• 
• 7 min
• Kinolab
• 2013

In this film, actress Robin Wright plays a fictionalized version of herself as an actress whose popularity is declining. Her agent Al exposes her to deep fake technology which creates a virtual version of an actor to play a role in any number of scenarios or films. These “actors” are 3D holographs with AI that have been trained to replicate the real person which they imitate. However, Robin is disconcerted with the lack of agency that she would have in deciding how her image and identity appeared in these movies.

• Control of Personal Data/Identity
• Deep Fake
• Machine Learning
• 3D Imaging

• 
• 16 min
• Kinolab
• 2004

Joel Barish recently broke up with Clementine, his girlfriend of two years, in a brutal argument. After discovering that she has used a procedure known as Lacuna to erase him from her memories, Joel decides to undergo the same procedure to forget that he ever knew Clementine. The procedure uses a brain-computer interface to map the areas of Joel’s brain that are active whenever he has a memory of Clementine, first when he is awake and using associated objects to perform active recall and then when he is asleep and subconsciously remembering her. Despite Joel’s eventual regrets and desperate attempts to remember Clementine, the procedure is successful, and he forgets her. However, Joel and Clementine reunite in the real world after their respective procedures, and as they have a fresh start, they end up listening to Clementine’s tape from before the procedure where she dissects all of the flaws of Joel and their relationship.

• Digital Censorship
• Edited Humans
• Computer Vision
• Brain-Computer Interfaces

• 
• 10 min
• New York Times
• 2019

Racial bias in facial recognition software used for Government Civil Surveillance in Detroit. Racially biased technology. Diminishes agency of minority groups and enhances latent human bias.

• Civil Surveillance
• Algorithmic Bias
• Computer Vision
• Data Streaming

• 
• 27 min
• Cornell Tech
• 2019

Podcast about worker quantification in factors such as hiring, productivity and more. Dives into the discussion on why we should attempt a fair making of algorithms. Warns specifically about how algorithms can find “proxy variables” to approximate for cultural fits like race or gender even when the algorithms is supposedly controlled for these factors.

• Algorithmic Bias
• Technology and Race
• Machine Learning

• 
• 10 min
• Survival of the Best Fit
• 2018

Explores hiring bias of AI by playing a game in which you are the hiring manager.

• Algorithmic Bias
• Technology and Race
• Machine Learning

• 
• 7 min
• n/a
• 2018

Exploration of how, through facial and emotion recognition, digital artifacts make decisions on what we may want or need, and what they are able to do with this data.

• Data Privacy
• Computing Ethics Issues
• Computer Vision
• Data Mining