Rookwood
Collaborative Work and Community to Address and Prevent Algorithmic Bias (304771)
*Norma Padron, American Hospital AssociationKeywords: data ethics, community, algorithmic bias, data tools
We are at the cusp of the fourth paradigm, increasingly grasping the power of data-intensive science and with this, its opportunities and challenges as we move away from hypothesis-driven to data-driven science. Among these challenges is the realization that algorithmic design and the data used for it may have profound societal implications across dimensions such as fair housing, economic opportunity and discrimination.
One of the key challenges in understanding and addressing bias is that there are multiple, different categories of bias and definitions, many of which are context-specific. In statistics, bias simply refers to an estimate’s deviation from a statistical standard. As algorithms are used to make automated decisions or as input to decisions made by people, other standards become important to prevent moral bias, regulatory bias, social bias, psychological bias and others.
The main goal of this discussion article is to outline strategies for active and engaged collaborative design and prototyping of a set of digital tools and frameworks that can aid the understanding of algorithimic bias through community development.