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Home»News»Global Free Speech»Do the dead have free expression?
Global Free Speech

Do the dead have free expression?

News RoomBy News Room11 months agoNo Comments6 Mins Read4 Views
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This article first appeared in Volume 54, Issue 3 of our print edition of Index on Censorship, titled Truth, trust and tricksters: Free expression in the age of AI, published on 30 September 2025. Read more about the issue here.

“Don’t speak ill of the dead” is an aphorism that dates back centuries, but what if the dead speak ill of you? Over the past few years there has been a rise in the creation of chatbots trained on the social media and other data of the deceased. These griefbots are deepfakes designed to simulate the likeness and the personality of someone after their death, as though they have been brought back as ghosts.

The concept of the griefbot is not new. Our narratives around AI span centuries and the stories about creating an artificial version of a lost loved one can be found in Greek mythology: Laodameia, for example, distraught at losing her husband Protesilaus during the Battle of Troy, commissioned an exact likeness of him. (It did not end well: she was caught in bed with it. Her father, fearing she was prolonging her grief, burned the wax replica husband and Laodameia killed herself to be with Protesilaus.)

Further back, as US academic Alexis Elder has explored, there are precursors to griefbots in classical Chinese philosophy. The Confucian philosopher Xunzi, writing in the third century BCE, described a ritual where the deceased person was deliberately impersonated via a roleplay to allow loved ones the chance to engage with them once more.

These days, sci-fi likes to surface our contemporary fears and the TV shows have notable storylines warning of the pitfalls of resurrecting our loved ones via technology. In the 2013 Black Mirror episode Be Right Back, a grieving woman uses an AI service to talk with her recently deceased partner, desperate for communication that is ultimately doomed to be illusory.

Grief tech hit the headlines in 2020 when US rapper Kanye West gave his then-wife, Kim Kardashian, a birthday hologram of her dead father.

“Kanye got me the most thoughtful gift of a lifetime,” she wrote on social media. “It is so lifelike and we watched it over and over.”

West likely steered the script, which might’ve been obvious when the hologram told Kim she’d married “the most, most, most, most, most genius man in the whole world – Kanye West”.

While the broader public perception of ghostbots is often one of distaste and concern, those who have engaged with the digital echoes of a lost loved one have been surprisingly positive. When we lose someone we love, we do what we can to fix in place our concept of them. We remember and we memorialise: keepsakes and pictures, speaking their names and telling their stories. Having them with us again through technology is compelling. A Guardian newspaper article in 2023 reported users’ sense of comfort and closure at engaging with chatbots of their dead relatives.

“It’s like a friend bringing me comfort,” said one user.

With a potentially huge new market – grief is universal, after all – come the start-ups. Alongside general tools like ChatGPT are the dedicated software products. The US-based HereAfterAI, which bills itself as a ‘memory app’, allows users to record their thoughts, upload photos and grant access of their content to their loved ones. South Korean company DeepBrain AI claims it can build you an avatar of your dead loved one from just a single photo and a 10 second recording of their voice.

Current technology offers us the ‘could we?’, but what about the ‘should we’? In their 2023 paper, Governing Ghostbots, Edina Harbinja, Lilian Edwards and Marisa McVey flagged a very major problem: that of consent.

“In addition to the harms of emotional dependence, abusive communications and deception for commercial purposes, it is worth considering if there is potential harm to the deceased’s antemortem persona,” they wrote.

If we have some ownership of our data when alive, then should we have similar rights after our death? Creating an avatar of someone who is no longer around to approve it means we are literally putting words in someone’s mouth. Those words might be based on sentences they’ve typed and videos they’ve made but these have been mediated through machine learning, generating an approximation of an existence.

There is, of course, the potential that a desire for a sanitised reminder of the deceased means their words are only permitted to be palatable. Content moderation of AI chatbots might mean censorship or moderation – the same that applies to the large language models (LLMs) that drive them. Views could be watered down, and ideologies reconfigured. There is no true freedom of speech in the literal sense, and no objection available to the lack of it. The dead have no redress.

Conversely, what if posthumous avatars are built for political influence? In India in 2024, a deepfake avatar of a woman who had died more than a decade previously – the daughter of the founder of the Tamil Tigers – was shown in a video urging Tamils to fight for freedom. And in the USA, the parents of Joaquin Oliver, killed in a school shooting in Florida in 2018, created an AI version of their son to speak to journalists and members of Congress to push for gun reform. In both the India and USA cases, the griefbot technology did not exist when these people died and they would have had no way of knowing this could happen, let alone be able to consent to it.

