Publications

• COVID-19 has conferred new experiential knowledge on society and a rare opportunity to better understand the social model of disability and to improve the lives of persons with disabilities.
• The COVID-19 experience may offer contextual knowledge of the prepandemic lives of persons with disabilities and foster greater social awareness, responsibility and opportunities for change towards a more inclusive society.
• Information, family and social relationships, health protection and healthcare, education, transport and employment should be accessible for all groups of the population. The means must be developed and deployed to ensure equity – the deployment of resources so that people with different types of needs have the same opportunities for living good lives in inclusive communities.
• We have learnt from COVID-19 that inclusive healthcare and universal access should be the new normal, that its provision as a social good is both unifying and empowering for society as a whole.

Ebuenyi ID, Smith EM, Holloway C, et alCOVID-19 as social disability: the opportunity of social empathy for empowermentBMJ Global Health 2020;5:e003039.

Assistive technology is a critical component of maintaining health, wellbeing, and the realization of rights for persons with disabilities. Assistive technologies, and their associated services, are also paramount to ensuring individuals with functional limitations have access to important health and social service information, particularly during a pandemic where they may be at higher risk than the general population. Social isolation and physical distancing have further marginalized many within this population. We have an opportunity to learn from the COVID-19response to develop more inclusive and resilient systems that will serve people with disabilities more effectively in the future. In this Current Issues piece, we present a starting point for discussion, based on our experiences working to promote access to assistive technologies through inclusive and sustainable systems and policies.

Emma M. Smith, Malcolm MacLachlan, Ikenna D. Ebuenyi, Catherine Holloway & Victoria Austin (2021) Developing inclusive and resilient systems: COVID-19 and assistive technology, Disability & Society, 36:1, 151-154, DOI: 10.1080/09687599.2020.1829558

Amartya Sen’s seminal Tanner lecture: Equality of What? began a contestation on social justice and human wellbeing that saw a new human development paradigm emerge—the capability approach (CA)—which has been influential ever since. Following interviews with leading global assistive technology (AT) stakeholders, and users, this paper takes inspiration from Sen’s core question and posits, AT for what? arguing that AT should be understood as a mechanism to achieve the things that AT users’ value. Significantly, our research found no commonly agreed operational global framework for (disability) justice within which leading AT stakeholders were operating. Instead, actors were loosely aligned through funding priorities and the CRPD. We suggest that this raises the possibility for (welcome and needed) incoming actors to diverge from efficiently designed collective action, due to perverse incentives enabled by unanchored interventions. The Global Report on Assistive Technology (GReAT) helps, greatly! However, we find there are still vital gaps in coordination; as technology advances, and AT proliferates, no longer can the device-plus-service approach suffice. Rather, those of us interested in human flourishing might explore locating AT access within an operational global framework for disability justice, which recognizes AT as a mechanism to achieve broader aims, linked to people’s capabilities to choose what they can do and be.

Austin, V.; Holloway, C. Assistive Technology (AT), for What? Societies 2022, 12, 169. https://doi.org/10.3390/soc120...

Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. Millions of students are already starting to benefit from the use of these technologies, but millions more around the world are not. If this trend continues, the first delivery of AI in Education could be greater educational inequality, along with a global misallocation of educational resources motivated by the current technological determinism narrative. In this paper, we focus on speculating and posing questions around the future of AI in Education, with the aim of starting the pressing conversation that would set the right foundations for the new generation of education that is permeated by technology. This paper starts by synthesising how AI might change how we learn and teach, focusing specifically on the case of personalised learning companions, and then move to discuss some socio-technical features that will be crucial for avoiding the perils of these AI systems worldwide (and perhaps ensuring their success). This paper also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We also emphasise the need for collectively designing human-centered, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as support new emerging pedagogies. Finally, we ask what would it take for this educational revolution to provide egalitarian and empowering access to education, beyond any political, cultural, language, geographical and learning ability barriers.

Extracting useful information from the user history to clearly understand informational needs is a crucial feature of a proactive information retrieval system. Regarding understanding information and relevance, Wikipedia can provide the background knowledge that an intelligent system needs. This work explores how exploiting the context of a query using Wikipedia concepts can improve proactive information retrieval on noisy text. We formulate two models that use entity linking to associate Wikipedia topics with the relevance model. Our experiments around a podcast segment retrieval task demonstrate that there is a clear signal of relevance in Wikipedia concepts while a ranking model can improve precision by incorporating them. We also find Wikifying the background context of a query can help disambiguate the meaning of the query, further helping proactive information retrieval.