Last week I said that ‘If open data, and its access by citizens, is as important as governments seem to think, then the deep accessibility of that data is just as important.’ Now, we’ve seen the specific case in relation to Big Open Data; but what do I mean by deep accessibility in more general terms…?
Most of us know what accessibility is – or at least we think we do. We normally think that good accessibility goes had in hand with following standards, and the provision of ‘additional’ information for use by assistive technologies. This additional information is usually aimed at turning the implicit to the explicit (for example,
alt attributes turn images which are implicitly visually described, to be explicitly described for people without vision).
But I think we need to go beyond this conception of accessibility, this more shallow view of accessibility, and start to add aspects to the computational model of accessibility which is not just about mark-up to make the implicit, explicit.
The most important part of deep accessibility is that knowledge needs to be created to fill in gaps left in the data and/or code of the original computational artefact. It not just enough to rely on attributes being present, but for deep accessibility we may need to create information for accessibility to effectively occur. For example in large data sets the raw information may very well be accessible but without suitable tools to summarise and enunciate it – the data is not accessible. In this example, these tools need to be able to create summaries, gists, and glances, all forms of description extrapolated from the raw data. This extrapolation can only occur with some degree of understanding, and for this we meaning.
It seems to me that deep accessibility can be thought of as seamless analysis of the inaccessible/or impenetrable raw computational artefact to afford the user direct access to its meaning.