Whether we like it or not, most of us will live on digitally when we die. Our presence is already out there in the form of data – all the social media we’ve ever posted, all the photos and videos of us online, our transaction history, our digital footprints. Right now, there is a lack of clear governance. Digital rights vary dramatically from jurisdiction to jurisdiction, and AI regulation is in its infancy. Only the EU and China currently have explicit AI legislation in place with moves afoot in other countries including the USA and UK, but not yet in statute. Amidst all of this, global tech companies get to set the agenda. For now, all we have is the hope that we can set our own personal boundaries for posthumous expression before our grief becomes someone else’s commodity.

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          As news broke that 84,270 people, including more than 32,000 children, had been affected by Nepal’s catastrophic floods, one visual offered something the world desperately needed in the moment: hope. The video shows a little girl, buried under thick mud, being is rescued by Nepal’s emergency teams. Instinctively, the viewer roots for her as rescuers carry her to safety, wrapping her shivering body in a red shawl. But the most moving moment of the video comes towards the end. The girl is sitting motionless in a relief camp, an officer feeding her warm soup. As the camera lingers on her face, a faint smile almost begins to show. It is the kind of image that can offer the viewer respite from the horrors of a humanitarian crisis. The video rapidly spread rapidly across all social media platforms, with commentators calling it a miracle. Of sorts. In fact, it was an AI miracle. Agence France-Presse (AFP) Fact Check’s investigation led them to an India-based creator, who admitted creating the video with the help of AI-generative tools. His justification was disarmingly straightforward: emotionally engaging visuals perform well online. This was just one of the many digitally-manufactured “miracles” put into social media heavy rotation during the floods. Another clip, showing a bridge collapsing into floodwaters swallowing vehicles and human lives, seemed to have been lifted straight out of a Roland Emmerich disaster film. The video racked up more than 2 million views on X before it was debunked as AI-generated by Factly. What made this social media post stand out for me was the identity of one of the people sharing it – an experienced news editor. The post, still pinned on his X profile and with more than 2 million views, suggests that even experienced newsroom decision-makers could be fooled by synthetic disaster footage – or, worse still, be untroubled about the veracity of such videos. Both videos incorporate the visual language of short films, but were not released in cinemas or on Netflix. They surfaced on Facebook, X and Instagram, even as emergency teams continued to search for the thousands of people that went missing after a glacial collapse triggered devastating floods in Nepal and Tibet on August 26. Even as survivors photographed authentic eyewitness footage, synthetic visuals emerged alongside them. The floods became one of the first major climate disasters in South Asia where documentation and fabrication coexisted in the same visual ecosystem.     Screenshot of debunked Nepal floods footage “During earlier disasters, our primary challenge was identifying old footage, establishing where a video was recorded and determining whether it was being presented in the correct context,” Saurabh Shukla told Index. Shukla is founder and editor-in-chief of NewsMobile, an India-based independent media organisation that debunks AI-related deepfakes and focuses on verified information. “The biggest difference today is the sheer speed at which fabricated and misleading visuals entered the information ecosystem,” Shukla said. “With generative AI, we now have an additional challenge – determining whether the event depicted in a video ever happened in the first place.” This has fed into a media environment where unverified visuals are disseminated faster than the verified facts reported by journalists. “Speed without verification can amplify confusion, while verification without timely reporting can leave an information vacuum,” Shukla said. “The challenge for newsrooms is to strike the right balance between being fast and being accurate.” The first images of rescue missions in secluded mountainous regions almost always come from locals, and thus forming the spine of disaster reporting. These inevitably blurry and shaky recordings do not have the polished quality of professional news reporting, but do carry something far more consequential: the credibility that comes with eyewitness documentation. These images often shape the core of the collective memory of a tragedy. But in an age of visual manipulation and trickery, that presumption of authenticity no longer holds. Factly conducted 30 fact-checks into coverage of the Nepal floods; fully one-third involved AI-generated visuals. “Beyond checking the location, source, and context of a visual, we also had to establish whether the visual itself was authentic,” Akshay Kumar Appani, lead fact checker and researcher at Factly, told Index. “During breaking disasters, old AI-generated visuals can spread rapidly while being presented as current footage. This makes it harder for journalists to establish the truth. The visual of the miracle child perfectly captured this dilemma. It was designed to evoke an emotional reaction; but it also looked like the kind of footage that journalists hope to find during crisis reporting. The video was vertically filmed. The camera movement was wobbly. The lighting was grainy. The muffled background sound felt believable. The video presented none of the glaring distortions