Values

The I-CRL is a value based lab. It follows the Professor Michael Cooley ethos of human centered systems. In 2020 a lab manifesto was written here (open access paper).

A new manifesto for systems engineering design praxis

1. We embrace open ended childlike curiosity, the raising of new questions and looking at old problems through different angles without fear of derision.
2. We embrace both success and failure because of the valuable knowledge gained from them.
3. We embrace the widespread sharing of knowledge gained from experiences along the way.
4. We embrace the challenge to materialise the digital humanities stewards vision.
5. We embrace human creativity, collaboration and problem solving in the form of “makers”.
6. We embrace socially responsible systems and socially useful products.
7. We embrace acknowledge and anticipate post solution “bricolage” (virtuoso tinkering) as a natural occurrence striving to harness benefits while minimising entropy.
8. We embrace the dedication of craftsmanship in sculping viable solutions.
9. We embrace support and encourage the unique and colourful people we work with.
10. We embrace and invite in all those who feel marginalised.
11. We embrace opportunities to guide eager minds in the ways of HCS.
12. We embrace the diligent safeguarding of artefacts and embedded wisdom in our care during development.
13. We embrace safeguarding all stakeholders against negative risk(privacy violations or any injustice) during any endeavours that are informed by the system.
14. We embrace a repeatable process over a methodology.

The ENRICHER method which followed is is underpinned by these set of values

The ENRICHER method for human machine symbiotics & smart data: A socially responsible approach to the intelligent augmentation of knowledge work.

1. Ethos Centric: ethos of development continuously revisited and reviewed. Important to articulate and rearticulate core values of development.
2. eNgagement as an Outcome: Shift from “why are we doing this” questions to “how are we engaging together on this” question.
3. Reuse Machine Knowledge: Reuse and extending existing knowledge models rather than predefining total schema where possible before implementing.
4. Insights from Context: Derive technology to fit the context—of-tacit-knowledge-use. Means acquiring an understanding of knowledge-in-action to drive software creation and technology development.
5. Co-evolution: co-evolve the methodology and the technology with all participants. Also, co-evolve and reshape work-technology symbiotic relationship.
6. Hospitality: Technology “guest” invited into the work context, otherwise not deployed.
7. Expressiveness: Semantics emphasise expressiveness of the machine model rather than processing efficiency and technical capability (which come later).
8. Reverse Engineer and Extend Semantic Model: Constantly reverse engineer from data and metadata resources and standards as a way of building the knowledge model, extending the model and integrating the resources semantically.