Detection tools being developed to combat the growing threat of deepfakes – realistic-looking false content – must use training datasets that are inclusive of darker skin tones to avoid bias, experts have warned. Most deepfake detectors are based on a learning strategy that depends largely on the dataset that is used for its training. It then uses AI to detect signs that may not be clear to the human eye. This can include monitoring blood flow and heart rate. However, these detection methods do not always work on people with darker skin tones, and if training sets do not contain all ethnicities, accents, genders, ages and skin-tone, they are open to bias, experts warned.

Detection tools being developed to combat the growing threat of deepfakes – realistic-looking false content – must use training datasets that are inclusive of darker skin tones to avoid bias, experts have warned. Most deepfake detectors are based on a learning strategy that depends largely on the dataset that is used for its training. It then uses AI to detect signs that may not be clear to the human eye. This can include monitoring blood flow and heart rate. However, these detection methods do not always work on people with darker skin tones, and if training sets do not contain all ethnicities, accents, genders, ages and skin-tone, they are open to bias, experts warned.

The process worked by feeding a computer lots of still images of one person and video footage of another. Software then used this to generate a new video featuring the former's face in the place of the latter's, with matching expressions, lip-synch and other movements.

Synthetic media generated by AI and represents another dark side of technology and the issue. A deepfake is created by pitting two computer programs against each other -- which are called Generative Adversarial Networks, or GANs. Although this form of Artificial Intelligence has been in the spotlight for the most part of 2019, the biggest news that blew the issue wide open was a LinkedIn user by the name Katie Jones, who appeared on the platform & started connecting with the Who's Who of the political elite in Washington DC. The ease with which deep learning created a real-life image of a person & then penetrated the social media was alarming not just for lawmakers & regulators, but for the general public as well. Lawmakers are especially worried about how this can affect and/or manipulate the 2020 Presidential elections in the U.S. People falling prey to misinformation can greatly jeopardize the transparency of the democratic process.