We ask how users interact with ’knowledge’ in the
context of artificial intelligence systems. Four examples of visual
interfaces demonstrate the need for such systems to allow room
for negotiation between domain experts, automated statistical
models, and the people who are involved in collecting and
providing data.Africa’s Voices Foundation
Boeing
BT
EPSRC
The Health Foundatio

Blackwell, A

Church, L

Mahmoudi, M

Marasoiu, M

English

Crossref

Visual Knowledge Negotiation

Apollo (Cambridge)

Visual Knowledge NegotiationAlan BlackwellComputer LaboratoryUniversity of CambridgeCambridge, UKAlan.Blackwell@cl.cam.ac.ukLuke ChurchComputer LaboratoryUniversity of CambridgeCambridge, UKluke@church.nameMatthew MahmoudiDepartment of SociologyUniversity of CambridgeCambridge, UKmm2134@cam.ac.ukMariana Ma˘ra˘s, oiuComputer LaboratoryUniversity of CambridgeCambridge, UKmariana.marasoiu@cl.cam.ac.ukAbstract—We ask how users interact with ’knowledge’ in thecontext of artificial intelligence systems. Four examples of visualinterfaces demonstrate the need for such systems to allow roomfor negotiation between domain experts, automated statisticalmodels, and the people who are involved in collecting andproviding data.Index Terms—intelligent interfaces, visualisation, knowledgenegotiationI. WHY WE NEED KNOWLEDGE NEGOTIATIONPhilip Agre’s classic critique of Artificial Intelligence re-search articulates a key problem in the mechanisation ofknowledge, which he formulates as the “discursive practice”of AI research [1]. This is best summarised in his own words:AI is a discursive practice. A word such as planning,having been made into a technical term of art, has twovery different faces. When a running computer programis described as planning to go shopping, for example, thepractitioner’s sense of technical accomplishment dependsin part upon the vernacular meaning of the word [...] Onthe other hand, it is only possible to describe a program as”planning” when “planning” is given a formal definition interms of mathematical entities or computational structuresand processes. [...] This dual character of AI terminology— the vernacular and formal faces that each technicalterm presents — has enormous consequences for theborderlands between AI and its application domains.Recent critique of machine learning methods, in Cheney-Lippold’s “We Are Data” [2], identifies a related issue inmachine learning. He proposes that the named categories andlabels fundamental to supervised machine learning shouldalways be placed in quotation marks, in order to avoid theimplication that these names correspond to the “vernacularface” (in Agre’s terms) of concept names outside of thestatistical model. For example, Cheney-Lippold notes that hisown Google profile identifies him as being “female” (throughstatistical analysis of his online behaviour) when this is nottrue. Nevertheless, the statistical observations of Cheney-Lippold as a “female” customer within Google’s models maybe useful data for their advertisers, and may be a goodprediction of Cheney-Lippold’s future purchases. But whenCase studies funded by Africa’s Voices Foundation, Boeing, BT, EPSRCand the Health Foundationmaking use of this fact it is important to remember that thismodel-label, although potentially useful, is not true.Building intelligent systems to be useful in some applica-tion domain requires constant attention to the necessary dualcharacter of the “knowledge” encoded in the system, andthe vernacular language of the user. Where statistical modelsresult in interactive visual languages, we have a critical designproblem. Should the visual language correspond to one typeof knowledge (which?), or to both?We claim that visual interaction with intelligent algorithmsmust be designed in order to allow negotiation between theuser and the inferred statistical “knowledge”. In summary,visual languages support negotiation of knowledge, becausethey are not linguistically over-determined.II. VISUAL DESIGN FOR KNOWLEDGE NEGOTIATIONWe illustrate this theoretical concern with four practical casestudies, supported by visual interfaces as seen in Figures 1, 2,3 and 4. (Longer descriptions of these case studies are beingpresented at a satellite workshop of this conference, on De-signing Technologies to Support Human Problem Solving [3]).Each of these four systems is designed for use by a specificclass of domain expert — police analysts (Fig 1), businessanalysts (Fig 2), research translators (Fig 3) and hospitalFig. 