Decisions impacting human lives are increasingly being made or assisted by automated decision-making algorithms. Many of these algorithms process personal data for predicting recidivism, credit risk analysis, identifying individuals using face recognition, and more. While potentially improving efficiency and effectiveness, such algorithms are not inherently free from bias, opaqueness, lack of explainability, maleficence, and the like. Given that the outcomes of these algorithms have a significant impact on individuals and society and are open to analysis and contestation after deployment, such issues must be accounted for before deployment. Formal audits are a way of ensuring algorithms meet the appropriate accountability standards. This work, based on an extensive analysis of the literature and an expert focus group study, proposes a unifying framework for a system accountability benchmark for formal audits of artificial intelligence-based decision-aiding systems. This work also proposes system cards to serve as scorecards presenting the outcomes of such audits. It consists of 56 criteria organized within a four-by-four matrix composed of rows focused on (i) data, (ii) model, (iii) code, (iv) system, and columns focused on (a) development, (b) assessment, (c) mitigation, and (d) assurance. The proposed system accountability benchmark reflects the state-of-the-art developments for accountable systems, serves as a checklist for algorithm audits, and paves the way for sequential work in future research.
Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Center for Information Technology Policy, Princeton University（普林斯顿大学信息技术政策中心）；Center for Advanced Study in the Social Sciences, Stanford University（斯坦福大学社会科学高等研究中心）
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
MIT Media Lab（麻省理工学院媒体实验室）；Facebook Research（脸书研究院）；Oak Ridge National Laboratory（橡树岭国家实验室）；Harvard University（哈佛大学）
Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
MIT Media Lab（麻省理工学院媒体实验室）；Facebook Research（脸书研究院）；Oak Ridge National Laboratory（橡树岭国家实验室）；Harvard University（哈佛大学）
