Uncertain Facing (2020) is a data-driven, interactive audiovisual installation that aims to represent the uncertainty of data points of which their positions in 3D space are estimated by machine learning (ML) techniques. Uncertain Facing visualizes the real-time clustering of fake faces in 3D space through t-SNE, a non-linear dimensionality reduction technique, with face embeddings of the faces. However, unlike the original purpose of t-SNE that is for objective data exploration, it represents data points as metaballs making two or more faces a merged face when they are close, to reflect the uncertain, probabilistic nature of data locations the algorithm yields. Uncertain Facing also sonifies the change of the overall data distribution in 3D space. This multimodal data representation also reflects error values, which t-SNE measures at each iteration between a distribution in original high dimensions and a deduced low-dimensional distribution, to represent the uncertainty of data as jittery motion and inharmonic sound. Uncertain Facing allows the audience to take a picture of their face as new data and to find their face being merged with similar fake faces, raising concerns about the unintended use of ML with synthetic/fake data.
More information can be found at https://sihwapark.com/Uncertain-Facing
