Siân Lindley* Innovations in machine learning are enabling organisational knowledge bases to be automatically generated from employees' activities, the results of which can then be presented to workers via the software applications they commonly use. The potential for these systems to shift the ways in which knowledge is produced and shared raises questions regarding what types of knowledge might be inferred from employees' practices, how these can be used to support work, and what the broader ramifications of this might be. This paper draws on findings from two studies to offer an initial exploration of these topics. The research described investigated workplace (i) collaborative actions and (ii) knowledge actions, to explore how they might (i) inform automatically generated knowledge bases, and (ii) find support through the design of intelligent systems. We draw on the literature on implicit interactions in considering next steps. CCS CONCEPTS • Human-centered computing ~ Empirical studies in HCI • Information systems ~ Enterprise information systems Additional Keywords and Phrases: machine leaning, artificial intelligence, collaboration, information, process, knowing, practice
Innovations in machine learning are enabling knowledge bases (KBs) to be automatically generated from content produced within large organisations, and then presented to workers via the software applications they commonly use. For instance, the recently released Microsoft Viva Topics [8] recognises common topics within an organisation, creates ‘topic pages’ and ‘topic cards’, and highlights these across Microsoft 365. The increasing capabilities of such systems raise questions about what can be inferred from the activities of employees, and how – in turn – the information that is mined can then be used to support working practices.
Human action is inherently bound up with knowing how to get things done in organisational work. Orlikowski
[9] emphasizes that knowing and knowledgeability are continuously developed and demonstrated through
workers’ actions, which are themselves contextual. Thus, knowing is the capacity to perform useful practices,
given the unique circumstances of the current situation. Drawing on this position, we present findings from two
studies of how actions taken as part of work could (i) inform an automatically generated KB, and (ii) find support
through an intelligent system. In Study 1, we asked participants to focus on collaboration; the aim was to identify
actions that workers take in relation to others (hereafter ‘collaborative actions’). In Study 2, we asked
participants to focus on knowledge; the aim was to identify actions taken in relation to important types of
knowledge in the workplace (hereafter ‘knowledge actions’). It is worth noting that, in Orlikowski’s view, knowing
cannot be captured; it is inseparable from its constituting practice. However, we posit that information mined
from workers’ activities could play a role in the performance of useful practices, either by supporting workers’
own recurring activities, or by helping others develop the ability to perform competent work. In this short paper,
we analyse the collaborative and knowledge actions workers report to highlight possible scenarios for design,
and draw out related challenges. We draw on the concept of implicit interactions (e.g., [7] [
Study 1 was a diary study in which participants recorded and then discussed collaborative actions performed as part of work. While the aim was to identify actions that could receive support from intelligent systems, we did not limit data collection to technology-mediated actions, as we were also interested in those accomplished without digital artefacts that may nevertheless raise implications for research or design.
19 participants, who varied in occupation (10 managers/directors; 7 employees, 2 sole traders), age and gender, completed the study. One participant withdrew after the first interview and is not included in the analysis.
The study began with a telephone interview to explain the goals and methods of the study, ascertain consent, and learn about the participant’s work. Questions included: What is a typical day like? Who do you work with? What does collaboration look like in your role? How is collaboration managed? What tools do you use as part of collaboration? Participants then completed a diary over four working days and one day off (even if nothing happened on the day off). They were asked to record a few collaborative actions per day, including those that involved no technology or that seemed brief or non-intensive. Finally, participants were interviewed for a second time, in-person, for up to 90 minutes. They talked through the actions they had recorded and, for each action, the interviewer recorded a short description on a sticky note. The sticky notes were organized into groups of similar activities by the participant after all diarized actions had been discussed. Participants were then thanked and given a gift voucher. Interviews were audio-recorded and photos of the sticky note groupings were taken.
Study 2 was a design workshop in which information workers discussed actions that automatically generated KBs should support. The aim was to understand the everyday actions on knowledge that a KB should enable, and the context in which these actions are performed.
12 participants in two workshops were first shown a video of a fictional AI system that extracted descriptive and procedural information from work and surfaced that knowledge to others to guide next steps. To prime participants to think about knowledge, they were then asked to list important types of knowledge that they use every day for work. Next, participants were asked to generate the key actions they take on knowledge as part of work that should be supported by an ML system. After this, participants were asked to make a flow diagram to describe a time when they worked collaboratively to achieve a shared objective, highlighting important actions and pieces of knowledge. Finally, in one of the workshops, participants were asked to perform an evaluation exercise, explicitly comparing how the in-context activities described during the flow diagram task fitted, or extended, the collaborative actions that were generated through Study 1. The sessions were audio-recorded, and video recordings and photographs were captured at various points throughout.
For each of the studies, activities generated and described by participants were clustered. The clusters were then organized to produce a single framework across both studies. This framework has three high-level themes: actions that focus on content, actions that focus on process, and actions that focus on a worker’s individual growth, as shown in Table 1.
