The workshop focuses on the issue of digital discrimination, particularly towards gender. Its main goal is to help participants improve their digital literacy by understanding the social issues at stake in digital (gender) discrimination, and learning about technical applications and solutions. We make use of our own research in language modelling and Word Embeddings in order to clarify how human gender biases may be incorporated into AI/ML models.
We first offer a short introduction into digital discrimination and (gender) bias. We give examples of gender discrimination in the field of AI/ML, and discuss the clear gender binary (M/F) that is presupposed when dealing with computational bias towards gender. We then move to a technical perspective, introducing the DADD Language Bias Visualiser which allows us to discover and analyse gender bias using Word Embeddings. Finally, we show how computational models of bias and discrimination are built on implicit binaries, and discuss with participants the difficulties pertaining to these assumptions in times of post-binary gender attribution. The workshop will also include pre- and post-workshop questionnaires, which are intended to track participants’ ideas around algorithmic bias.
The workshop derives from our UK EPSRC-funded project Discovering and Attesting Digital Discrimination (DADD), a cross-disciplinary project at King’s College London addressing research questions on digital discrimination involving academic (Computer Science, Digital Humanities, Law and Ethics) and non-academic partners (Google, AI Club), and the general public, including technical and non-technical users.