1. In ForensicMesh, computer vision algorithms locate video from a bodyworn camera in a city location, but emphasising the subjective viewpoint ofthe person wearing it by rendering that person’s body in the foreground, sothat the user can interpret this “objective” digital evidence within a subjectcontext.Fig. 2. In SelfRaisingData, a statistical model of unseen data is synthesisedby a business analyst as a way of formulating research questions from a userperspective.Fig. 3. In Coda, Somali translators classify SMS messages relating to publichealth, with semi-automated labels negotiated through varying shades of thecategory colours. 1clinicians (Fig 4). In each case, a model has been constructedon the basis of data originally acquired from human sources.A statistical model, more or less complete and more or lessaccurate, has been created on the basis of that data. And ineach case, the domain expert who interacts with the system hasa richer, more sophisticated and more complete understandingof the context than has been embedded in the model.That expert understanding extends beyond critical evalua-tion of the predictive power of the statistical models — italso extends to critical understanding of the data from whichthe model has been created, and of the human agency throughwhich the data was captured. We therefore try to avoid systemdesigns in which models are trained with a pre-defined set oflabels that might be liable to simple acceptance as the fulland complete truth — so in Coda (Fig 3), the set of labels canalways be expanded, redefined, or replaced with other sets.We also try to highlight the human origins of apparentlymechanical data acquisition, for example in ForensicMesh(Fig 1) we render a human figure into the scene, representing1Since the data Coda is used with is usually sensitive, the data in this screen-shot is a sample from the Reddit comment data available on Google BigQuery(https://bigquery.cloud.google.com/table/fh-bigquery:reddit comments)the police officer who was wearing a body-worn camera fromwhich video was collected.In the extreme case of SelfRaisingData (Fig 2), we proceedwith no data at all, giving expert analysts the opportunityto negotiate far further down the ‘supply chain’ of statisticalinference by creating a data set. This has no objective status atall, in that no data exists, but provides a basis for negotiatingthe model that might be created.ICUMAP (Fig 4) also subverts the conventional visuallanguage of statistics by creating a clustering algorithm thatis not a simple dimension reduction of a multivariate space,but modifies the t-SNE distance metric to allow the narrativeof a journey (through placing successive time-point samplesnearby), and explicitly reflecting the clinicians’ prior expec-tation (by weighting clusters to represent the most salientclinical category of surgical procedure). These allow cliniciansto reason ‘outward’ from their own knowledge to explorestatistical similarities beyond the ‘obvious’ (to clinicians) priorexpectations.To conclude, these design case studies demonstrate howvisual languages can support negotiation of knowledge, wherestatistical terminology fails to distinguish between model andapplication.Fig. 4. In ICUMAP, the outcomes of post-surgery intensive care are visualisedas trajectories toward discharge (green) or mortality (red), so that clinicianscan assess typicality or risk of new cases in relation to precedent, but withoutrelinquishing judgment.REFERENCES[1] P. E. Agre, “Toward a Critical Technical Practice: Lessons Learned inTrying to Reform AI,” in Bridging the Great Divide: Social Science,Technical Systems, and Cooperative Work, L. S. Les Gasser and G. B.Bill Turner, Eds. Erlbaum, 1997.[2] J. Cheney-Lippold, We are data: Algorithms and the making of our digitalselves. NYU Press, 2017.[3] A. Blackwell, L. Church, M. Jones, R. Jones, M. Mahmoudi, M. Mara-soiu, S. Makins, D. Nauck, K. Prince, A. Semrov, A. Simpson, M. Spott,A. Vuylsteke, and X. Wang, “Computer says ‘don’t know’ - interactingvisually with incomplete AI models,” in Designing Technologies toSupport Human Problem Solving Workshop - A Workshop in Conjunctionwith VL/HCC 2018, 2018.

Visual knowledge negotiation

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Visual knowledge negotiation

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