Female perspectives on algorithmic bias: implications for AI researchers and practitioners
Bibliographic record
Abstract
Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".