This  full-day workshop,  co-located with the  HCOMP2018 Conference , aims to bring together a latent  community  of researchers who treat  disagreement (and subjectivity and ambiguity)  as signal, rather than noise. Workshop, induce and derive value from uncertainty, ambiguity and Disagreement. The workshop will include  invited talks, short technical talks and a discussion of medium- and long-term challenges  to fuel future work. We  invite applicant from fields such as computer science, information sciences, law, communication science and political science , as Well as those chief working on Human computation  and crowd   sourcing . Solutions to these conditioned problems will benefit from a diverse set of perspectives.

Ambiguity  creates uncertainty in practically every facet of crowdsourcing. This includes the information presented to workers as part of a task, the instructions for what to with it, and the information they are asked to provide. Also lexical ambiguities, ambiguity can result from missing Details , contradictions and subjectivity. Subjectivity may stem from differences in cultural context, life experiences, or individual perception of hard-to-quantify properties. All of these can leave leave workers with conflicting interpretations, leading to results that requesters—including the end-users Of crowd-powered systems—would regard as "wrong".

Historically, The Human Computation Community has largely controlled the Attributed  disagreement  to Low-Quality Workers. This LED to Mathematical Approaches Intended to Minimize The Supposed Noise. Strategies, included aggregation (EG, Majority, Expectation maximization), Linguistic Approaches, Statistical Filtering and Incentive Design (Among Many others). These are all executed after the data is collected.

Based on interaction design and computer-supported collaborative work to refine task designs until  disagreement  is minimized. This is akin to the methodology used by linguistic annotation and social content analysis of task refinement using inter rater reliability ( Krippendorff, 2013 ). Focus is on minimizing the perceived  ambiguity  or  subjectivity  before the data has been collected .