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ALT a diverse crowd of people holding up protest signs. Text on the poster reads "The Labor Movement! The folks that brought you... Overtime pay, living wage laws, parental leave, sick leave, civil rights, domestic partner benefits, social security, paid vacations, child labor laws, pension benefits, OSHA, grievance procedures, workplace power, farm labor rights, equal pay for equal work, the weekend, unemployment insurance, health benefits, workers comp, minimum wage." smaller text near the top reads "Fought for, supported, defends, demands." while text at the bottom holds a Frederick Douglass quote: "Power concedes nothing without a demand. It never did and it never will." poster by Ricardo Levins Morales @ricardolevinsmorales (IG).
ALT Excerpt of the linked article: Consent education makes assumptions about people's capacities that position survivors' struggles with consent as a 'problem.' For example, it's assumed in consent education that saying "yes" can or always will be enthusiastic, which may guilt survivors who, understandably, may have a hard time enjoying sex after sexual assault. Further, for survivors, sometimes consent isn't as simple as "Yes" or "No" and they can find themselves in a grey area. Survivors aren't the problem, they just require more care than is currently imaginable in much consent education and society more broadly.
!["For content owners and creators, few tools can prevent their content from being fed into a generative Al model against their will. [...] In an effort to address this power asymmetry, we have designed and implemented Nightshade, a tool that turns any image into a data sample that is unsuitable for model training. More precisely, Nightshade transforms images into "poison" samples, so that models training on them without consent will see their models learn unpredictable behaviors that deviate from expected norms, e.g. a prompt that asks for an image of a cow flying in space might instead get an image of a handbag floating in space. Used responsibly, Nightshade can help deter model trainers who disregard copyrights, opt-out lists, and do-not-scrape/robots.txt directives. It does not rely on the kindness of model trainers, but instead associates a small incremental price on each piece of data scraped and trained without authorization.](https://pbs.twimg.com/media/HAfc95jaIAAjpVK.jpg?name=orig)
ALT "For content owners and creators, few tools can prevent their content from being fed into a generative Al model against their will. [...] In an effort to address this power asymmetry, we have designed and implemented Nightshade, a tool that turns any image into a data sample that is unsuitable for model training. More precisely, Nightshade transforms images into "poison" samples, so that models training on them without consent will see their models learn unpredictable behaviors that deviate from expected norms, e.g. a prompt that asks for an image of a cow flying in space might instead get an image of a handbag floating in space. Used responsibly, Nightshade can help deter model trainers who disregard copyrights, opt-out lists, and do-not-scrape/robots.txt directives. It does not rely on the kindness of model trainers, but instead associates a small incremental price on each piece of data scraped and trained without authorization.
ALT alt text continued: Nightshade's goal is not to break models, but to increase the cost of training on unlicensed data, such that licensing images from their creators becomes a viable alternative." NOTE: We are not associated or directly affiliated with Nightshade nor are we attempting to take credit for its existence or creation. We are simply highlighting a relevant tool for artists, against Al-scrapping and other intellectual and/or artistic property theft. Dig Deeper: Check out the People's Guide to Al to learn more about Artificial Intelligence. Centered around 2 Primary Questions: What does fairness look like when computers shape decision-making? Who is creating the future, and how can we ensure that these creators reflect diverse communities and complex social dynamics?
ALT Artists are increasingly cautious about the use of generative Al - and they are right to do so. Over the past few years, we've seen an increase in the availability of generative Al to the general public - which has, amongst other things, led to a devastating uptick in the mimicry and/or outright theft of the hard work of many creatives. Notably, Suzzane Collin's 2025 novel Sunrise on the Reaping includes an anti-Al directive as part of its official copyright information. While including explicit instruction (regulation, prevention, etc.) of Al is useful, artists cannot safely or sustainably rely on or expect the kindness of model trainers. One useful tool for artists looking to protect their work from training Al models, especially without consent, is the Nightshade tool from UChicago, developed by Chicago to help artists protect their work from being used to train Al, especially without consent.
ALT "When sexual assault victims speak out, their institutions often betray them" By Jennifer J. Freyd, PhD for The Conversation. Above a link and an article excerpt: "Institutional betrayal is harm an institution does to those who depend upon it. This betrayal can take the form of overt policies or behaviors, such as discriminatory rules or genocide. Harm can also mean failing to do that what is reasonably expected of the institution, such as not providing relief to disaster victims or failing to respond effectively to sexual violence. For instance, some victims of assault are punished or even demoted or fired for reporting the assault to their institution. From ©theconversationdotcom January 11, 2018." #StudentsAgainstRapeCulture shared by @wgssa.utsc on IG for Consent Action Week 2026