that once made AI interventions easy to spot. Fabricated videos, today, can be produced to mimic the aesthetics of authentic citizen journalism. It was for this precise reason that the floods presented journalists with a crisis that went beyond recognising AI-generated images: they were increasingly obliged to verify whether the footage reaching them was genuine in the first place. Screenshot of video debunked by Factly Newsmobile’s verification process increasingly involves examining individual frames, conducting reverse-image searches, analysing visual inconsistencies and using AI-detection tools where appropriate. “However, detection tools are only one part of the process. We still need corroboration,” Shukla said. “We cannot rely solely on automated detection tools,” Sumit Dubey, South Asia digital verification editor for AFP’s Fact Check vertical, told Index. Fact-checking, he explained, requires going beyond automated outputs: tracing the original source, examining context, verifying metadata, looking for corroborating evidence and, where possible, speaking to relevant sources. “Automated tools are useful indicators, but fact-checking cannot be outsourced to a button,” Dubey said. The proliferation of synthetic media output in the wake of natural disasters is not new. It’s the sophistication of the output that has changed. For years, much of the public conversation around AI manipulation centred on faces: celebrity deepfakes, altered political speeches and fabricated portraits. The Nepal floods flipped that narrative. Almost every fabricated clip featured rivers, glaciers, collapsing hillsides, dams, flooded valleys or stranded animals. “AI-generated environmental content poses a different problem: [the] tells are failures of physics rather than anatomy,” Mahsa Alimardani told Index. Alimardani is associate director for technology threats and opportunities at Witness, a global organisation helping people use video and technology to protect and defend human rights This explains why the Nepal videos were so plausible. The miracle girl video wasn’t believable just because of her facial expressions. It was convincing because the sludge moved convincingly, because the choreography resembled actual emergency rescues, because the emotional tonality seemed genuine. “Most post-hoc detection tools were designed for content featuring physical characteristics, and they currently struggle significantly with environmental and disaster content,” Bruna Martins dos Santos – policy and advocacy manager, Witness Technology Threats and Opportunities – told Index. “Journalists might also struggle because many tools have limited capabilities to identify synthetic content generated elsewhere,” she said. Another emerging trend is the increasingly complicated role of Big Tech itself in detecting and labelling fake generative-AI content. Some of the clearest AI verdicts came from detection tools built by the very same companies whose models created the same fakes. Three weeks after the US state of California and the EU mandated the requirement for greater transparency with respect to AI-generated content, Witness and Indicator tested thirteen major AI providers. They found that seven had no public detector at all. “The capability to identify synthetic content partly existed. The governance around it did not,” Alimardani said. When both authentic and synthetic content circulate in disaster situations, the biggest challenge that journalists face is that of establishing truth amidst widespread public uncertainty and doubt. According to Martins dos Santos, “Journalists and local fact-checkers can face immense time pressure, often wasting scarce resources re-detecting the same recycled disaster footage across multiple platforms and languages.” The greatest danger emerging from Nepal may not be that people believed the fake videos. It may be that they have begun to doubt the real ones. Shukla’s concern is that people may stop trusting visual evidence altogether. “Imagine a situation where genuine footage of a disaster emerges, but people dismiss it as AI-generated. Facts become secondary to perception.” Within days of the floods, even authentic CCTV footage from the Gyirong border was being perceived as AI-generated, prompting fact-checkers to establish that the scenes it depicted were real. This exposed a troubling issue: even authentic visuals can no longer be taken at face value. Martins dos Santos told Index that the phenomenon has a name: “Liar’s dividend”, whereby the mere existence of convincing synthetic content allows bad actors to dismiss genuine, damaging evidence as fake simply by claiming it was AI-generated. This means that genuine footage is no longer accorded credibility immediately; it may first be perceived with suspicion. “This means a video may look completely authentic and still be fabricated. Conversely, genuine footage can contain compression artefacts that make it appear suspicious,” Shukla warned. Appani, for his part, noted that synthetic media doesn’t merely affect verification in real time. It also reshapes collective memory. “Widely circulated synthetic or recycled footage may eventually be remembered as genuine imagery from the disaster. Generative-AI could therefore affect not only how disasters are verified in real-time, but also how they are documented and remembered in the future.”The Nepal floods left destruction in its wake, and also a warning for journalists covering climatic disaster. The defining challenge of disaster reporting is no longer capturing the first image, but establishing which image the world can trust. In Shukla’s words: “For fact-checkers, the challenge is not just determining whether a video is real. It is establishing whether it actually belongs to the event it claims to depict.” READ MORE

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