Clarifying Decision Making Processes: Tools for Interdependency Modeling
Name
baker-efbaker-sm-tpp-2025-thesis.pdf
Description
Thesis PDF
Size
3.4 MB
Format
Adobe PDF
Checksum (MD5)
1ca98637323a3df3c3d080360bbf25c8
Author(s)
Baker, Ellie F.
Advisor(s)
Dahleh, Munther
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Tools for problem specification in AI Decision making are underdeveloped at present. I propose two new tools for this purpose; first, a model of AI Decision Making, which supports problem identification and mitigation. Second, a Bill of Assumptions for Data Production. Data is an important component of AI Decision Making Systems, and data is necessarily produced by making a series of assumptions. My Bill of Assumptions for Data Production is a new approach to communicating these assumptions that facilitates collaboration, data transparency, and reduction of harmful bias. I illustrate this new approach by developing a dataset that estimates the distribution of Government education spending in the US across income deciles. My dataset informs existing Distributional National Accounts (DINA), which are a primary measure of income inequality in the US (Piketty et al., 2018). My estimate shows Government education spending is more progressive than assumed in current DINA. Furthermore, I show that removing federal education funding to postsecondary institutions would produce substantial harm.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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