Submission rejected on 9 June 2026 by Avgeekamfot (talk). The subject is contrary to the purpose of Wikipedia. Rejected by Avgeekamfot 2 months ago. Last edited by Avgeekamfot 2 months ago. Ask for advice

Submission declined on 2 June 2026 by .nhals8 (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. Declined by .nhals8 2 months ago.

Submission declined on 8 May 2026 by BlueStaticHorse (talk). This draft's references do not show that the subject meets Wikipedia's criteria for inclusion. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, not just brief mentions or routine announcements; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as interviews, press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. Declined by BlueStaticHorse 3 months ago.

Submission declined on 8 May 2026 by EatingCarBatteries (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. Declined by EatingCarBatteries 3 months ago.

• Comment: 3 prior declines without issue being resolved. Avgeekamfot (talk) 10:45, 9 June 2026 (UTC)

• Comment: If you've used AI, admit it. nhals8 (rats in the house of the dead) 03:20, 2 June 2026 (UTC)

• Comment: The issues were not fixed from the previous submission. BSH (talk) - (she/her) 18:15, 8 May 2026 (UTC)

Digital necromancy is the use of artificial intelligence (AI), natural language processing, robotics, and computer-generated imagery to simulate or recreate deceased individuals. These digital representations, often termed "griefbots" or digital clones, are generated through the aggregation of a deceased person's historical digital footprint, including social media history, telecommunications data, audio recordings, and photographs.

Initially, this phenomenon focused on replicating the image of public figures, creating a group of posthumous icons known in the scientific literature as "Delebs" (from dead celebrities – a term coined by Dr. Denver D'Rozario) The film and advertising industries have repeatedly "revived" deceased celebrities using digital visual effects:

• The completion of films in which actors died during filming, as was the case with Paul Walker in Fast & Furious 7^[1]
• Performances in the form of digital holograms, such as rapper Tupac Shakur's posthumous "concert" in 2012 and actor Peter Cushing's virtual appearance in Rogue One: A Star Wars Story^[2]
• Commercial use in television commercials – including a computer-generated Audrey Hepburn in a Galaxy chocolate ad and Bruce Lee promoting Johnnie Walker whisky. These types of posthumous contracts generate earnings exceeding a billion pounds per year^[1]

Initially, the phenomenon focused on replicating the likenesses of public figures, establishing a category of posthumous icons referred to in academic literature as "delebs" (a portmanteau of "dead celebrities" coined by marketing scholar Denver D'Rozario)^[1]. The entertainment and advertising industries have utilized digital visual effects to posthumously feature deceased individuals in media productions.

In film production, technology has been employed to complete projects left unfinished due to an actor's death, such as the digital recreation of Paul Walker in Furious 7 (2015)^[1]. Virtual performances have also been staged using computer-generated imagery and projection techniques, notable examples being the digital appearance of rapper Tupac Shakur at the 2012 Coachella festival and the posthumous casting of Peter Cushing in Rogue One: A Star Wars Story (2016).^[2]

In advertising, digital reconstructions have been used for commercial endorsements, including campaigns featuring a computer-generated Audrey Hepburn for Galaxy chocolate and Bruce Lee for Johnnie Walker whisky. This sector of the entertainment economy generates significant revenue, with licensing and intellectual property contracts for deceased celebrities estimated to exceed one billion pounds annually.^[1]

The integration of large language models (LLMs) has expanded digital recreation from visual replication to conversational simulation. By utilizing text-based data as training inputs, algorithms can generate interactive text systems designed to mimic the communication style of specific deceased individuals.

This technology has been applied by private individuals for bereavement purposes. A documented case includes Joshua Barbeau, who utilized a generative language system to create a chatbot based on the text archives of his deceased fiancée

Additionally, researchers have developed conversational simulations of historical and public figures for experimental study. Notable examples include simulated personas of the former Prime Minister of Israel, Golda Meir, and the British author Terry Pratchett. During exploratory testing of the Pratchett simulation, researchers observed anomalous behavioral outputs from the model when it was prompted with information regarding its status as an artificial entity.^[1]

The advancement of digital resurrection technologies has generated academic, ethical, and legal debates across several domains, primarily concerning consent, accuracy, and data security. A primary ethical concern is the lack of explicit consent from deceased individuals regarding the posthumous utilization of their likeness and behavioral data. While some public figures have proactively restricted the posthumous use of their digital likeness - such as actor Robin Williams, whose estate trust barred the commercial exploitation of his name, voice, signature, or likeness until 2039 ^[1] - the vast majority of individuals do not have legal frameworks or explicit provisions to opt out of posthumous digital reconstruction.

Ethical concerns also extend to the misrepresentation of a deceased individual's historical beliefs, lifestyle, or values. For example, a commercial campaign featured a digitally reconstructed Bruce Lee endorsing an alcoholic beverage, despite the fact that Lee abstained from alcohol during his lifetime^[1]. Additionally, conversational models trained on unverified internet data are susceptible to artificial intelligence hallucination. During experimental testing, a large language model simulating former Israeli Prime Minister Golda Meir generated factually incorrect and anomalous claims, including a statement that she had consumed centipedes.^[3]

Furthermore, the creation of high-fidelity digital avatars requires the aggregation and processing of large volumes of personal data, raising privacy concerns. Security analysts note that the storage of such datasets introduces vulnerabilities, including the risk of data breaches, criminal exploitation, or identity theft of deceased individuals for fraudulent activities.

The creation of high-fidelity digital replicas faces distinct aesthetic and cognitive limitations. While the replication of basic physical features has become technologically accessible, the synthesis of precise micro-expressions and dynamic ocular movements remains a primary challenge in character animation. Inconsistencies in these subtle facial behaviors often trigger the uncanny valley effect, wherein viewers experience unease due to near-identical but imperfect human replicas^[1]

Beyond visual fidelity, computational models face cognitive limitations in replicating human consciousness. Large language models frequently fail to capture the nuanced cultural contexts, idiosyncratic experiences, and psychological depth of a specific individual due to the sparse data architectures typically available for private individuals.