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In 2001, social theorist bell hooks warned about the dangers of a loveless zeitgeist. In All About Love: New Visions, she lamented the lack of an ongoing public discussion . . . about the practice of love in our culture and in our lives. Back then, the internet was at a crossroads. The dot-com crash had bankrupted many early internet companies, and people wondered if the technology was long for this world. The doubts were unfounded. In only a few decades, the internet has merged with our bodies as smartphones and mined our personalities via algorithms that know us more intimately than some of our closest friends. It has even constructed a secondary social world. Yet as the internet has become more integrated in our daily lives, few would describe it as a place of love, compassion, and cooperation. Study after study describe how social media platforms promote alienation and disconnectionin part because many algorithms reward behaviors like trolling, cyberbullying, and outrage. Is the internets place in human history cemented as a harbinger of despair? Or is there still hope for an internet that supports collective flourishing? Algorithms and alienation I explore these questions in my new book, Attention and Alienation. In it, I explain how social media companies profits depend on users investing their time, creativity, and emotions. Whether its spending hours filming content for TikTok or a few minutes crafting a thoughtful Reddit comment, participating on these platforms takes work. And it can be exhausting. Even passive engagement, like scrolling through feeds and lurking in forums, consumes time. It might feel like free entertainmentuntil people recognize they are the product, with their data being harvested and their emotions being manipulated. Blogger, journalist, and science fiction writer Cory Doctorow coined the term enshittification to describe how experiences on online platforms gradually deteriorate as companies increasingly exploit users data and tweak their algorithms to maximize profits. For these reasons, much of peoples time spent online involves dealing with toxic interactions or mindlessly doomscrolling, immersed in dopamine-driven feedback loops. This cycle is neither an accident nor a novel insight. Hate and mental illness fester in this culture because love and healing seem to be incompatible with profits. Care hiding in plain sight In his 2009 book Envisioning Real Utopias, the late sociologist Erik Olin Wright discusses places in the world that prioritize cooperation, care and egalitarianism. Wright mainly focused on offline systems like worker-owned cooperatives. But one of his examples lived on the internet: Wikipedia. He argued that Wikipedia demonstrates the ethos from each according to ability, to each according to needa utopian ideal popularized by Karl Marx. Wikipedia still thrives as a nonprofit, volunteer-run bureaucracy. The website is a form of media that is deeply social, in the literal sense: People voluntarily curate and share knowledge, collectively and democratically, for free. Unlike social media, the rewards are only collective. There are no visible likes, comments, or rage emojis for participants to hoard and chase. Nobody loses and everyone wins, including the vast majority of people who use Wikipedia without contributing work or money to keep it operational. Building a new digital world Wikipedia is evidence of care, cooperation, and love hiding in plain sight. In recent years, there have been more efforts to create nonprofit apps and websites that are committed to protecting user data. Popular examples include Signal, a free and open source instant messaging service, and Proton Mail, an encrypted email service. These are all laudable developments. But how can the internet actively promote collective flourishing? In Viral Justice: How We Grow the World We Want, sociologist Ruha Benjamin points to a way forward. She tells the story of Black TikTok creators who led a successful cultural labor strike in 2021. Many viral TikTok dances had originally been created by Black artists, whose accounts, they claimed, were suppressed by a biased algorithm that favored white influencers. TikTok responded to the viral #BlackTikTokStrike movement by formally apologizing and making commitments to better represent and compensate the work of Black creators. These creators demonstrated how social media engagement is workand that workers have the power to demand equitable conditions and fair pay. This landmark strike showed how anyone who uses social media companies that profit off the work, emotions, and personal data of their userswhether its TikTok, X, Facebook, Instagram, or Redditcan become organized. Meanwhile, there are organizations devoted to designing an internet that promotes collective flourishing. Sociologist Firuzeh Shokooh Valle provides examples of worker-owned technology cooperatives in her 2023 book, In Defense of Solidarity and Pleasure: Feminist Technopolitics in the Global South. She highlights the Sulá Batsú co-op in Costa Rica, which promotes policies that seek to break the stranglehold that negativity and exploitation have over internet culture. Digital spaces are increasingly powered by hate and discrimination, the group writes, adding that it hopes to create an online world where women and people of diverse sexualities and genders are able to access and enjoy a free and open internet to exercise agency and autonomy, build collective power, strengthen movements, and transform power relations. In Los Angeles, theres Chani Inc., a technology company that describes itself as proudly not funded by venture capitalists. The Chani app blends mindfulness practices and astrology with the goal of simply helping people. The app is not designed for compulsive user engagement, the company never sells user data, and there are no comments sections. No comments What would social media look like if Wikipedia were the norm instead of an exception? To me, a big problem in internet culture is the way peoples humanity is obscured. People are free to speak their minds in text-based public discussion forums, but the words arent always attached to someones identity. Real people hide behind the anonymity of user names. It isnt true human interaction. In Attention and Alienation, I argue that the ability to meet and interact with others online as fully