“Misogynoir” is a term that refers to the anti-Black forms of misogyny that Black women experience. To explore how current automated hate speech detection approaches perform in detecting this type of hate, we evaluated the performance of two state-of-the-art detection tools, HateSonar and Google’s Perspective API, on a balanced dataset of 300 tweets, half of which are examples of misogynoir and half of which are examples of supporting Black women and an imbalanced dataset of 3138 tweets of which 162 tweets are examples of misogynoir and 2976 tweets are examples of allyship tweets. We aim to determine if these tools flag these messages under any of their classifications of hateful speech (e.g. “hate speech”, “offensive language”, “toxicity” etc.). Close analysis of the classifications and errors shows that current hate speech detection tools are ineffective in detecting misogynoir. They lack sensitivity to context, which is an essential component for misogynoir detection. We found that tweets likely to be classified as hate speech explicitly reference racism or sexism or use profane or aggressive words. Subtle tweets without references to these topics are more challenging to classify. We find that the lack of sensitivity to context may make such tools not only ineffective but potentially harmful to Black women.

Global debates on artificial intelligence (AI) ethics and governance remain dominated by high-income, AI-intensive nations, marginalizing perspectives from low- and middle-income countries and minoritized practitioners. This qualitative study adopts a decolonial and sociotechnical lens to examine how AI practitioners across Africa, Asia, South America, the Caribbean, and minoritized groups working in high-income contexts conceptualize AI’s value, harms, and governance. Drawing on reflexive thematic analysis of 22 in-depth interviews, the study explores how geographic, cultural, and professional contexts shape practitioners’ understandings of ethics, harm, and power within the global AI ecosystem. Findings reveal a dual orientation. While some participants view AI as a neutral tool shaped by human intent, others frame it as a sociotechnical system that reproduces structural inequities through data colonialism, exclusion, and epistemic dependency. Despite these asymmetries, participants articulated cautious yet agentic imaginaries of AI’s potential to address local and regional problems in healthcare, education, and public governance. The study advances decolonial AI ethics by empirically grounding how ethical reasoning and governance are negotiated under constraint and by highlighting pathways toward more equitable, context-sensitive global AI governance.

In this paper, we propose a set of best practices for the responsible adoption of Generative Artificial Intelligence (AI) in higher education. These best practices provide a comprehensive framework for higher education institutions to effectively and ethically integrate Generative AI into their teaching and learning practices. The framework prioritises a responsible and human-centred approach, alongside pedagogical soundness, careful planning, transparency, as well as content quality. By exploring the responsible adoption of Generative AI in higher education, we seek to provide scalable, personalised learning experiences for large cohorts of students. Our research focuses on harnessing Generative AI to offer tailored educational content and generate constructive feedback for students. Additionally, by adhering to responsible AI practices, we aim to address challenges such as misinformation, copyright violations, and bias.

In Industry 5.0 it is essential that humans are in the loop of technology integration of industry processes. With the advancements of Generative Artificial Intelligence (GenAI), a lot of new opportunities and challenges for learning and teaching are present. Many students already use publicly available AI Digital Assistants (p-AIDA) like ChatGPT for academic purposes. However, there are concerns around the use of such p-AIDA tools, particularly in terms of academic integrity, data privacy, intellectual property, and the impact on the quality of education. Furthermore, many higher education institutions have substantial learning materials and data about students that they may not want to share with p-AIDA. Therefore, using the Technology Acceptance Model (TAM) and following a Design-Based Research (DBR) approach, we explored the perspectives and experiences of a beta-test of an institutionally developed AIDA (i-AIDA) with 18 UK students using multiple methods and data sources (including pre-post-test, interviews, think-aloud, and prompt analysis). Our research underscores the potential benefits and limitations of in-house i-AIDA in enhancing learning experiences without compromising academic integrity or privacy, and how higher education institutions can prepare themselves for Industry 5.0.

The study sought to ascertain how the Farmer Business School (FBS) introduced in 2012 by the Ghana Cocoa Board as an extension approach makes the Ghanaian cocoa farmer more business and entrepreneurially minded. The research methods included using the descriptive survey and multi-stage sampling techniques to cover the six cocoa regions and 600 cocoa farmers. The 600 cocoa farmers selected were made up of 230 non-participants and 370 participants of the FBS. In terms of competency, the results showed that FBS participants had greater knowledge, a more positive attitude, and better skills than non-participants. In terms of market orientation, FBS participants were more competitor oriented, had less intelligence generation, were less market responsive, had less intelligence dissemination, had more customer emphasis, and had more interfunctional coordination than non-participants. The market orientation of cocoa farmers can be enhanced by provision of relevant practical experience for attitudinal change.

“Misogynoir" is the anti-Black racist misogyny experienced by Black women, which is characterised by components of both racism and sexism. Misogynoir is challenging to detect due to its inherent subjectivity and its intersectional nature, and people’s opinions and interpretations of such hate might vary, which adds to the challenges of understanding it. In this paper, we explored how and some potential why’s different annotator characteristics influence how they interpret and annotate a dataset for potential cases of Misogynoir and Allyship. We sampled tweets containing public responses to self-reported misogynoir cases by four prominent Black women in technology, designed an online annotation task study, and recruited annotators of diverse ethnicities and genders from the Prolific crowdsourcing platform. We found that participants’ sources of evidence in judging and interpreting content for potential cases of Misogynoir and Allyship, even in circumstances where they all agree on a prospective label, vary across different factors, such as different ethnicity, lived experiences and gender. In addition, we present a variety of plausible interpretations influenced by the various annotators’ characteristics. This study demonstrates the relevance of different annotator perspectives and content comprehension in hate speech and the need for further efforts to understand intersectional hate better.