Textbooks are important for shaping children's understanding of the world. Previous studies of individual countries have suggested that gender inequality exists. There lacks a study that compares gender inequality in textbooks around the world. This study uses NLP approaches to quantify gender inequality in English textbooks in 7 cultural spheres, 22 countries, by measuring the count, firstness, and TF IDF words by gender. The study also counted the names that appeared in TF IDF word lists and sorted the names by gender, found out that LLMs can distinguish between the different TF IDF word lists, and mapped the TF IDF words to GloVe to see that some keywords are closer to one gender than the other. The study found more male count, firstness, and names. The study found that there is significant gender inequality in all the textbooks. Gender inequality is demonstrated the least in textbooks of the Latin Cultural Sphere.

This study addresses the persistent challenges of Workplace Gender Equality (WGE) in Indonesia, examining regional disparities in gender empowerment and inequality through the Gender Empowerment Index (IDG) and Gender Inequality Index (IKG). Despite Indonesia's economic growth and incremental progress in gender equality, as indicated by improvements in the IDG and IKG scores from 2018 to 2023, substantial regional differences remain. Utilizing k-means clustering, the study identifies two distinct clusters of regions with contrasting gender profiles. Cluster 0 includes regions like DKI Jakarta and Central Java, characterized by higher gender empowerment and lower inequality, while Cluster 1 comprises areas such as Papua and North Maluku, where gender disparities are more pronounced. The analysis reveals that local socio-economic conditions and governance frameworks play a critical role in shaping regional gender dynamics. Correlation analyses further demonstrate that higher empowerment is generally associated with lower inequality and greater female representation in professional roles. These findings underscore the importance of targeted, region-specific interventions to promote WGE, addressing both structural and cultural barriers. The insights provided by this study aim to guide policymakers in developing tailored strategies to foster gender equality and enhance women's participation in the workforce across Indonesia's diverse regions.

Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit which is generally documented in detail in the court proceedings. Via a substantial corpus of 17,306 court proceedings, this paper investigates gender inequality through the lens of divorce court proceedings. While emerging data sources (e.g., public court records) on sensitive societal issues hold promise in aiding social science research, biases present in cutting-edge natural language processing (NLP) methods may interfere with or affect such studies. We thus require a thorough analysis of potential gaps and limitations present in extant NLP resources. In this paper, on the methodological side, we demonstrate that existing NLP resources required several non-trivial modifications to quantify societal inequalities. On the substantive side, we find that while a large number of court cases perhaps suggest changing norms in India where women are increasingly challenging patriarchy, AI-powered analyses of these court proceedings indicate striking gender inequality with women often subjected to domestic violence.

Study finds that just 8% of all depictions of AI professionals from 100 years of film are women – and half of these are shown as subordinate to men. Researchers from the University of Cambridge argue that such cultural tropes and a lack of female representation affects career aspirations and sector recruitment. Without enough women building AI there is a high risk of gender bias seeping into the algorithms set to define the future, they say. The team from the University's Leverhulme Centre for the Future of Intelligence (LCFI) whittled down over 1,400 films to the 142 most influential cinematic works featuring AI between 1920 and 2020, and identified 116 characters they classed as "AI professionals". Of these, 92% of all AI scientists and engineers on screen were men, with representations of women consisting of a total of eight scientists and one CEO.

For the 3rd episode of our interview series with AI experts, we had a great conversation with Anna Bethke! Anna Bethke is a Principal Data Scientist focused on fair, accountable, transparent, &…

Gender-neutral internet searches yield results that nonetheless produce male-dominated output, finds a new study by a team of psychology researchers. Moreover, these search results have an effect on users by promoting gender bias and potentially influencing hiring decisions. The work, which appears in the journal Proceedings of the National Academy of Sciences (PNAS), is among the latest to uncover how artificial intelligence (AI) can alter our perceptions and actions. "There is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded," says Madalina Vlasceanu, a postdoctoral fellow in New York University's Department of Psychology and the paper's lead author. "As a consequence, their use by humans may result in the propagation, rather than reduction, of existing disparities."

In this post, I'm going to explain about the gender inequality with AI. I will provide some examples and a case study to address this issue. Let's first know about the AI system and it's working. Artificial Intelligence (AI) mimics human intelligence and use computers to analyse, classify and make predictions from the data. Machine Learning (ML) is the capability of an AI system to improve continuously through experiences.

Since 2010, in the post #Metoo world, the causes and consequences of gender inequalities have come under increasing scrutiny from academics, policy-makers, consumers and the general public. Also during the last decade, concerns about the diffusion of artificial intelligence (AI) have attracted increased attention in the public debate. AI is a "general purpose technology" (GPT), the advances of which create a drop in prediction costs, especially thanks to the "machine learning" domain (Agrawal, Gans & Goldfarb, 2019), meaning the use of data to make predictions. One area that will strongly be impacted by AI is the labor market, a market where gender inequalities have been particularly studied by social scientists. The gender wage gap (the average difference between the wages of men and women) has been deconstructed to investigate the role of attributes (for example differences between men and women in years of education, occupational choices, years of experience…) and the role of discrimination (different effects of the same attributes).

Throughout history, women have faced discrimination. This Inequality has been a long-present social evil. Whether it be voting rights or access to equal healthcare women have stood up against social evils whenever they see this inequality creeping in. But what if they are unaware that discrimination of some form is happening to them how will that be rectified. This is the problem that today's closed guarded black box AI algorithms pose towards society.

In 2015, it was found out that the algorithm used for hiring employees for Amazon was biased. It was trained on the number of resumes submitted over the past ten years, and due to the existing gender gap in the industry, as the number of male candidates was higher than female candidates, the algorithm also favored males. In a similar case in the UK, a gymnasium wrongly assumed that a woman was a man, just because she was a doctor. The algorithm used titles of the members to allot a fitting room. The algorithm had accidentally learned that the title of Doctor is given to men, which resulted in this error.