Using humanities knowledge to explore bias in big data approaches to knowledge creation
Author: Jennifer Edmond
Dr Jennifer Edmond, is the Director of Strategic Projects in the Faculty of Arts, Humanities and Social Sciences at Trinity College Dublin. Trained as a scholar of German literature, Jennifer is mostly engaged professionally with the investigation of knowledge exchange and collaboration in Humanities research and in particular the impact of technology on these processes.
Jörg Lehmann and Jennifer Edmond were very pleased to have been given a chance to present some learnings from the KPLEX project to an engaged audience at the DH 2019 conference on 12th July 2019. The paper was entitled “Digital Humanities, Knowledge Complexity and the Six ‘Aporias’ of Digital Research,” and explored a number of the cultural clashes we found between the perspectives in our interviews. While DH was never a planned audience for our results, the response today convinced us that there is still much to mine from our interviews and insights!
The slides from the presentation can be viewed here.
Director of the Trinity College Dublin Centre for Digital Humanities and Principal Investigator on the KPLEX Project
One of the major terminological forces driving ICT development today is that of ‘big data.’ While the phrase may sound inclusive and integrative, in fact, ‘big data’ approaches are highly selective, excluding, as they do, any input that cannot be effectively structured, represented, or, indeed, digitised. Data of this messy, dirty sort is precisely the kind that humanities and cultural researchers deal with best, however. In particular, knowledge creation and information management approaches from the humanities shed light on gaps such as: the manner in which data that are not digitised or shared become ‘hidden’ from aggregation systems; the fact that data are human created, and lack the objectivity often ascribed to the term; and the subtle ways in which data that are complex almost always become simplified before they can be aggregated. Humanities insight also exposes the problematic discursive strategies that big data research deploys, strategies that can be seen reflected not only in the research outputs of the field, but also in many of the urgent challenges our digitised society faces.
The CfP for the Trinity College Dublin Centre for Digital Humanities Software Cultures Working Group has been extended to Friday 18 May, don’t miss your chance to participate in this innovative & creative meeting. Further details available here.
The KPLEX project now features as a Net4Society Success Story in Social Science and Humanities Integration (SSH) into Information and Communication Technology (ICT) Research. The fact sheet is now available to read here
Mark Zuckerberg posted the following statement on his Facebook feed:
“Today we’re publishing research on how AI can deliver better language translations. With a new neural network, our AI research team was able to translate more accurately between languages, while also being nine times faster than current methods.
Getting better at translation is important to connect the world. We already perform over 2 billion translations in more than 45 languages on Facebook every day, but there’s still a lot more to do. You should be able to read posts or watch videos in any language, but so far the technology hasn’t been good enough.
Throughout human history, language has been a barrier to communication. It’s amazing we get to live in a time when technology can change that. Understanding someone’s language brings you closer to them, and I’m looking forward to making universal translation a reality. To help us get there faster, we’re sharing our work publicly so that all researchers can use it to build better translation tools.”
Key messages: taking time to understand people is for fools, and language is the problem.
When did language become a barrier to communication? Would we not be hard pressed to communicate much at all without it? Doesn’t machine translation have the potential to create as much distance as ‘understanding?’ Building intimacy (for this is what I take the phrase “brings you closer” to mean) is not about having a rough idea of what someone is saying, it is about understanding the nuance of every gesture, every reference and resonance. Isn’t the joy of encountering a new culture tied up in the journey of discovery we make on the road to understanding?
I salute Facebook for making their research and software open, but a bit of humility in the face of the awesome and varied systems of signs and significations we humans have built could make this so much better news.
The KPLEX project is founded upon a recognition that definitions of the word ‘data’ tend to vary according to the perspective of the person using the word. It is therefore useful, at least, to have a standard definition of ‘big’ data.
Big Data is commonly understood as meeting 3 criteria, each conveniently able to be described with a word beginning with the letter V: Volume, Velocity and Variety.
Velocity is how fast the data grows. That >250 billion images figure is estimated to be growing by a further 300-900 million per day (depending on what source you look at). Yeah.
Variety refers to the different formats, structures etc. you have in any data set.
Now, from a humanities data point of view, these vectors are interesting. Very few humanities data sets would be recognised as meeting criteria 1 or 2, though some (like the Shoah Foundation Video History Archive) come close. But the comparatively low number of high volume digital cultural datasets is related to the question of velocity: the fact that so many of these information sources have existed for hundreds of years or longer in analogue formats means that recasting them as digital is a highly expensive process, and born digital data is only just proving its value for the humanist researcher.
But Variety? Now you are talking. If nothing else, we do have huge heterogeneity in the data, even before we consider the analogue as well as the digital forms.
Cultural data makes us consider another vector as well, however: if it must start with V, I will call it “voluptuousness.” Cultural data can be steeped in meaning, referring to a massive further body of cultural information stored outside of the dataset itself. This interconnectedness means that some data can be exceptionally dense, or exceptionally rich, without being large. Think “to be or not to be;” think the Mona Lisa; think of a Bashō haiku. These are the ultimate big data, which, while tiny in terms of their footprint of 1s and 0s, sit at the centre of huge nets of referents, referents we can easily trace through the resonance of the words and images across people, cultures, and time.
Will the voluptuousness of data be the next big computational challenge?
“I end up is feeling that in the rush to new tools and ‘Big Data’ Humanist scholars are forgetting what they spent much of the second half of the twentieth century discovering – that language and art, cultural construction, human experience, and representation are hugely complex – but can be made to yield remarkable insight through close analysis. In other words, while the Humanities and ‘Big Data’ absolutely need to have a conversation; the subject of that conversation needs to change, and to encompass close reading and small data. ”
The KPLEX Project held its official kick-off meeting on 1 February 2017 in Dublin, Ireland. The project team took this opportunity for some structured discussion and knowledge sharing on our 4 key themes and set out the plans for the work programme in the months ahead:
Toward a New Conceptualisation of Data,
Hidden Data and the Historical Record
Data, Knowledge Organisation and Epistemics
Culture and Representations of System Limitations
We are particularly grateful to our EU project officer, Pierre-Paul Sondag, who joined us in Dublin for this meeting.
The Trinity Long Room Hub, Arts & Humanities Research Institute
Beyond the ‘long tail’ metaphor, the distribution of data within the scientific field has been described in terms of ‘data wealth’ and ‘data poverty’. Steve Sawyer has sketched a political economy of data in a short essay (a slightly modified version of this paper is freely accessible here). According to him, in data-poor fields data are a prized possession; access to data drives methods; and there are many theoretical camps. By contrast, data-rich fields can be identified by three characteristics: pooling and sharing of data is expected; method choices are limited since forms drive methods; and only a small number of theoretical camps have been validated. This opposition leads to an unequal distribution of grants received, since data wealth provides for legitimacy to claims of insight as well as access to additional resources.
While Sawyer describes a polarity within the scientific field with respect to funding and cyberinfrastructures, which he sees as a means to overcome obstacles in data-poor fields, the KPLEX Project will take a look into how contents and characteristics of data relate to methodologies and epistemologies, integration structures and aggregation systems.