Sixty years ago, on March 7, 1965, hundreds of civil rights activists, also known as foot soldiers, marched from Selma to Montgomery, Alabama to fight for the right to vote for Black people. As they marched across the Edmund Pettus Bridge, Alabama State Troopers brutally beat them. The violence these activists endured became synonymous with the struggle for Black enfranchisement.

The Edmund Pettus Bridge march, alongside three other marches in Alabama, helped Congress recognize the urgency with which it must pass voting rights legislation. Mere weeks after the Edmund Pettus march, the Voting Rights Act of 1965 was presented to Congress on March 17, 1965. President Johnson signed the bill into law on August 6, 1965.

Since 1965, voting rights advocates have gathered in Alabama annually for the Selma Jubilee to mark the anniversary by marching the Edmund Pettus Bridge in remembrance. This was the first year the American Civil Liberties Union sponsored the Selma Jubilee, and I was lucky enough to travel to Alabama to witness the march firsthand.

Credit: Lynsey Weatherspoon

Early that morning, we gathered at the historic Brown AME chapel, which played a large role in the civil rights movement. Activists would gather here to plan protests and civil rights efforts, and this church was the original meeting place before the march.

On the steps of the chapel before the march to Selma, speakers from voting rights groups nationwide recognized the original marchers, also known as foot soldiers, while contextualizing the current state of voting rights.

Credit: Lynsey Weatherspoon

Yasmin Cader, the director of the ACLU Trone Center for Justice, kicked us off by reminding us how the current fights for voting rights and racial justice are intertwined.

Credit: Lynsey Weatherspoon

We began to march toward Edmund Pettus Bridge, led by the ACLU of Alabama, holding signs and singing protest songs. The energy in the crowd was electric. While we are all different, we gathered in Selma because of our shared belief in the enduring power of justice.

As we approached the Edmund Pettus Bridge, everyone moved back to ensure the foot soldiers had the chance to cross the bridge first. Watching them march, I was struck by the history of the moment. These brave people were willing to risk everything for the right to participate in democracy.

Credit: Lynsey Weatherspoon

As I marched across the bridge, I reflected on how we still have work to do to ensure that we live up to the promise of the Voting Rights Act. While the methods may change, the right to vote is still under attack nationwide. We’re fighting in court to ensure that states like Louisanna maintain voting districts that give black voters equal representation, and politicians are trying to pass bills like the SAVE Act that would make it more difficult to vote.

We honor the foot soldiers by continuing their fight.

On March 6, a federal judge ruled that President Donald Trump illegally fired former National Labor Relations Board (NLRB) chair Gwynne Wilcox. The judge ordered that she be restored and allowed to fulfill her duties as a duly-appointed member of the NLRB. With its quorum now re-established, the NLRB can resume its mission enforcing workers’ rights under the National Labor Relations Act (NLRA). President Trump has opposed this important work, as shown by his email purporting to fire Wilcox, who is the only Black woman to ever serve on the NLRB.

In his email, Trump at turns ignored and derided the agency’s work, further undercutting his claims to be pro-worker. As he admitted, his decision to remove Wilcox was because she is pro-worker, as shown in her support for a pro-worker ‘joint employer’ rule, which, as the American Civil Liberties Union has argued, is essential to holding employers accountable for their misdeeds. In his email, Trump did not mention the NLRB’s mission to safeguard workers’ right to collective action or the benefits of holding employers accountable for their treatment of workers. He expressed empathy for employers only, claiming without evidence that Wilcox’s decisions had “improperly cabined employers’ rights to speak on the subject of unionization[.]” Trump may have been alluding to the numerous unfair labor practices for which the NLRB found Tesla liable, including a threat to retaliate, which is not protected by the First Amendment, by Elon Musk when workers at Tesla began unionizing.

Though often overlooked compared to better-known rights, the NLRA-protected “right to self-organization, to form, join, or assist labor organizations, to bargain collectively through representatives of their own choosing, and to engage in other concerted activities for the purpose of collective bargaining or other mutual aid or protection” are essential to the causes of racial and economic justice.

The right to engage in collective action to protest working or other conditions is a core human right. It is implicit in the First Amendment right to speech, association, and petition and is codified in Article 23 of the Universal Declaration of Human Rights and Section 7 of the NLRA. Thanks to Section 7, most private sector workers in the U.S. have the express right to organize and form unions, to bargain collectively, and to engage in other forms of collective action to improve working conditions.