realized, three-dimensional human beings would go a long way toward creating a more empathetic, cooperative internet. When I was 8 years old, my parents lived abroad for work. Sometimes we talked on the phone. Often I would cry late into the night, praying for the ability to see them through the phone. It felt like a miraculous possibilitylike magic. I told this story to my students in a moment of shared vulnerability. This was in 2020 during the COVID-19 pandemic, so the class was taking place over videoconferencing. In these online classes, one person talked at a time. Others listened. It wasnt perfect, but I think a better internet would promote this form of discussion: people getting together from across the world to share the fullness of their humanity. Efforts like Clubhouse have tapped into this vision by creating voice-based discussion forums. The company, however, has been criticized for predatory data privacy policies. What if the next iteration of public social media platforms could build on Clubhouse? What if they brought people together and showcased not just their voices, but also live video feeds of their faces without harvesting their data or promoting conflict and outrage? Raised eyebrows. Grins. Frowns. Theyre what make humans distinct from increasingly sophisticated large language models and artificial intelligence chatbots like ChatGPT. After all, is anything you cant say while looking at another human being in the eye worth saying in the first place? Aarushi Bhandari is an assistant professor of sociology at Davidson College. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Mom guilt is such a familiar phrase that we rarely stop to ask what it really meansor why its so persistent. It describes that quiet, gnawing feeling that many mothers carry: that were not doing enough, not present enough, not loving, patient, or creative enough. That were falling short, even when were doing our best. But what if that guilt isnt just about personal choices? What if its not a private emotional shortcoming, but a reflection of something much largercultural messages, historical expectations, and systemic gaps that shape how mothers live and feel today? This essay offers a different way to think about mom guilt: not as a flaw in individual women, but as a symptom of a society that demands too much, offers too little, and then asks mothers to feel bad about the gap. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/acupofambition_logo.jpg","headline":"A Cup of Ambition","description":"A biweekly newsletter for high-achieving moms who value having a meaningful career and being an involved parent, by Jessica Wilen. To learn more visit acupofambition.substack.com.","substackDomain":"https:\/\/acupofambition.substack.com","colorTheme":"salmon","redirectUrl":""}} A guilt with no off switch Psychologically, guilt is often defined as a moral emotiona response to doing something wrong and wanting to make it right. But mom guilt rarely stems from a specific mistake. Instead, it often shows up as a vague, persistent sense of inadequacy. It lingers, shapeless but heavy. Because its so diffuse and constant, mom guilt may be less a personal emotion and more a shared emotional patterna kind of cultural atmosphere. Cultural theorist Raymond Williams called this a structure of feeling: not a formal rule, but a common way of feeling shaped by a particular time and place. In this view, mom guilt isnt just something mothers feelits something weve been taught to feel. Where did these expectations come from? To understand how this emotional pattern developed, we need to look at the historical construction of the good mother in American culture. After World War II, the ideal mother was cast as a full-time homemaker: white, middle-class, married to a breadwinner, and entirely devoted to her children. Her work was invisible but essential, and her worth came from self-sacrifice. By the 1990s and early 2000s, that ideal had morphed into what sociologist Sharon Hays called intensive mothering: mothers were now expected to be constantly emotionally attuned, manage every detail of their childs development, follow expert advice, and sacrifice their own needs to do it all. And even as more women entered the workforce, this new model still assumed unlimited time, energy, and emotional bandwidth. The result? Many mothers felt stretched thin, torn between competing demands: be selfless but successful, always available but independent. Mom guilt wasnt a sign of failureit was a natural outcome of being asked to do the impossible. The role of systemsand their silence These expectations dont exist in a vacuum. Theyre intensified by how little structural support American families receive. Unlike many wealthy countries, the U.S. offers no guaranteed paid parental leave. Childcare is expensive and hard to access. Most workplaces still operate as if someone else is handling everything at home. When mothers feel exhausted or overwhelmed, the message they receive is: Try harder. Be more grateful. Find balance. This reflects a deeper cultural logicone that blames individuals for structural problems. In this model, the solution to burnout is self-help, not social change. Mom guilt thrives in this space. It turns systemic failure into personal shame. It keeps women striving, quiet, and inwardly focusedwondering if theyre doing enough, instead of asking whether society is. Guilt is gendered Its also important to say this clearly: mom guilt is not evenly distributed. Fathers, especially in heterosexual partnerships, are rarely expected to feel guilty for long work hours or needing rest. When they show up for parenting, theyre often praised for helping. Mothers, by contrast, are expected to organize their livesand emotionsaround their childrens needs. Sociologist Arlie Hochschild called this emotional labor: the often invisible work of managing others feelings. In families, mothers are expected to carry the emotional weight. When they fall short, they feel guiltnot just about actions, but about presence, patience, and even joy. So what do we do with it? Rather than telling mothers to get over their guilt, we might ask: what is this guilt doing? Who benefits from it? Mom guilt isnt just a feelingits a social mechanism. It keeps women pushing toward unattainable ideals, keeps them quiet about their needs, and keeps attention focused inward instead of outward. It makes it harder to question the systems that are, in fact, failing us. Theres no quick fix. But theres power in naming it. When guilt creeps in, we can pause and ask: Where did this should come from? Whose expectations am I trying to meet? What would I needpersonally and structurallyto feel less torn? These questions wont erase guilt, but they can loosen its grip. They shift the storyfrom one of individual failure to one of cultural clarity and collective care. {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/acupofambition_logo.jpg","headline":"A Cup of Ambition","description":"A biweekly newsletter for high-achieving moms who value having a meaningful career and being an involved parent, by Jessica Wilen. To learn more visit acupofambition.substack.com.","substackDomain":"https:\/\/acupofambition.substack.com","colorTheme":"salmon","redirectUrl":""}}
