Clarifying Decision Making Processes: Tools for Interdependency Modeling

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Baker, Ellie F.

Advisor dc:contributor.advisor

• Dahleh, Munther

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162317

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162317