A Model for Evaluating Algorithmic Systems Accountability
This addresses the need for accountability in algorithmic decision-making, particularly for stakeholders affected by opaque systems, but it is incremental as it builds on existing transparency frameworks.
The paper tackles the problem of evaluating transparency in algorithmic systems by proposing a model that assesses both the algorithm and the organizational maturity, applying it to a financial institution's classification algorithm and finding that the organization had only partial control and lacked benchmarks for result interpretation.
Algorithmic systems make decisions that have a great impact in our lives. As our dependency on them is growing so does the need for transparency and holding them accountable. This paper presents a model for evaluating how transparent these systems are by focusing on their algorithmic part as well as the maturity of the organizations that utilize them. We applied this model on a classification algorithm created and utilized by a large financial institution. The results of our analysis indicated that the organization was only partially in control of their algorithm and they lacked the necessary benchmark to interpret the deducted results and assess the validity of its inferencing.