In considering (i) how these actions could inform the development of machine learned KBs, and (ii) how they might receive support from intelligent systems, we developed the framework presented in Figure 1. We applied this framework to contemplate how actions relating to content or to process can result in implicit or explicit contributions to machine-learned systems. We then generated scenarios that might receive support from an intelligent system for each set of actions, and identified related considerations for design. A concise overview of these results is presented in Table 2. A reflection on how organisational KBs might play a role in supporting a worker’s individual growth is the focus of a separate paper, which is currently in preparation.
Our analysis of collaborative and knowledge actions taken as part of work highlights how employees might
make explicit and implicit contributions to organisational KBs, and conversely, how those KBs could support
employees as they work. However, it also highlights considerations for design that speak to numerous issues.
In this discussion, we draw on the literature on implicit interactions to engage with some of these. Serim &
Jacucci [
A lack of awareness over implicit input may be acceptable for systems that have minimal consequences for
users, such as the ordering of world-wide web search engine results. However, implicit interactions with
organisational KBs have the potential to have more significant effects for workers. As the functionality of such
systems begins to mature, the technology could introduce radical shifts in the ways in which knowledge is
generated, shared, and consumed within workplaces, which could in turn affect the ways in which employees
contribute to, or are seen to contribute to, their organisations. Indeed, as workers grow to understand the
potential for their work to be made available to others via organisational KBs, it seems possible that they may
begin to change their working practices accordingly. Dix [3] has observed that the same interaction can be
understood as incidental, expected, or intended, depending on the user’s awareness and understanding of the
consequences of their actions. Building on this, Serim & Jacucci note that users may avoid certain actions to
prevent what they perceive as the unwanted effects of implicit interactions, or they may reformulate their goals
in relation to these. In their review, Serim & Jacucci cite research showing how users abstain from reading email
to avoid sending read receipts [5], play music not to listen to it but to shape their social media profiles [
This type of agency is dependent on users having some understanding of how organisational KBs are produced and presented to workers. However, workplace systems that are both automatic and pervasive raise challenges in communicating to users what the consequences of their actions are. Janssen et al. [6] argue that the use of automated systems by non-professional users necessitates a deep consideration of how to foster Table2:PossiblecontributionstoaKBviacontentandprocess-relatedactions,potentialscenarios,andconsiderations. iitrsseanonodC iifttftIfrrcscscnogudeouonenoeou iililttffttrrrsvypedendedeaaaneehanem iitt,,tcxcscneoouhoanedBKwmm litrkcsnoaegdedhouhpndaaw iiiltttryccdeenedounab?am iltttttrrrrrsksscssehaehhaaehaoueW iillilttftrrrrsccconadnoenudoeeeoewmm ilil,ttfffrssscnadbaeeebubbdnaae ii’ittttrrrsksvcoppuneoooeah?apewm iiIttttrsscyebangedhennaehohauoAW iiifrr--ycyccsvanadheneauannahuwm lt-ed?deeeohopn iliiiitttttrrsksyveogahhaaeanaondnonW litrrrsssccceaeepehenpoedaandanbew ''illllitttfcysoaua?oedaohehowmW ''iifttrrrccssdeuaneoaedeop?epm iitttr-sskveanohahadoenehooawwmW irrrr-ksssvsskvoeope?eenoeoaKBAww iiittffttttryschaadeonanenenoo?hww iiIttttrsscybeangedhenanehohauoAW iiitffr-scsspooedaopngnoa?anheW lit---ooehpneededuann?hm d rk n o a w n o i g t in ta tr n
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r s e A v set of challenges for the design of automated systems. Fröhlich et al. are primarily referring to users who have chosen to use automation technologies outside of work. However, we suggest that both observations could extend to employees who are using, for instance, standard office software and cloud storage technologies, which are deployed by their organisations. Guidelines for the design of AI [1] emphasise responsibility in intelligent systems design, but they tend to do so from the standpoint of intentional use. We see a need to extend these to cover implicit interactions, such that employees using office technologies might gradually build an understanding of how the content they produce and store using workplace technologies is mined by ML systems and made visible in new ways.
In our own research, we are exploring ways of supporting employees in learning how organisational KBs might be shaped by and in turn might shape their work. In this, we take inspiration from Ju & Leifer’s [7] emphasis on the value of designing “courteous” implicit interactions. In particular, we are exploring how systems might do employees the courtesy of helping them understand how their work has contributed to organisational KBs and the practices they support, in the hope of aligning with Serim & Jacucci’s recommendation that implicit interactions should be considered appropriate in retrospect. Our aim is that, by highlighting how implicit interactions have informed organisation KBs, we will enable workers to build mental models of the system that can underpin interactions with it over time and offer opportunities for repair where needed. In doing so, we hope to design user experiences that enable agency and systems that are deserving of trust.