Algorithmic decision-making (ADM) is becoming increasingly prevalent in society, due to the rapid technological developments in Artificial Intelligence. ADM make substantially impactful decisions about people: diagnosing whether we have a disease, what news and which ads we get to see, whether we are eligible for a job, benefits, a college or a loan, they show us personalized media and news, and steer the car that drives us home. However, ADM brings about ethical, legal and social issues by inheriting and perpetuating human biases, learning to discriminate—even learning gender or racial stereotypes, and lacking transparency and accountability. This unexpected and biased behaviour arises because these software systems are usually built without the specification of fairness requirements (i.e. what fair behaviour is expected of the system). We envision a Software Engineering for Values (SEfV) method that solves this problem. This study addresses that specification problem, aiming to help practitioners design ADM software for fairness. Using literature in social sciences—specifically organizational justice—the human value of fairness has been conceptualized in regard to ADM. This resulted in a fairness tree with four dimensions (procedural, distributive, informational and interpersonal fairness), which is further specified into 31 fairness norms. Subsequently, the fairness tree is related to current measures of fairness and techniques. Finally, we put forward the Software Engineering for Values (SEfV) framework, based on the principles of Software Engineering and Design for Values, and show how it can be applied to design ADM for fairness. Experiments were conducted where participants (N = 12) performed a design task (M = 3, 75 requirements specified) and an audit task for a hypothetical loan decision system—using a prototype of the SEfV framework. Participants found the prototype useful for both design as auditing, especially as a tool for reflecting on fairness considerations. This suggests that a high fidelity version would be useful for practitioners.

To achieve more desirable futures, we need to completely overhaul our current production, consumption, and innovation systems, requiring practices that go beyond merely understanding these systems and their problems. While the necessity for systemic changes to address grand societal challenges is widely supported across diverse schools of thought, systems innovation faces two significant limitations. First, many approaches to systems innovation focus primarily on understanding and mapping systems, seldom enabling robust change towards achieving desirable futures. Second, and relatedly, the literature on systems innovation (SI) is fragmented and built upon different fields with contradictory ontological assumptions—some suggest changes can be strategically planned and managed, while others fundamentally reject this notion. This fragmentation and these ontological contradictions hinder robust actions towards desirable futures by increasing the divide across disciplines, antagonizing the very core principles of system innovation that demand transdisciplinarity. Therefore, the central research question of this paper is: How can the integration and alignment of diverse approaches within systems innovation overcome current limitations and better support the transition towards desirable futures? In this paper, we argue that the conceptualization of ‘systems innovation’ stems from three distinct approaches that have largely remained unconnected: 1) Research and innovation approaches, 2) Systems thinking approaches, and 3) Societal transition approaches. This paper aims to bring conceptual clarity by offering a comprehensive overview of these fields ultimately arriving at a re-conceptualization of systems innovation. While these different fields have largely developed independently, we find that their alignment facilitates a more cohesive and effective re-conceptualization of ‘systems innovation’.@en

To achieve more desirable futures, we need to completely overhaul our current production, consumption, and innovation systems, requiring practices that go beyond merely understanding these systems and their problems. While the necessity for systemic changes to address grand societal challenges is widely supported across diverse schools of thought, systems innovation faces two significant limitations. First, many approaches to systems innovation focus primarily on understanding and mapping systems, seldom enabling robust change towards achieving desirable futures. Second, and relatedly, the literature on systems innovation (SI) is fragmented and built upon different fields with contradictory ontological assumptions—some suggest changes can be strategically planned and managed, while others fundamentally reject this notion. This fragmentation and these ontological contradictions hinder robust actions towards desirable futures by increasing the divide across disciplines, antagonizing the very core principles of system innovation that demand transdisciplinarity. Therefore, the central research question of this paper is: How can the integration and alignment of diverse approaches within systems innovation overcome current limitations and better support the transition towards desirable futures? In this paper, we argue that the conceptualization of ‘systems innovation’ stems from three distinct approaches that have largely remained unconnected: 1) Research and innovation approaches, 2) Systems thinking approaches, and 3) Societal transition approaches. This paper aims to bring conceptual clarity by offering a comprehensive overview of these fields ultimately arriving at a re-conceptualization of systems innovation. While these different fields have largely developed independently, we find that their alignment facilitates a more cohesive and effective re-conceptualization of ‘systems innovation’.@en