Value Sensitive Algorithm Design: Method, Case Study and Lessons
By Haiyi on
Intelligent algorithmic systems are assisting humans to make important decisions in a wide variety of critical domains. Examples include: helping judges decide whether defendants should be detained or released while awaiting trial; assisting child protection agencies in screening referral calls; and helping employers to filter job resumes.
However, technically sound algorithms might fail in multiple ways. First, automation may worsen engagement with key users and stakeholders. For instance, a series of studies have shown that even when algorithmic predictions are proved to be more accurate than human predictions, domain experts and laypeople remain resistant to using the algorithms. Second, an approach that largely relies on automated processing of historical data might repeat and amplify historical stereotypes, discriminations, and prejudices. For instance, African-American defendants were substantially more likely than Caucasian defendants to be incorrectly classified as high-risk offenders by recidivism algorithms.
In this CSCW paper, we propose a novel approach to the design of algorithms, which we call Value-Sensitive Algorithm Design. Our approach is inspired by and draws on Value Sensitive Design and the participatory design approach. We propose that the Value Sensitive Algorithm Design method should incorporate stakeholders’ tacit knowledge and insights into the abstract and analytical process of creating an algorithm. This helps to avoid biases in the design choices and compromises of important stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders’ values, motivations and interests, and help achieve important collective goals. (more…)