论文标题
部分可观测时空混沌系统的无模型预测
Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making
论文作者
论文摘要
随着算法在整个行业和政府之间的普遍性越来越多，越来越多的工作努力应对如何理解其社会影响和道德意义。在算法开发的不同阶段使用了各种方法，以鼓励研究人员和设计师考虑其研究的潜在社会影响。在这个领域中的一个局面而有前途的领域正在利用参与性的远见来预测这些不同的社会影响。我们采用众包作为参与性的远景手段，根据一组政府算法决策制定工具来揭示四种不同类型的影响区域：（1）感知的价值，（2）社会领域，（3）特定的抽象影响类型，以及（4）道德算法问题。我们的发现表明，这种方法有效地利用人群的认知多样性来揭示一系列问题。我们进一步分析了所确定的影响区域的相互作用中的复杂性，以证明众包如何阐明影响之间的连接模式。最终，这项工作确立了众包作为预期算法影响的有效手段，通过利用参与性的远见和认知多样性，可以补充其他方法来评估社会中的算法。
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.
