Digital cloning

Intelligent Avatar Platforms (IAP) Intelligent Avatar Platform (IAP) can be defined as an online platform supported by artificial intelligence that allows one to create a clone of themselves. Essentially, the platforms are marketed as a place where one 'lives eternally', as they are able to interact with other avatars on the same platform. IAP is becoming a platform for one to attain digital immortality, along with maintaining a family tree and legacy for generations following to see. Additionally, to ensure that the clone is as close to the original person, companies also encourage interacting with their own clone by chatting and answering questions for them. This allows the algorithm to learn the cognition of the original person and apply that to the clone. Intellitar closed down in 2012 because of intellectual property battles over the technology it used. Potential concerns with IAP includes the potential data breaches and not getting consent of the deceased. IAP must have a strong foundation and responsibility against data breaches and hacking in order to protect personal information of the dead, which can include voice recording, photos, and messages. With deepfakes, industries can cut the cost of hiring actors or models for films and advertisements by creating videos and film efficiently at a low cost just by collecting a series of photos and audio recordings with the consent of the individual. Potential concerns with deepfakes is that access is given to virtually anyone who downloads the different apps that offer the same service. With anyone being able to access this tool, some may maliciously use the app to create revenge porn and manipulative videos of public officials making statements they will never say in real life. This not only invades the privacy of the individual in the video but also brings up various ethical concerns. Voice cloning Voice cloning is a case of the audio deepfake methods that uses artificial intelligence to generate a clone of a person's voice. Voice cloning involves deep learning algorithm that takes in voice recordings of an individual and can synthesize such a voice to the point where it can faithfully replicate a human voice with great accuracy of tone and likeness. Cloning a voice requires high-performance computers. Usually, the computations are done using the Graphics Processing Unit (GPU), and very often resort to the cloud computing, due to the enormous amount of calculation needed. Audio data for training has to be fed into an artificial intelligence model. These are often original recordings that provide an example of the voice of the person concerned. Artificial intelligence can use this data to create an authentic voice, which can reproduce whatever is typed, called Text-To-Speech, or spoken, called Speech-To-Speech. This technology worries many because of its impact on various issues, from political discourse to the rule of law. Some of the early warning signs have already appeared in the form of phone scams and fake videos on social media of people doing things they never did. Protections against these threats can be primarily implemented in two ways. The first is to create a way to analyze or detect the authenticity of a video. This approach will inevitably be an upside game as ever-evolving generators defeat these detectors. The second way could be to embed the creation and modification information in software or hardware. This would work only if the data were not editable, but the idea would be to create an inaudible watermark that would act as a source of truth. In other words, we could know if the video is authentic by seeing where it was shot, produced, edited, and so on. Its gratis and non-commercial nature (with the only stipulation being that the project be properly credited when used), ease of use, and substantial improvements to current text-to-speech implementations have been lauded by users; however, some critics and voice actors have questioned the legality and ethicality of leaving such technology publicly available and readily accessible. Although this application is still in the developmental stage, it is rapidly developing as big technology corporations, such as Google and Amazon are investing vast amounts of money for the development. Some of the positive uses of voice cloning include the ability to synthesize millions of audiobooks without the use of human labor. Also, voice cloning was used to translate podcast content into different languages using the podcaster's voice. Another includes those who may have lost their voice can gain back a sense of individuality by creating their voice clone by inputting recordings of them speaking before they lost their voices. On the other hand, voice cloning is also susceptible to misuse. An example of this is the voices of celebrities and public officials being cloned, and the voice may say something to provoke conflict despite the actual person has no association with what their voice said. In recognition of the threat that voice cloning poses to privacy, civility, and democratic processes, the Institutions, including the Federal Trade Commission, U.S. Department of Justice and Defense Advanced Research Projects Agency (DARPA) and the Italian Ministry of Education, University and Research (MIUR), have weighed in on various audio deepfake use cases and methods that might be used to combat them. == Constructive uses ==

