How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I am a newly divorced bisexual dad who's moved to a city adjacent to my 20-year-old son's college. He's extremely shy and hasn't talked about sex with me in years. He identifies as queer but has provided no more detail than that.

When Anisa* graduated from her second degree, she felt "fairly confident" that her postgraduate studies, double-major undergraduate degree and years spent balancing two volunteering roles and a part-time job would account for something in the job market. She applied to every entry-level or junior role she could find in her industry, tracking each application's outcome on a spreadsheet. Before she knew it, "applying for jobs became a full-time job": in total, she applied for, and was rejected from, 350 jobs before finally landing one 18 months later. And she believes that AI – in particular its use in screening applications – is a huge part of the reason. "The rise of third party, AI-run digital online forms are a huge pain point for so many jobseekers," Anisa says as she recalls uploading CVs alongside filling in digital forms.

Western democracies are confronted with collapsing trust in their governments and surging nationalist movements. These phenomena have a particularly cruel timing, occurring contemporaneously with the rise of complex, global problems that urgently require coordinated, collective action. As the world becomes increasingly complicated and interconnected, the decisions of governments, corporations, and large institutions have become less accessible to the average citizen. Whether in relation to data privacy, climate change, or automation and the Fourth Industrial Revolution, citizens are being asked to accept decisions made by experts whose knowledge they can't match or evaluate. The average citizen, whose voice and consent was once proclaimed as essential to the functioning of democracy and legitimized political power, is now being asked to yield more authority to experts.

Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision criteria can produce counter-intuitive results, e.g. that the optimal constrained boundary may reject intuitively better candidates due to demographic imbalance in similar candidates. Both approaches can be seen as introducing individual resentment, where some individuals would have received a better outcome if they either belonged to a different demographic class and had the same qualifications, or if they remained in the same class but had objectively worse qualifications (e.g. lower test scores). We show that both forms of resentment can be avoided by using monotonically constrained machine learning models to create individually fair, demographically balanced classifiers.

Advancements in artificial intelligence have brought incredible changes. Increasingly, our lives are becoming automated. Many everyday tasks have already been relegated to algorithms of smart software. A staggering 70% of stock trading is fully automated, for example. This automation has resulted in massive job loss as well as resentment towards nascent AI technologies.