Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System

Abstract

In the transition to a data-driven society, organizations have introduced data-driven algorithms that often apply artificial intelligence. In this research, an ethical framework was developed to ensure robustness and completeness and to avoid and mitigate potential public uproar. We take a socio-technical perspective, i.e., view the algorithm embedded in an organization with infrastructure, rules, and procedures as one to-be-designed system. The framework consists of five ethical principles: beneficence, non-maleficence, autonomy, justice, and explicability. It can be used during the design for identification of relevant concerns. The framework has been validated by applying it to real-world fraud detection cases: Systeem Risico Indicatie (SyRI) of the Dutch government and the algorithm of the municipality of Amersfoort. The former is a controversial country-wide algorithm that was ultimately prohibited by court. The latter is an algorithm in development. In both cases, it proved effective in identifying all ethical risks. For SyRI, all concerns found in the media were also identified by the framework, mainly focused on transparency of the entire socio-technical system. For the municipality of Amersfoort, the framework highlighted risks regarding the amount of sensitive data and communication to and with the public, presenting a more thorough overview compared to the risks the media raised.

Explainable AI and interpretable computer vision : from oversight to insight

“…Although we are currently not yet close to Artificial General Intelligence but rather develop AI systems for one or a limited set of tasks, it is generally acknowledged that the common good should be protected and uncontrolled, unintended consequences should be prevented. Beneficence and justice are therefore identified as ethical principles for AI, which require explainability [BK21;FCB+21].…”