Compounding barriers to fairness in the digital technology ecosystem

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology innovation, delivery and access. Amongst these are barriers and omissions at the earliest stages of technology intentionality and design; systemic inadequacies in sensing systems that deteriorate performance for individuals based on ethnicity, age and physicality; system design, co-requisite and interface decisions that limit access; biases and inequities in datasets and algorithms; and limiting factors in system function and security. Additionally, there are concerns about unethical and illegal practices amongst digital technology providers: for example, in planned obsolescence and anti-competitive behaviors, failings in data practices and security, and in responses to problematic use and behaviors. It is critical that these failings are identified and addressed to better evolve a fairer future digital technology ecosystem. This paper contributes a perspective on technological stewardship and innovation; it identifies the compounding nature of barriers to fairness in the current digital technology ecosystem, and contrasts these with the non-compounding fairness drivers that, in general, establish minimum requirements

Indirect Discrimination: Huduma Namba (Digital Identification) and the Plight of the Nubian Community in Kenya

Years after Kenya’s independence, the Nubians in Kenya are yet to enjoy the status of being fully-fledged citizens in their country. This is due to a variety of factors including the government’s refusal to formally acknowledge them as citizens, and its reluctance to streamline the current vetting process despite the overwhelming proof of its shortcomings. The discriminatory approach in the issuance of Kenyan identity cards (IDs) through the vetting process on grounds of religion and ethnicity not only entrenches the social, political, and economic exclusion of Nubians in Kenya but is also prohibited under Article 27(4) of the Constitution as indirect discrimination. Without taking adequate steps to change the status quo, the Kenyan government has instead launched a new digital identification system whose enrolment requires citizens’ IDs. Despite the full roll-out being halted by the court on grounds of data protection concerns, the switch to the Huduma Namba system is nonetheless set to disproportionately affect the ability of Nubians to participate as Kenyan citizens and contribute to their ‘otherness’. Consequently, this paper argues that the mandatory operationalisation of the Huduma Namba system in Kenya will constitute indirect discrimination against the Nubian community. It conducts this assessment by discussing the moral wrongfulness of indirect discrimination and laying out the architecture of indirect discrimination law in Kenya

Organizational Governance of Emerging Technologies: AI Adoption in Healthcare

Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO.org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare

Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

Machine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, research communities are slow to adopt them. Canonical Workflow Framework for Research (CWFR) provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers. This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate, focusing on HPC and big data. Specifically, we discuss Fair Digital Object (FDO) and Research Object (RO) in the DeepRain project to achieve granular reproducibility. DeepRain is a project that aims to improve precipitation forecast in Germany by using ML. Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access. We suggest the Juypter notebook as a single reproducible experiment. In addition, we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface

Steering Capital: Optimizing Financial Support for Innovation in Public Education

Examines efforts to align capital to education innovation and calls for clarity and agreement on problems, goals, and metrics; an effective R&D system; an evidence-based culture of continuous improvement; and transparent, comparable, and useful data

The best chance for all: a policy roadmap for post-pandemic panic

The Best Chance for All was developed in 2018 as a long-term policy vision for student equity in Australian tertiary education. We argue in this article that COVID-19 has exacerbated the issues that the policy vision sought to address and has increased demands on and of post-secondary education. Specifically, we argue that the magnitude of the social and economic challenges presented by COVID-19 warrants holistic policy responses that enable the transition to a connected tertiary education system; one designed to deliver choice and flexibility for lifelong learners. A roadmap for this transition exists in the form of The Best Chance For All. The vision can be actuated through demand driven funding arrangements across tertiary education that are coherently aligned to optimise the performance of both the higher and vocational education sectors and are underpinned by sustained investment in equity outreach and support