INTERACTIONS. ACM. ORG 60 INTERACTIONS NOVEMBER–DECEMBER 2018 INTERACTIONS. ACM. ORG special topic teams who own data-driven platforms or features are continuously faced with decisions about data collection, maintenance, and modeling. In order for algorithmic-bias efforts to succeed, teams must have support for making these decisions thoughtfully and intentionally. It is also crucial to assess data quality and understand the appropriate measures of effectiveness. At least three complementary types of effort are required (Figure 1). The first is research and analysis to know how to assess and address bias. This involves translating both existing research into the organizational context, as well as case studies into specific products. The second is developing processes that are easy to integrate into existing product cycles. This requires organizational work: education and organizational coordination. The third is engaging with external communities to exchange lessons learned and ensure that the work done internally keeps up with the state of the art. Each of these come with specific challenges that have to be addressed within the product, technical, and organizational contexts.