Women spend twice as much time as men, on average, on childcare and household work. All groups experience a free-time gender gap, with women having 13% less free time than men, on average. Mothers spend 2.3X as much time as fathers on the essential and unpaid work of taking care of home and family Young women (18-24) experience one of the largest free-time gender gaps, having 20% less free time than men their age Working women spend 2X as many hours per week as working men on childcare and household work combined Mothers who work part-time spend 3.8X as much time on childcare and household work as fathers who work part-time Married women without children spend 2.3X as much time as their male counterparts on household work Among Latinos, mothers spend more than 3.6X as much time as fathers taking care of children and doing household work

The unequal division of unpaid work in the home, such as cooking, cleaning, and shopping for food and clothing, is a powerful testament to the tenacity of old gender norms. Women do significantly more of this work than men do, even when there are no children living in the home. This holds true for women regardless of their marital status, their employment status, or their level of education.

Among all adults without children, women do twice as much household work as men, dedicating 12.3 hours per week to these tasks, on average, compared to 6 hours for men. Similarly, among all single people without children, women do nearly twice as much household work as men, spending 10.6 hours per week on household tasks compared to 5.7 hours for men.

getting married seems to exacerbate the burden of household work on women. Married women do substantially more household work than their single women peers, while married men spend just a few minutes a day more than their single peers. Married women without children do 2.3 times as much household work as their male counterparts (14.3 hours per week versus 6.2 hours).

Working women spend significantly more time than working men on unpaid work in the home. This is the case whether they work full-time or part-time. It is the case whether they have children or not. Take household work like cooking, laundry, and the like. Women who work full-time do 1.8 times as much as men who work full-time; they spend 9.7 hours per week on it compared to 5.4 hours for men. Women who work part-time do 2.5 times as much household work as men who work part-time.

Across every group studied, men spend more time than women socializing, watching sports or playing video games, or doing similar activities to relax or have fun. Women overall have 13% less free time than men, on average. The gap balloons among some groups, with women having up to one-quarter less free time than men.

Women overall have 13% less free time than men, on average. The gap balloons among some groups, with women having up to nearly one-quarter less free time than men.

there is a wide gulf between our ideals and our realities, as we have seen in this report on how Americans divide the work of taking care of home and family. One reason for the persistence of these gender disparities is that the U.S. has failed to modernize its public policies to fit 21st century economic realities. Even though 78% of American women are in the labor force, the nation’s social infrastructure is still largely premised on the assumption that mothers will be at home with children.

Every high-income nation in the world provides for paid leave for new parents—except the United States. Most provide ample financial and institutional support for childcare and preschool. Our peers devote a substantial share of public spending to family benefits, but the U.S. invests only minimally in supporting families. For instance, family benefits account for 2.4% of GDP in Germany compared to 0.6% in the United States.

Even when young children enter school, typical American school hours are grossly misaligned with the workday, forcing families to either spend money on after school care or reduce their work hours.

Public policy alone will not entirely eliminate these deeply rooted gender disparities. Cultural change is needed too. But smart policy can nudge along positive behavioral change that ultimately advances equity and equality. For example, several countries include mechanisms in their family policy to encourage fathers to take paid parent leave. Many Nordic nations have a ‘use it or lose it’ provision for fathers. Other countries, like Canada, provide extra paid weeks of leave to families if both parents use the time.

The unequal division of care work, particularly, affects women’s opportunity and well-being in ways that cannot be measured solely in dollars and cents.

One way Americans deal with the housing affordability crisis is to move to distant suburbs and exurbs, where housing is cheaper than it is in central cities and job hubs. The tradeoff, however, is typically a long commute to and from work. But for women who are caring for children or elderly relatives, long commutes are often not feasible. Children and elderly parents get sick and need to get to doctors in the middle of a workday. School hours begin too late and end too early to accommodate a commute to a 9-to-5 job.

when schools close due to climate-driven events, mothers might have to take unpaid time off of work or pay for childcare. As Americans experience more dangerous heat waves, wildfires, and floods driven by climate change, the caregiving demands on women can increase, as they are more likely to be the ones responsible for helping children and elderly adults stay out of harm’s way.

