WeBuildAI

By Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas and 1 more. 2019

154 citations recorded by the source corpus.

Algorithms increasingly govern societal functions, impacting multiple stakeholders and social groups. How can we design these algorithms to balance varying interests in a moral, legitimate way? As one answer to this question, we present WeBuildAI, a collective participatory framework that enables people to build algorithmic policy for their communities. The key idea of the framework is to enable stakeholders to construct a computational model that represents their views and to have those models vote on their behalf to create algorithmic policy. As a case study, we applied this framework to a matching algorithm that operates an on-demand food donation transportation service in order to adjudicate equity and efficiency trade-offs. The service's stakeholders--donors, volunteers, recipient organizations, and nonprofit employees--used the framework to design the algorithm through a series of studies in which we researched their experiences. Our findings suggest that the framework successfully enabled participants to build models that they felt confident represented their own beliefs. Participatory algorithm design also improved both procedural fairness and the distributive outcomes of the algorithm, raised participants' algorithmic awareness, and helped identify inconsistencies in human decision-making in the governing organization. Our work demonstrates the feasibility, potential and challenges of community involvement in algorithm design.

Subfields: Computer Science Applications, Information Systems, Safety Research, AI Ethics, Fairness & Society, Human-Computer Interaction

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Citations in the corpus

Improving alignment of dialogue agents via targeted human judgements (2022, background): “In future, more participatory inputs (Berditchevskaia et al., 2021; Halfaker and Geiger, 2020; Lee et al., 2019) from other stakeholders will be critical for developing language agents that are both legitimate and aligned to the needs of its users.”
Aligning AI Optimization to Community Well-Being (2020, methodology): “The WeBuildAI method (Lee et al. 2019) demonstrates what participatory design of metrics might look like.”
ORES (2019, background): “Recent work has focused on more participatory [63], value-sensitive [112], and experiential design [3] approaches to machine learning.”