Transparency in gender data analysis

Gender-sensitive data collection and estimation are essential for producing statistics that reflect the realities of both women and men. This training module was developed under the Asia-Pacific Network of Statistical Training Institutes to provide statisticians, researchers and civil society with practical guidance on integrating gender perspectives into data processes, from collection to estimation and analysis . This module covers the following key aspects: – Rationale and learning objectives for mainstreaming gender in data systems – Integration of gender considerations in censuses, administrative records, registries and household surveys – Specific guidance for time-use surveys and violence against women surveys, addressing design, sampling and interviewer training – Common gender biases in data processes and strategies to minimise them through careful design and training – Methods for gender data estimation, including identifying research questions, applying international standards and developing tabulation plans – Use of internationally agreed metadata and repositories (UNSD, ILO, WHO, UNESCO, FAO) to align concepts and methods – Recommendations for multi-level sex disaggregation and intersectional analysis across population groups The content emphasises that gender must be integrated at all stages of statistical work—from questionnaire design and sample selection to interviewer training and coding—to avoid bias and ensure relevance. By using international standards, engaging gender specialists and applying careful disaggregation, the module equips practitioners to generate more accurate, inclusive and policy-relevant gender statistics that can inform sustainable development and social equity.

Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models The International Research Centre on Artificial Intelligence (IRCAI), under the auspices of UNESCO, in collaboration with UNESCO HQ, has released a comprehensive report titled “Challenging Systematic Prejudices: An Investigation into Gender Bias in Large Language Models”. This groundbreaking study sheds light on the persistent issue of gender bias within artificial intelligence, emphasizing the importance of implementing normative frameworks to mitigate these risks and ensure fairness in AI systems globally. "...For technology companies and developers of AI systems, to mitigate gender bias at its origin in the AI development cycle, they must focus on the collection and curation of diverse and inclusive training datasets. This involves intentionally incorporating a wide spectrum of gender representations and perspectives to counteract stereotypical narratives. Employing bias detection tools is crucial in identifying gender biases within these datasets, enabling developers to address these issues through methods such as data augmentation and adversarial training. Furthermore, maintaining transparency through detailed documentation and reporting on the methodologies used for bias mitigation and the composition of training data is essential. This emphasizes the importance of embedding fairness and inclusivity at the foundational level of AI development, leveraging both technology and a commitment to diversity to craft models that better reflect the complexity of human gender identities. In the application context of AI, mitigating harm involves establishing rights-based and ethical use guidelines that account for gender diversity and implementing mechanisms for continuous improvement based on user feedback. Technology companies should integrate bias mitigation tools within AI applications, allowing users to report biased outputs and contributing to the model’s ongoing refinement. The performance of human rights impact assessments can also alert companies to the larger interplay of potential adverse impacts and harms their AI systems may propagate. Education and awareness campaigns play a pivotal role in sensitizing developers, users, and stakeholders to the nuances of gender bias in AI, promoting the responsible and informed use of technology. Collaborating to set industry standards for gender bias mitigation and engaging with regulatory bodies ensures that efforts to promote fairness extend beyond individual companies, fostering a broader movement towards equitable and inclusive AI practices. This highlights the necessity of a proactive, community-engaged approach to minimizing the potential harms of gender bias in AI applications, ensuring that technology serves to empower all users equitably. https://lnkd.in/eTyr6XTn

🚀 𝐍𝐞𝐰 𝐑𝐞𝐩𝐨𝐫𝐭: 𝐖𝐡𝐲 𝐆𝐞𝐧𝐝𝐞𝐫 𝐃𝐚𝐭𝐚 𝐢𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈! 🤖📊 AI is shaping our world—but without gender-sensitive data, it risks perpetuating bias and deepening inequalities. The latest Friedrich-Ebert-Stiftung report, "𝐆𝐞𝐧𝐝𝐞𝐫 𝐃𝐚𝐭𝐚: 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐢𝐭 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬?", by Payal Arora & Weijie Huang, explores the critical role of gender data in ensuring fair, ethical, and inclusive AI development. 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫? 📌 AI systems rely on data for decision-making, but biased datasets reinforce stereotypes and exclude diverse experiences. 📌 Gender data is key to addressing inequalities in healthcare, employment, and education, where AI-driven decisions can disproportionately disadvantage women and marginalized groups. 📌 Barriers to gender data collection—including binary norms, lack of transparency, and structural discrimination—must be dismantled to create equitable AI systems. 📢 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐝𝐨𝐧𝐞? 🔎 The report calls for algorithmic transparency, inclusive data frameworks, cross-sectoral collaboration, and feminist ethics to ensure AI systems are designed with fairness and equity in mind. If we want AI to serve all of society, we must start with inclusive and representative data. This report is a must-readfor anyone working at the intersection of gender, AI, and social justice! 📖 You can download the paper from the post. #GenderData #AIandInclusion #EthicalAI #SocialJustice #DataForEquality #ArtificialIntelligence #FeministAI #InclusiveTech

The United Nations Sustainable Stock Exchanges Initiative (SSE)Market Monitor: Gender Equality Disclosure Metrics (2025) report highlights how stock exchanges and standard-setting organizations such as #ESRS, #GRI, and #SASB guide companies in disclosing gender equality performance. The goal is to assess how well these metrics align with the UN’s Women’s Empowerment Principles (WEPs). The findings show that while most stock exchanges encourage gender-related disclosures, they still lack the consistency and depth found in global reporting standards. The analysis reveals that stock exchanges tend to focus on internal company issues such as leadership representation and fair treatment (WEP 1 and 2), while gender aspects across supply chains and community empowerment (WEP 5 and 6) are rarely reported. Frameworks like GRI and ESRS include broader gender indicators such as pay gap and inclusive policies, whereas SASB applies gender metrics to only a few sectors. As a result, available data remain difficult to compare across companies or countries, and often fail to capture real progress toward SDG 5. The report recommends three key actions: first, making gender-related metrics more visible and accessible through centralized guidance; second, agreeing on a consistent global methodology to ensure comparability and credibility of data; and third, fostering collaboration among exchanges, regulators, and standard setters to establish shared principles for harmonized disclosure. Thoughts? Detail: https://sseinitiative.org