Coding Happiness
Investigating the Social Implications of Emotion Recognition Technologies
Step 1:
Upload your picture
We respect your privacy. Your photos will not be stored or used for any other purpose, but for the one time emotion recognition assessment that you will see the results of.
Step 2:
Answer four easy questions
While we will store your answers, they will not be associated with your picture or name. Your answers are completely anonymous.
Step 3:
Help us rate the algorithm
Be the judge of the success of an AI tool designed to recognize human emotion. By comparing your answers to those of the algorithm, we make the human subjective perception the final authority on the recognition and assessment of human emotion.
DIGITAL USES OF EMOTION
Digital uses of emotion call for a re-evaluation of our understanding of the notion. This content analysis study builds an inventory of new uses of human emotion in their respective digital contexts, as well as keeps track of the language that describes and validates the digital applications of human emotion.
LOST IN TRANSLATION: the problem of algorithmic representation of emotion
Algorithmic representation of emotion rests on the assumed translatability between signs, signification systems, and experience. This study examines some of the inherent problems in algorithmic representation of emotion and the process of dividing expressions of emotion into basic meaning-carrying components which are then translated into code.
100% HAPPY: WHO’S TO ARGUE WITH THE AI ALGORITHM?
This case study questions the criteria for accuracy of automatic emotion recognition applications. In it I assess about a hundred personally taken photographs of people in various contexts and I run them through FaceReader and Skybiometry – two online demos of emotion recognition APIs (application programming interface). Comparing the automatic assessment of emotion values with my own subjective perception of the photographs raises the opportunity to note and classify disparities between the subjective and the automated readings of these facial expressions, as well as pose the question: whose criteria is more valid – the human and subjective one or that of the ‘impartial’ AI algorithm?
