Algorithm Transparency and Assessment Mechanism Construction in AI Accountability

Abstract

Building an accountability framework for AI is key to ensuring its sustainable development and maintaining public confidence. Two foundations to such a framework are algorithmic transparency and evaluation. Algorithmic transparency has many different aspects and can be achieved in different ways through technical advancement, legal control, industry self-regulation and civic oversight. A solid scientific evaluation framework should consider the lifecycle management of AI, build a multi-dimensional set of indicators to assess technical reliability, ethical compliance and social impact, etc., and make sure that the evaluation results are used for risk mitigation and for tracing responsibility by coordinating and executing cross-stakeholder collaboration. Joint investigation on algorithms transparency and evaluation is a useful theoretical basis and practical framework to address AI governance and responsible innovation issues. It also contributes to public engagement, accountability process and long-term resilience and oversight.