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Decisions impacting human lives are increasingly being made or assisted byautomated decision-making algorithms. Many of these algorithms process personaldata for predicting recidivism, credit risk analysis, identifying individualsusing face recognition, and more. While potentially improving efficiency andeffectiveness, such algorithms are not inherently free from bias, opaqueness,lack of explainability, maleficence, and the like. Given that the outcomes ofthese algorithms have a significant impact on individuals and society and areopen to analysis and contestation after deployment, such issues must beaccounted for before deployment. Formal audits are a way of ensuring algorithmsmeet the appropriate accountability standards. This work, based on an extensiveanalysis of the literature and an expert focus group study, proposes a unifyingframework for a system accountability benchmark for formal audits of artificialintelligence-based decision-aiding systems. This work also proposes systemcards to serve as scorecards presenting the outcomes of such audits. Itconsists of 56 criteria organized within a four-by-four matrix composed of rowsfocused on (i) data, (ii) model, (iii) code, (iv) system, and columns focusedon (a) development, (b) assessment, (c) mitigation, and (d) assurance. Theproposed system accountability benchmark reflects the state-of-the-artdevelopments for accountable systems, serves as a checklist for algorithmaudits, and paves the way for sequential work in future research.

System Cards for AI-Based Decision-Making for Public Policy