Large Language Models (LLMs) are trained primarily on minimally processed web text, which exhibits the same wide range of social biases held by the humans who created that content. Consequently, text generated by LLMs can inadvertently perpetuate stereotypes towards marginalized groups, like the LGBTQIA+ community. In this paper, we perform a comparative study of how LLMs generate text describing people with different sexual identities. Analyzing bias in the text generated by an LLM using regard score shows measurable bias against queer people. We then show that a post-hoc method based on chain-of-thought prompting using SHAP analysis can increase the regard of the sentence, representing a promising approach towards debiasing the output of LLMs in this setting.

We live in an era when rapid advances in robotics and artificial intelligence are colliding with an expanding conception of sexual identity. This comes quickly on the heels of growing worldwide acceptance of gay, trans and bisexual people. Now you may describe yourself as polyamorous or demisexual -- that last one is people who only feel sexual attraction in close emotional relationships. Perhaps you best identify as aromantic (that's people who don't feel romance) or skoliosexual (that's a primary attraction to people of no, or multiple, or complex genders). Self-identification is not the same as identity, and some classes of description now may be closer to metaphor.

It can be a sore point for evolutionary biologists who study sexual selection. In the popular coverage of evolution, mate choice too often gets overlooked, in the shadow of natural selection. Yale biologist Richard O. Prum's new book responds to this imbalance. Prum is William Robertson Coe Professor of Ornithology at Yale. Over the years he has conducted detailed field studies of multiple bird species and their mating habits all around the world.

The campaign, led by academics Kathleen Richardson and Erik Billing, argues that the development of sex robots should be stopped because it reinforces or reproduces existing inequalities. Yes, society has enough problems with gender stereotypes, entrenched sexism and sexual objectification. But actual opposition to developing sexual robots that aims for an outright ban? That seems shortsighted, even – pardon the pun – undesirable. Groundbreaking work by David Levy, built on the early research into teledildonics – cybersex toys operable through the internet – describes the increasing likelihood of a society that will welcome sex robots. For Levy, sex work is a model that can be mirrored in human-robot relations.