Search results

The purpose of the study detailed here was to engage with Directors of Centres for Doctoral Training (CDTs) during the first year of their new Centres to form a snapshot view of the nature and type of training that was being incorporated and how this might affect the wider institution – in this case the university. Using an organisational learning lens, this paper empirically examines the work-in-progress of the responsible innovation (RI) training in CDTs to assess how new RI understandings are being created, retained and transferred within the CDTs, questioning whether this process represents a programme of “institutionalisation”.

During the past decade, RI has become increasingly embedded within the EU and UK research context, appearing with greater frequency in funding calls and policy spaces. As part of this embedding, in its 2018 funding call for CDTs, the Engineering and Physical Sciences Research Council (EPSRC) required RI training to be included in the programme for all doctoral students.

The paper concludes that, at present, institutionalisation is highly variegated, with the greater organisational change required to truly embed RI mindsets.

The paper provides original, empirical research evidence of RI institutionalisation in UK CDTs, and, using a “learning organisation” lens, examines areas of value to both RI and learning organisation theory.

The purpose of this paper is to report on empirical work conducted to open up algorithmic interpretability and transparency. In recent years, significant concerns have arisen regarding the increasing pervasiveness of algorithms and the impact of automated decision-making in our lives. Particularly problematic is the lack of transparency surrounding the development of these algorithmic systems and their use. It is often suggested that to make algorithms more fair, they should be made more transparent, but exactly how this can be achieved remains unclear.

An empirical study was conducted to begin unpacking issues around algorithmic interpretability and transparency. The study involved discussion-based experiments centred around a limited resource allocation scenario which required participants to select their most and least preferred algorithms in a particular context. In addition to collecting quantitative data about preferences, qualitative data captured participants’ expressed reasoning behind their selections.

Even when provided with the same information about the scenario, participants made different algorithm preference selections and rationalised their selections differently. The study results revealed diversity in participant responses but consistency in the emphasis they placed on normative concerns and the importance of context when accounting for their selections. The issues raised by participants as important to their selections resonate closely with values that have come to the fore in current debates over algorithm prevalence.

This work developed a novel empirical approach that demonstrates the value in pursuing algorithmic interpretability and transparency while also highlighting the complexities surrounding their accomplishment.