Data, Responsibly (Vol. 1) Mirror, Mirror

Mirror, Mirror is a science comic authored by Falaah Arif Khan and Julia Stoyanovich.

Falaah is a scientist/engineer by training and an artist by nature. Fascinated by the idea of developing robust and ethical machine learning, Falaah started “Machinelearnist Comics,” a collection of science comics discussing the current landscape of Artificial Intelligence.

Julia, meanwhile, is an assistant professor of Computer Science, Computer Engineering, and Data Science at New York University (NYU). Passionate about responsible data science, Julia leads the “Data, Responsibly” project, which offers an interdisciplinary course on responsible data science.

The comic tackles, in accessible language, themes such as digital accessibility and the biases produced by biased data. In a comedic, critical, and satirical way, it invites us to multiple reflections, offering us several examples of how these kinds of technologies work.

Passages like:

“We look around us and see the hardest problems we know, and decide that since we can’t solve them, we should instead get a machine to do it for us.”

And:

“The data alone can’t tell us whether it’s a distorted reflection of a perfect world, or a perfect reflection of a distorted world, or whether these distortions compound.”

Give us a small glimpse of the rich and relevant content covered by the Data, Responsibly comics.

Mirror, Mirror is educational and made for a general audience. As such, it is a valuable addition to the literature, introducing new readers to the challenges we face in trying to develop ethical and safe Artificial Intelligence.

The first volume of the Data, Responsibly series was translated by Nicholas Kluge Corrêa and Carolina Del Pino Carvalho, and can be accessed at the following link.

To access all volumes of the Data, Responsibly series, click here!