While these rights benefit all workers, low-income workers – disproportionately Black and Hispanic – stand to benefit the most. Unions negotiate for higher wages, improved benefits, reduced income equality, and workplace protections, as the federal government and others have documented.

The NLRB is charged with protecting these rights, which has led in recent years to wins for low-income workers and workers of color. Specifically:

• The NLRB administered the election of Amazon warehouse workers in Staten Island, more than 60 percent of whom were Black or Latino, who voted to join the Amazon Labor Union (ALU), a culmination of the first successful organizing drive in Amazon history. The NLRB also went after Amazon after it committed numerous unfair labor practices against ALU.
• The NLRB oversaw the unionization of thousands of Starbucks employees, more than 50 percent of whom identify as a racial or ethnic minority. The NLRBhas consistently prosecuted Starbucks when it has committed unfair labor practices against those workers.
• Since 2021 petitions for union elections at the NLRB have more than doubled. Black workers have higher union membership rates than other racial and ethnic groups and, thus, are most likely to depend on the protections afforded by the NLRA.

Ultimately, if workers are to improve their lot, they must rely on each other. At their core, labor unions are vehicles for collective self-help that, when leveraged wisely, can help workers of all stripes but particularly low-income and other vulnerable workers – who alone can do little to improve their lot – effectuate their demands for better working conditions. Renowned civil rights leaders have recognized their potential. A. Phillip Randolph, who helped organize the 1963 March on Washington for Jobs and Freedom, recognized the potential of unions to help Black workers band together to oppose discrimination. Martin Luther King Jr. spent the last days of his life in Memphis in solidarity with striking sanitation workers seeking better pay and safer working conditions. Pedro Albizu Campos, famed nationalist leader of Puerto Rico, organized sugarcane workers in furtherance of this principle. Unions are a rare institution where people of all backgrounds, and across racial lines, can make common cause in furtherance of mutual aid.

The NLRB’s reopening is cause for celebration and a step in the direction of economic and racial justice. Regardless of the NLRB, it’s on workers to organize to see their demands met. As former NLRB General Counsel Jennifer Abruzzo (fired in the same email as Wilcox) observed in her outgoing statement: “if the Agency does not fully effectuate its Congressional mandate in the future as we did during my tenure, I expect that workers with assistance from their advocates will take matters into their own hands in order to get well-deserved dignity and respect in the workplace, as well as a fair share of the significant value they add to their employer’s operations.”

Imagine an America where multiple police officers and security guards stand watch on every block, in every park, in every store, and in every other public space around the clock. Imagine these officers watching us constantly, not only scrutinizing our every move for signs of “suspicious” behavior, but also noting many details about us — such as what we’re wearing and carrying, who we’re with and what our relationship appears to be with them — and recording those details in a searchable database.

That’s not going to happen. Nobody wants to pay for human officers to stand watch in places where almost nothing ever happens. But we are moving toward a future where we might end up with an automated machine equivalent. Video analytics, the technology to make that possible, has been developing for many years. Now, the same generative artificial intelligence (AI) techniques that have revolutionized large language models (LLMs) like ChatGPT are in the process of creating a new, more powerful generation of this technology that could super-charge video surveillance.

In 2019, the ACLU published a report on how video analytics makes it possible for machines to not jut record oceans of video, but to “watch” that video — in the sense that they’re able to analyze, in real time, what’s happening in a video feed — and send alarms to humans when certain conditions are met. Video data, which was formerly very difficult to search and analyze, has also become increasingly searchable through queries such as “find me a male wearing a purple shirt and carrying a violin case” that (like face recognition) can now be run across vast amounts of video data. Since then, video analytics technologies have become widely available, with most commercial surveillance cameras including some form of the technology built in.

The older generation of video analytics, however, is limited to detecting a narrow set of objects on which it has been laboriously trained, and often performs poorly — “sold and marketed way beyond real-life performance,” as one industry player put it. But today the revolutionary advances in large language models are in the process of spawning a new generation of the technology. While language models, per their name, are mostly focused on text, the techniques and advances that led to those models’ breakthrough success are spilling into machine vision as well — specifically programs dubbed “Vision Language Models” (VLMs) that can understand both visual and natural-language textual inputs. In computer science terms, these new machine vision programs are based on the same technology as language models, called transformers, as opposed to classic machine vision work, which is based on a technology called convolutional neural networks (CNNs). While both technologies continue to be used and sometimes combined, and video technologies are still evolving fast, this appears to be a big change.