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For search and rescue, AI is not more accurate than humans, but it is far faster. Recent successes in applying computer vision and machine learning to drone imagery for rapidly determining building and road damage after hurricanes or shifting wildfire lines suggest that artificial intelligence could be valuable in searching for missing persons after a flood. Machine learning systems typically take less than one second to scan a high-resolution image from a drone, versus one to three minutes for a person. Plus, drones often produce more imagery to view than is humanly possible during the critical first hours of a search, when survivors may still be alive. Unfortunately, todays AI systems are not up to the task. We are robotics researchers who study the use of drones in disasters. Our experiences searching for victims of flooding and numerous other events show that current implementations of AI fall short. However, the technology can play a role in searching for flood victims. The key is AI-human collaboration. AIs potential Searching for flood victims is a type of wilderness search and rescue that presents unique challenges. The goal for machine learning scientists is to rank which images have signs of victims and to indicate where in those images search-and-rescue personnel should focus. If the responder sees signs of a victim, they pass the GPS location in the image to search teams in the field to check. The ranking is done by a classifier, which is an algorithm that learns to identify similar instances of objectscats, cars, treesfrom training data in order to recognize those objects in new images. For example, in a search-and-rescue context, a classifier would spot instances of human activity, such as garbage or backpacks, to pass on to wilderness search-and-rescue teams, or even identify the missing person themselves. A classifier is needed because of the sheer volume of imagery that drones can produce. For example, a single 20-minute flight can produce over 800 high-resolution images. If there are 10 flightsa small numberthere would be over 8,000 images. If a responder spends only 10 seconds looking at each image, it would take over 22 hours of effort. Even if the task is divided among a group of squinters, humans tend to miss areas of images and show cognitive fatigue. The ideal solution is an AI system that scans the entire image, prioritizes images that have the strongest signs of victims, and highlights the area of the image for a responder to inspect. It could also decide whether the location should be flagged for special attention by search-and-rescue crews. Where AI falls short While this seems to be a perfect opportunity for computer vision and machine learning, modern systems have a high error rate. If the system is programmed to overestimate the number of candidate locations in hopes of not missing any victims, it will likely produce too many false candidates. That would mean overloading squinters or, worse, the search-and-rescue teams, which would have to navigate through debris and muck to check the candidate locations. Developing computer vision and machine learning systems for finding flood victims is difficult for three reasons. One is that while existing computer vision systems are certainly capable of identifying people visible in aerial imagery, the visual indicators of a flood victim are often very different compared with those for a lost hiker or fugitive. Flood victims are often obscured, camouflaged, entangled in debris, or submerged in water. These visual challenges increase the possibility that existing classifiers will miss victims. Second, machine learning requires training data, but there are no datasets of aerial imagery where humans are tangled in debris, covered in mud, and not in normal postures. This lack also increases the possibility of errors in classification. Third, many of the drone images often captured by searchers are oblique views, rather than looking straight down. This means the GPS location of a candidate area is not the same as the GPS location of the drone. It is possible to compute the GPS location if the drones altitude and camera angle are known, but unfortunately, those attributes rarely are. The imprecise GPS location means teams have to spend extra time searching. How AI can help Fortunately, with humans and AI working together, search-and-rescue teams can successfully use existing systems to help narrow down and prioritize imagery for further inspection. In the case of flooding, human remains may be tangled among vegetation and debris. Therefore, a system could identify clumps of debris big enough to contain remains. A common search strategy is to identify the GPS locations of where flotsam has gathered, because victims may be part of these same deposits. An AI classifier could find debris commonly associated with remains, such as artificial colors and construction debris with straight lines or 90-degree corners. Responders find these signs as they systematically walk the riverbanks and flood plains, but a classifier could help prioritize areas in the first few hours and days, when there may be survivors, and later could confirm that teams didnt miss any areas of interest as they navigated the difficult landscape on foot. Robin R. Murphy is a professor of computer science and engineering at Texas A&M University. Thomas Manzini is a PhD student in robotics at Texas A&M University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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