Diversity matters: Robustness of bias measurements in Wikidata

Paramita Das, Sai Keerthana Karnam, Anirban Panda, Bhanu Prakash Reddy Guda, Soumya Sarkar, Animesh Mukherjee

Abstract: With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack in the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. In particular, we find that the embedding algorithm ComplEx is more robust to the choice of demographics compared to TransE. Subsequently, we observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. This is often overlooked by most of the coarse-grained approaches working at the aggregate level. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain itself from the popular dogma of “one-size-fits-all”.