Hannák Anikó előadása (CEU CNS):

   2017. szeptember 19.

Mindenkit szeretettel várunk az MTA TK "Lendület" RECENS  hálózati előadás-sorozatának következő alkalmára 2017. szeptember 19-án (kedden), melyen Hannák Anikó (CEU) tart előadást "New Faces of Bias in Online Labor Markets" címmel.  (az előadás nyelve angol)

Az előadás megrendezésére az MTA TK "Lendület" RECENS Kutatócsoport tárgyalótermében (MTA Humán Tudományok Kutatóháza, 1097 Budapest, Tóth Kálmán utca 4., T. épület, 1. emelet, 40. szoba) kerül sor 16:00-ás kezdettel.


The internet is fundamentally changing the labor economy. Millions of people use sites like LinkedIn, Upwork or Dribbble to find employment. These services are often driven by algorithms that rate, sort, recommend, and match workers and employers. In theory, many of the mechanisms that cause discrimination in traditional labor markets - cognitive bias, network homophily, statistical discrimination - should be absent from online markets. However, recent studies indicate that these mechanisms do transfer to online platforms, where they may be exacerbated by seemingly harmless design choices. 

In this talk I will investigate three techniques that online platforms use to match users with content: social network algorithms, search algorithms and public review systems. Specifically, I present case studies of 6 different employment platforms, using large scale user data from the employers perspective. I show that biases known from traditional labor markets are indeed present in online platforms, although they manifest in new ways. First, I present results that focus on the visibility of users, which directly impacts the chances of being selected for a job or selling a product. I find that women often receive lower visibility either due to their ranking in the sites’ search interface, or their positions in the underlying social network. Furthermore, I investigate social feedback and other success measures found on user profiles, another important factor in hiring decisions. Overall, my investigations show that demographic features are often correlated with the attention and the social feedback workers and employees receive. Exploring these new forms of inequalities, understanding where social biases enter systems and which mechanisms reinforce them, can be crucial for developing mitigation strategies.