“I Treat Everyone with Respect”: Debt Collection Attorneys as Agents of Institutionalized Racism in a Color-blind America

How do debt collection attorneys understand their work in light of increased regulation of the industry and its historic structural racism? Drawing on over sixty hours of observation in two small claims courts, analysis of three months of cases, and semi-structured interviews with eight debt collection attorneys, I argue that attorneys reinforce the instutionalized racism of debt collection through their use of color-blind racism. Attorneys understand the state of the debt collection industry as inevitable, denying inconsistencies in their practice that privilege white defendants. Additionally, attorneys view themselves as helping rather than exploiting debtors, in contrast to frequent aggressive action without regard to its consequences for defendants’ lives. Attorneys who act with greater flexibility demonstrate the potential for lawyers to challenge the institutional racism of debt collection. However, the historic and contemporary stigma associated with debt collection in addition to the lack of professional prestige available to these attorneys gives significance to color-blindness not only as an explanatory device but, also, as a stigma management strategy. The necessity of stigma management in addition to the lack of professional stability and autonomy for many debt collection attorneys complicates the potential for future change

DITURIA: A Framework for Decision Coordination Among Multiple Agents

Decision making in multi-agent settings is a complex exercise where agents have to handle incomplete knowledge of the complete problem. Agents are interdependent in multi-agent decision making, being subject to the decisions of other agents who bring to bear other qualitative and quantitative criteria. Some aspects of this problem have been addressed in the Distributed Constraint Optimisation Problems (DCOP) and Markov Decision Processes literature. Taking inspiration from a medical example, our objective in this paper is to provide a framework to support multi-agent decision coordination. This method can be applied in scenarios where we seek to combine qualitative preferences on projected final states with assessment made using utility/objective functions, while accounting for partial agent knowledge