Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series We don't want to go to jail. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Bluesky .

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Phoebe Waller-Bridge is the most celebrated British TV creator of her generation. Her blistering 2016 traumedy broke big the moment it crossed the Atlantic via co-producer Prime Video, fueling rapturous reviews and spirited discourse about the comedy of female sexuality. A few years later, the surprisingly metaphysical second season dominated the Emmys, made " Hot Priest " Andrew Scott a sex symbol, and even got a shout-out from Barack Obama .

The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these models easily available and accessible. While prior research has identified biases in large language models, this paper considers biases contained in the most popular versions of these models when applied'out-of-the-box' for downstream tasks. We focus on generative language models as they are well-suited for extracting biases inherited from training data. Specifically, we conduct an indepth analysis of GPT-2, which is the most downloaded text generation model on HuggingFace, with over half a million downloads per month. We assess biases related to occupational associations for different protected categories by intersecting gender with religion, sexuality, ethnicity, political affiliation, and continental name origin. Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in USLabor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations. This raises the normative question of what language models should learn - whether they should reflect or correct for existing inequalities.

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An'Intimacy Crisis' Is Driving the Dating Divide In his book, sex and relationships researcher Justin Garcia says people have miscalculated their need for human intimacy, which is the real issue at root of the loneliness epidemic. In the US, nearly half of adults are single. A quarter of men suffer from loneliness. Rates of depression are on the rise . And one in four Gen Z adults--the so-called kinkiest generation, according to one study --have never had partnered sex. In an age of endless connection, where hooking up happens with the ease of a swipe and nontraditional relationship structures like polyamory are celebrated, why are people seemingly so disconnected and alone?

In recent years, significant advancements in the field of Natural Language Processing (NLP) have positioned commercialized language models as wide-reaching, highly useful tools. In tandem, there has been an explosion of multidisciplinary research examining how NLP tasks reflect, perpetuate, and amplify social biases such as gender and racial bias. A significant gap in this scholarship is a detailed analysis of how queer sexualities are encoded and (mis)represented by both NLP systems and practitioners. Following previous work in the field of AI fairness, we document how sexuality is defined and operationalized via a survey and analysis of 55 articles that quantify sexuality-based NLP bias. We find that sexuality is not clearly defined in a majority of the literature surveyed, indicating a reliance on assumed or normative conceptions of sexual/romantic practices and identities. Further, we find that methods for extracting biased outputs from NLP technologies often conflate gender and sexual identities, leading to monolithic conceptions of queerness and thus improper quantifications of bias. With the goal of improving sexuality-based NLP bias analyses, we conclude with recommendations that encourage more thorough engagement with both queer communities and interdisciplinary literature.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. When it comes to sex in 2025--who's having it, who isn't, and how--perceptions are all over the place. Is Gen Z sliding back in time? Are middle-aged women finally having good sex, or none at all? And what exactly is going on with seniors in retirement homes? In the series Pillow Talk, we interview one person in a specific time and place in their lives about what sex looks like for them and their peers, in every enlightening (and excruciating) detail. Get in touch if you have an idea for a subject--or if you have a story to tell.

One way social groups are marginalized in discourse is that the narratives told about them often default to a narrow, stereotyped range of topics. In contrast, default groups are allowed the full complexity of human existence. We describe the constrained representations of queer people in LLM generations in terms of harmful representations, narrow representations, and discursive othering and formulate hypotheses to test for these phenomena. Our results show that LLMs are significantly limited in their portrayals of queer personas.

Evaluating language models inherit biases through both their biases across multiple languages is critical as training and alignment processes (Feng et al., 2023; LLMs trained in one linguistic and cultural context Scherrer et al., 2024; Motoki et al., 2024). Identifying may not generalize fairly or accurately to others, the opinions and values that LLMs possess has leading to culturally inappropriate or biased outputs been a particularly intriguing area of research, as it when used globally. Our multilingual experiments carries significant sociological and quantitative implications further reveal that models exhibit different for real-world applications (Naous et al., biases in their secondary languages, such as Arabic 2023). Understanding the biases embedded in these and Chinese, which underscores the importance of powerful tools is crucial, given their widespread cross-linguistic evaluations in understanding bias use and the potential influence they may exert on resilience. Furthermore, we introduce a comprehensive users, often in unintended ways (Hartmann et al., human evaluation to compare how humans 2023) or in downstream tasks, such as content moderation.