrift Shop. This service model involves the sale of doxing files to customers, as well as the removal of the content from the shop and its dissemination to victims or individuals closely connected to them.

To access the Digital Thrift shop, this study relies on the Tor browser and a darknet indexing site. The authors then use an application to collect and store the web pages as PDFs. Finally, content analysis is performed on these PDFs to understand the Blackmail-as-a-Service business model developed by the Digital Thrift.

The doxing data available on the Digital Thrift is primarily targeted toward women. Digital Thrift has also established a way to value the purchase, sale and the removal of blackmail data. The presence of Blackmail-as-a-Service on darknet sites poses unique challenges for criminal justice agencies in terms of jurisdiction, due to the lack of harmonized laws and the obstacles involved in taking down content from darknet sites. Finally, the use of a service model for blackmail allows criminals without technical skills to engage in cyber-victimization using blackmail.

Research into Blackmail-as-a-Service from boutique providers has not been conducted. To the best of the authors’ knowledge, this is one of the first study seeking to understand the Blackmail-as-a-Service business model on the darknet when used to target adults. This study presents evidence of a lack of connection between the buyers of the compromising material and the potential victim, challenging preconceived notions about image-based sexual abuse and its connection to individuals involved in interpersonal relationships.