Education Digital cloning can be useful in an educational setting to create a more immersive experience for students. Some students may learn better through a more interactive experience and creating deepfakes can enhance the learning ability of students. One example of this includes creating a digital clone of historical figures, such as Abraham Lincoln, to show what problems he faced during his life and how he was able to overcome them. Another example of using digital clones in an educational setting is having speakers create a digital clone of themselves. Various advocacy groups may have trouble with schedules as they are touring various schools during the year. However, by creating digital clones of themselves, their clones can present the topic at places where the group could not physically make it. These educational benefits can bring students a new way of learning as well as giving access to those who previously were not able to access resources due to environmental conditions. Digital immortality Although digital immortality has existed for a while as social media accounts of the deceased continue to remain in cyberspace, creating a virtual clone that is immortal takes on a new meaning. With the creation of a digital clone, one can not only capture the visual presence of themselves but also their mannerism, including personality and cognition. With digital immortality, one can continue to interact with a representation of their loved ones after they have died. Furthermore, families can connect with the representations of multiple generations, forming a family tree, in a sense, to pass on the family legacy to future generations, providing a way for history to be passed down. == Concerns ==

Fake news With a lack of regulations for deepfakes, there are several concerns that have arisen. Some concerning deepfake videos that can bring potential harm includes depiction of political officials displaying inappropriate behavior, police officers shown as shooting unarmed black men, and soldiers murdering innocent civilians may begin to appear although it may have never occurred in real life. With such hyper-realistic videos being released on the Internet, it becomes very easy for the public to be misinformed, which could lead people to take actions, thus contributing to this vicious cycle of unnecessary harm. Additionally, with the rise in fake news in recent news, there is also the possibility of combining deepfakes and fake news. This will bring further difficulty to distinguishing what is real and what is fake. Visual information can be very convincing to the human eyes, therefore, the combination of deepfakes and fake news can have a detrimental effect on society. Personal use Another reason deepfakes can be used maliciously is for one to sabotage another on a personal level. With the increased accessibility of technologies to create deepfakes, blackmailers and thieves are able to easily extract personal information for financial gains and other reasons by creating videos of loved ones of the victim asking for help. Alternatively, a bad actor could, for example, create a deepfake of a person superimposed onto a video to extract blackmail payment and/or as an act of revenge porn. Creating deepfakes and voice clones for personal use can be extremely difficult under the law because there is no commercial harm. Rather, they often come in the form of psychological and emotional damage, making it difficult for the court to provide a remedy for. However, every platform has a risk of data breach, which could potentially lead to very personal information being accessed by groups that users never consented to. Furthermore, post-mortem privacy comes into question when family members of a loved one tries to gather as much information as possible to create a digital clone of the deceased without the permission of how much information they are willing to give up. == Existing laws in the United States ==

Copyright laws In the United States, copyright laws require some type of originality and creativity in order to protect the author's individuality. However, creating a digital clone simply means taking personal data, such as photos, voice recordings, and other information in order to create a virtual person that is as close to the actual person. In the decision of Supreme Court case Feist Publications Inc. v. Rural Television Services Company, Inc., Justice O'Connor emphasized the importance of originality and some degree of creativity. However, the extent of originality and creativity is not clearly defined, creating a gray area for copyright laws. Creating digital clones require not only the data of the person but also the creator's input of how the digital clone should act or move. In Meshwerks v. Toyota, this question was raised and the court stated that the same copyright laws created for photography should be applied to digital clones. If a deepfake video or digital clone of one arises without their consent, depicting the individual taking actions or making statements that are out of their personality, they can take legal actions by claiming that it is violating their right to publicity. Although the right to publicity specifically states that it is meant to protect the image of an individual in a commercial setting, which requires some type of profit, some state that the legislation may be updated to protect virtually anyone's image and personality. Another important note is that the right of publicity is only implemented in specific states, so some states may have different interpretations of the right compared to other states. == Preventative measures ==

Regulation Digital and digital thought clones raise legal issues relating to data privacy, informed consent, anti-discrimination, copyright, and right of publicity. More jurisdictions urgently need to enact legislation similar to the General Data Protection Regulation in Europe to protect people against unscrupulous and harmful uses of their data and the unauthorised development and use of digital thought clones. By applying a similar logic, they are trying to create a software that takes each frame of a given video and analyze it pixel by pixel in order to find the pattern of the original video and determine whether or not it has been manipulated. In addition to developing new technology that can detect any video manipulations, many researchers are raising the importance for private corporations creating stricter guidelines to protect individual privacy. Furthermore, it is also important to educate people on how to protect the information they put out on the Internet. By increasing the digital literacy of the public, people have a greater chance of determining whether a given video has been manipulated as they can be more skeptical of the information they find online. == See also ==