  Machine learning (ML) based approaches are increasingly being used in a
number of applications with societal impact. Training ML models often require
vast amounts of labeled data, and crowdsourcing is a dominant paradigm for
obtaining labels from multiple workers. Crowd workers may sometimes provide
unreliable labels, and to address this, truth discovery (TD) algorithms such as
majority voting are applied to determine the consensus labels from conflicting
worker responses. However, it is important to note that these consensus labels
may still be biased based on sensitive attributes such as gender, race, or
political affiliation. Even when sensitive attributes are not involved, the
labels can be biased due to different perspectives of subjective aspects such
as toxicity. In this paper, we conduct a systematic study of the bias and
fairness of TD algorithms. Our findings using two existing crowd-labeled
datasets, reveal that a non-trivial proportion of workers provide biased
results, and using simple approaches for TD is sub-optimal. Our study also
demonstrates that popular TD algorithms are not a panacea. Additionally, we
quantify the impact of these unfair workers on downstream ML tasks and show
that conventional methods for achieving fairness and correcting label biases
are ineffective in this setting. We end the paper with a plea for the design of
novel bias-aware truth discovery algorithms that can ameliorate these issues.
