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With the increasing pervasiveness of algorithms across industry andgovernment, a growing body of work has grappled with how to understand theirsocietal impact and ethical implications. Various methods have been used atdifferent stages of algorithm development to encourage researchers anddesigners to consider the potential societal impact of their research. Anunderstudied yet promising area in this realm is using participatory foresightto anticipate these different societal impacts. We employ crowdsourcing as ameans of participatory foresight to uncover four different types of impactareas based on a set of governmental algorithmic decision making tools: (1)perceived valence, (2) societal domains, (3) specific abstract impact types,and (4) ethical algorithm concerns. Our findings suggest that this method iseffective at leveraging the cognitive diversity of the crowd to uncover a rangeof issues. We further analyze the complexities within the interaction of theimpact areas identified to demonstrate how crowdsourcing can illuminatepatterns around the connections between impacts. Ultimately this workestablishes crowdsourcing as an effective means of anticipating algorithmicimpact which complements other approaches towards assessing algorithms insociety by leveraging participatory foresight and cognitive diversity.

Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making