The advent of vision language models will have three important effects.

VLMs are able to generalize much better than the older, CNN-based video analytics programs because they combine image recognition with the general world-knowledge that large language models gain as part of their training on all the Internet’s textual data.

In the older form of machine vision, for example, a CNN might be shown millions of pictures of horses and elephants and thus laboriously learn to identify and distinguish them. An LVM, on the other hand, might be able to find a zebra in a video even if it had never seen a photo of a zebra before, simply by leveraging its world knowledge (that a zebra is like a horse with stripes). Instead of being limited to a closed set of predefined things, VLMs are able to recognize an enormous variety of objects, events, and contexts without being specifically trained on each of them. VLMs also appear to be much better at contextual and holistic understandings of scenes.

The CEO of security analytics company Ambient.ai declared this shift “the most significant technology evolution in the history of video analytics ever,” saying “it solves all the problems that kept traditional analytics from getting the last miles to large-scale adoption.” Logan Kilpatrick, manager of Google DeepMind, told a podcaster that

my guess is that as [VLM-based] vision becomes more and more prominent, we’re going to see [startups] go after all of these eco-systems and industries where they’re using domain-specific vision models and not using a general purpose model. And… you unlock all these use-cases which those models are just not actually capable of doing; they’re very very rigid and can’t be fault tolerant in a lot of those cases.

Anybody can gain a sense of the power of the new models using this site created by a former Google engineer to teach people just how much information can be extracted from their photos by AI. Or by going directly to a site like Google’s AI Studio and play around with uploading photos and videos. In addition to detailed descriptions of objects and people, the models can make observations on things like emotional state and even social class.

In December the technologist Simon Willison calculated that to analyze all of the 68,000 images in his personal photo library using the Google Gemini model would cost $1.68. It’s also possible to stream videos to models like Gemini and have them analyze the contents, which appears to cost roughly 10 cents per hour of video. Such low costs mean that as the technology is refined, and as understanding of these capabilities spreads, it will not be confined to Google and a handful of other AI developers. The technology will become easily accessible to a broad variety of security companies and find its way into the products that are used to monitor us across a wide variety of contexts, from private spaces like stores and shopping malls, to those public spaces where police departments have deployed surveillance cameras.

As with LLMs, the models may also increasingly become possible to run locally, without having to connect to the servers of, and share data with, OpenAI, Google, or other big companies. It’s good if AI technologies are democratized rather than being controlled by big players, but that also means that guardrails — such as those we recommended in our report — are going to become vital as various parties, well-intentioned and not, deploy them.

Instead of being confined to precisely worded menus or tags of objects and behaviors that a model has been trained to recognize, users can just issue commands using everyday speech, such as “text me if the dog jumps on the couch,” “let me know if any kids walk on my lawn,” or troublingly, “alert me if a Black man enters the neighborhood” or “if someone is behaving suspiciously.”

It’s important to keep in mind that like large language models, vision language models are unreliable. The surveillance industry analysis firm IPVM tested one security company’s new LVM-powered product and observed that it “returned some results that were incredibly impressive but also some results that were incredibly bad.” A group of academic and industry experts explained in a recent paper that

connecting language to vision is not completely solved. For example, most models struggle to understand spatial relationships or count… They often ignore some part of the input prompt [and] can also hallucinate and produce content that is neither required nor relevant. As a consequence, developing reliable models is still a very active area of research.

As with face recognition (which is actually a subset of video analytics), there are reasons to worry about this technology when it works poorly — and other reasons to worry when it works well. If LVMs remain unreliable, but just reliable enough that people depend on them and don’t double-check that results are accurate, that could lead to false accusations and other injustices in security contexts. But to the extent it becomes more intelligent, that will also allow for more and richer information to be collected about people, and for people to be scrutinized, monitored, and subjectively judged in more and more contexts.

In the end, nobody knows how capable this technology will become or how quickly. But policymakers need to know that advancing AI means surveillance cameras no longer the classic cameras of yesterday that do nothing more than record. Already we’re seeing AI used for monitoring in an increasing number of contexts, including vehicle driver monitoring, workplace monitoring, gun detection, and the enforcement of rules. If we let it happen, we can expect that nearly every rule, regulation, law, and employer dictate that can be enforced through visual monitoring of human beings will become subject to these unblinking and increasingly intelligent yet unreliable artificial eyes.