Information and Communication Technologies (ICTs) are designed to support job seekers’ ability to search for jobs, create resumes, highlight skills, and share employment opportunities. However, the benefits of employment tools and technologies are unequally distributed. ICTs lack advantages for individuals with limited knowledge, skills, or experience to leverage them. Without an understanding of how people from low-resource settings use ICTs for job seeking, the same employment inequalities that occur offline will be repeated in online contexts.

Machine learning models are becoming increasingly opaque to human examination, even to their designers.  Yet these models are also increasingly used to make high-stakes decisions; who goes to jail, what neighborhoods police deploy to, and who should be hired for a job.  But how can we practically achieve accountability and transparency in the face of increasingly complex models?  And how do we know if the algorithmic decisions are fair or discriminatory - what does it mean for an algorithm to be fair?  In this talk, we’ll discuss recent work from the new and growing field of Fairness, Accou