  With the increasing pervasiveness of algorithms across industry and
government, a growing body of work has grappled with how to understand their
societal impact and ethical implications. Various methods have been used at
different stages of algorithm development to encourage researchers and
designers to consider the potential societal impact of their research. An
understudied yet promising area in this realm is using participatory foresight
to anticipate these different societal impacts. We employ crowdsourcing as a
means of participatory foresight to uncover four different types of impact
areas 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 is
effective at leveraging the cognitive diversity of the crowd to uncover a range
of issues. We further analyze the complexities within the interaction of the
impact areas identified to demonstrate how crowdsourcing can illuminate
patterns around the connections between impacts. Ultimately this work
establishes crowdsourcing as an effective means of anticipating algorithmic
impact which complements other approaches towards assessing algorithms in
society by leveraging participatory foresight and cognitive diversity.
