Ethical AI in Sex and Gender Research: Bias, Consent, and Historical Context

When we talk about ethical AI, artificial intelligence systems designed to respect human rights, fairness, and transparency. Also known as responsible AI, it’s not just about coding—it’s about who gets seen, whose stories are counted, and who gets left out. In sex and gender research, ethical AI isn’t a luxury. It’s a necessity. Every algorithm trained on historical data carries the weight of centuries of silence: women’s orgasms dismissed as irrelevant, bisexual identities erased from surveys, lesbian relationships coded as "friendships," and Black bodies misrepresented in medical datasets. If an AI learns from biased archives, it doesn’t just repeat history—it automates it.

That’s why gender bias in AI, the tendency of algorithms to reinforce stereotypes about sex, gender, and sexual behavior is so dangerous. Think of chatbots trained on Victorian medical texts that still call female masturbation "hysterical." Or facial recognition tools that fail to detect non-binary faces because they were only tested on binary portraits. Even something as simple as a dating app recommendation engine can push users toward outdated norms—monogamy, heterosexuality, traditional beauty—if it’s trained on data that reflects 1950s values, not 2025 realities. And then there’s consent in algorithms, the question of whether data about people’s sexual behavior was collected with real, informed permission. Many historical records used to train AI come from police files, medical journals, or undercover surveillance—all sources built on control, not cooperation. Using them without questioning their origin is like building a house on a foundation of stolen land.

But ethical AI isn’t just about fixing mistakes. It’s about rewriting the story. When researchers use AI to uncover hidden lesbian relationships in 19th-century letters, or map how Etruscan funerary art challenged Roman gender norms, they’re not just analyzing data—they’re restoring erased voices. The same tools that once silenced can now amplify—if they’re built with care. This collection dives into how AI intersects with real human experiences: from the medical myths around masturbation that still echo in health apps, to how modern HIV treatment data is being used to train predictive models without violating privacy. You’ll find articles on how feminist scholars are pushing back against biased datasets, how transgender histories are being digitally preserved before they vanish, and why the "neutral" AI trained on Victorian literature ends up reinforcing the very oppression it claims to study.