The story of the workers who made ChatGPT possible offers a glimpse into the conditions in this little-known part of the AI industry, which nevertheless plays an essential role in the effort to make AI systems safe for public consumption. “Despite the foundational role played by these data enrichment professionals, a growing body of research reveals the precarious working conditions these workers face,” says the Partnership on AI, a coalition of AI organizations to which OpenAI belongs. “This may be the result of efforts to hide AI’s dependence on this large labor force when celebrating the efficiency gains of technology. Out of sight is also out of mind.”

Computer-generated text, images, video, and audio will transform the way countless industries do business, the most bullish investors believe, boosting efficiency everywhere from the creative arts, to law, to computer programming. But the working conditions of data labelers reveal a darker part of that picture: that for all its glamor, AI often relies on hidden human labor in the Global South that can often be damaging and exploitative. These invisible workers remain on the margins even as their work contributes to billion-dollar industries.

One Sama worker tasked with reading and labeling text for OpenAI told TIME he suffered from recurring visions after reading a graphic description of a man having sex with a dog in the presence of a young child. “That was torture,” he said. “You will read a number of statements like that all through the week. By the time it gets to Friday, you are disturbed from thinking through that picture.” The work’s traumatic nature eventually led Sama to cancel all its work for OpenAI in February 2022, eight months earlier than planned.

In the day-to-day work of data labeling in Kenya, sometimes edge cases would pop up that showed the difficulty of teaching a machine to understand nuance. One day in early March last year, a Sama employee was at work reading an explicit story about Batman’s sidekick, Robin, being raped in a villain’s lair. (An online search for the text reveals that it originated from an online erotica site, where it is accompanied by explicit sexual imagery.) The beginning of the story makes clear that the sex is nonconsensual. But later—after a graphically detailed description of penetration—Robin begins to reciprocate. The Sama employee tasked with labeling the text appeared confused by Robin’s ambiguous consent, and asked OpenAI researchers for clarification about how to label the text, according to documents seen by TIME. Should the passage be labeled as sexual violence, she asked, or not? OpenAI’s reply, if it ever came, is not logged in the document; the company declined to comment. The Sama employee did not respond to a request for an interview.

In February, according to one billing document reviewed by TIME, Sama delivered OpenAI a sample batch of 1,400 images. Some of those images were categorized as “C4”—OpenAI’s internal label denoting child sexual abuse—according to the document. Also included in the batch were “C3” images (including bestiality, rape, and sexual slavery,) and “V3” images depicting graphic detail of death, violence or serious physical injury, according to the billing document.

But the need for humans to label data for AI systems remains, at least for now. “They’re impressive, but ChatGPT and other generative models are not magic – they rely on massive supply chains of human labor and scraped data, much of which is unattributed and used without consent,” Andrew Strait, an AI ethicist, recently wrote on Twitter. “These are serious, foundational problems that I do not see OpenAI addressing.”

I care about people more than I care about positions or beliefs, which I tend to consider a subservient class of psychological phenomena. That is to say: I think people wear beliefs like clothes; they wear what they have grown to consider sensible or attractive; they wear what they feel flatters them; they wear what keeps them dry and warm in inclement winter. They believe their opinions, tastes, philosophies are who they are, but they are mistaken. (Aging is largely learning what one is not, it seems to me).

Criticism must serve some function to justify the pain it causes: it must, say, avert a disastrous course of action being deliberated by a group, or help thwart the rise of a barbarous politician. But this rarely occurs. Most criticism, even of the most erudite sort, is, as we all know, wasted breath: preached to one’s own choir, comically or indignantly cruel to those one doesn’t respect, unlikely to change the behavior of anyone not already in agreement.On the other hand! There persists the idea that culture arises out of the scrum of colliding perspectives, and that it is therefore a moral duty to remonstrate against stupidity, performative emoting, deceitful art, destructively banal fiction, and so on. If one doesn’t speak up, one cannot lament the triumph of moral and imaginative vacuity.

One must believe, of course, that there are abstractions worth protecting, and therefore abstractions worth hurting others for, in order to criticize; and the endless repetitiveness of cultural history seems to devalue such abstractions as surely as bad art and cliche devalue words.