Visual and interactive machine learning systems (IML) are becoming ubiquitous
as they empower individuals with varied machine learning expertise to analyze
data. However, it remains complex to align interactions with visual marks to a
user’s intent for steering machine learning models.  We explore using data and
visual design probes to elicit users’ desired interactions to steer ML models
via visual encodings within IML interfaces. We conducted an elicitation study
with 20 data analysts with varying expertise in ML. We summarize our findings
as pairs of target-interaction, which we compare to prior systems to assess
the utility of the probes. We additionally surfaced insights about factors
influencing how and why participants chose to interact with visual encodings,
including refraining from interacting. Finally, we reflect on the value of
gathering such formative empirical evidence via data and visual design probes
ahead of developing IML prototypes.

# Eliciting Model Steering Interactions from Users via Data and Visual Design
Probes

Anamaria Crisan, Maddie Shang, and Eric Brochu A. Crisan is with Tableau
Research, Seattle, USA, 98103 M. Shang and E. Brochu are with Tableau,
Vancouver, CA, V6B 1A6.
E-mail: {acrisan, mshang,e<EMAIL_ADDRESS>Manuscript received April 19,
2005; revised August 26, 2015.

###### Index Terms:

Design Probes, Interactive Machine Learning, Model Steering, Semantic
Interactions

##  1 Introduction

Visual and Interactive machine learning (IML) systems are powerful interfaces
for incorporating humans into the machine learning loop [1, 2, 3, 4]. However,
designing visual encodings and interactions for steering machine learning
models is a complex problem [5, 6]. One source of this challenge is the
tension between model and data-centric priorities [7, 8, 9] that require
different interaction modalities, for example using control panels or direct
manipulation of marks [10]. Another is a broad user base with diverse
expertise in machine learning (ML) and data science (DS), but who regularly
employ these systems [11, 12]. Finally, there are challenges toward aligning
human intent with ML and data interactions [13, 6].

We explore the use of data and visual design probes as a way to gather
formative empirical evidence to align visual IML systems interactions with
users’ anticipated outcomes and intents. Probes are an important component of
pre-design formative research capable of co-creating user experiences with
impacted stakeholders and surfacing problems ahead of development effort [14].
They can be useful instruments for assessing the semantic distance [15]
between a user’s actions and the meaning behind them - for example, whether
changing the position of a mark suffices a user’s intent of modifying its
class label. While probes exist in a variety of forms and are widely used in
human computer interaction (HCI) research [14, 16], including prior IML
applications, [13, 17] they are under-explored in visualization research.
Instead, visualization research has tended to emphasize the development and
evaluation of prototypes, which can result in cementing design choices ahead
of sufficient formative input from end users [14]. Prototypes can also suffer
from the attribution problem and can lack “observed results are loosely
attributed to the system as a whole, but without accurate explanations or
insights as to what component(s) played a bigger role to achieve those
results” [5]

Here, we developed a set of data and visual design probes that we used to
conduct an elicitation study with twenty participants that routinely analyze
data but vary in their ML and DS expertise. The goals of our study are
threefold. First, we seek to elicit participants’ intents coupled with direct
manipulations of visual marks to update an ML model. We deliberately place a
lower emphasis on other features of a visual IML system and focus solely on
single visual encodings to mitigate attribution issues [5]. Second, we explore
to what extent and in what ways the interactions differ in accordance with
ML/DS expertise; while we can make some basic assumptions the goal is to
gather data in lieu of making design decisions on instinct. Finally, we
reflect on how the empirical evidence we gather can be used to prioritize the
elements of visual IML systems.

Collectively, we present three core contributions:

  * •

The design of data and visual design probes for examining direct manipulations
of visual encodings

  * •

A set of target-interaction pairs that describe both what data interactions
act upon and how

  * •

The results of an elicitation study that explores factors influencing
participants’ interactions

These contributions present opportunities for expanding the visualization
research toolbox of formative methodologies for delineating an interaction
design space that aligns with users’ needs and expectations.

##  2 Related Work

We summarize pertinent research from visualization, human computer
interaction, and machine learning research.

###  2.1 Frameworks for Human-AI/ML Interaction

Theoretical frameworks for paradigms for human-AI/ML interaction inform and
influence the development of techniques for visual and interactive machine
learning [18, 19, 20, 21, 22, 23]. Within the visualization community, these
frameworks are rooted in earlier articulations of interactions more generally.
Ceneda et al. [24] presented a framework that incorporated human guidance into
the visualization processes proposed by van Wikj’s [25] model of interactions.
Building on their processes is the work by Sperrle et al. [3], who present a
framework for a co-adaptive process for people to both learn from a model and
incrementally guide it toward a better understanding of the human’s analytic
intents over time. They describe action-reaction pairs that complement the
findings of the target-interaction pairs from the elicitation study. Sacha et
al. [2] and Collins et al. [1] proposed frameworks to address limitations of
incorporating human guidance via visual analytics processes. They delineate
various points and methods for human intervention, including semantic
interactions, across stages of model building. Beyond this work, others have
recently explored how an algebraic visualization design process (AVD) [26] can
help surface the side-effects of ML model updates on visual representations
[20, 27]. Dudely et al. [6] place visualization tools in the context of
broader user interface affordances and describe how they can benefit end-users
with varying degrees of ML experts

The development of our visual design probes is influenced by these prior
frameworks for human-AI/ML interaction. We were especially influenced by
Sacha’s [2] framework of “what you see is what you can change”, to enable
direct manipulations with data encoding marks.

###  2.2 Techniques for Interactive Model Steering

Direct manipulations for user interface elements have long been a component in
steering models of IML systems [28, 29, 6]. A common approach is to allow
users to change the model’s parameters by directly changing the values of
input widgets (i.e., change a number in an input box or slider). Although
common, this approach has limitations) [30, 10]. Input widgets are ‘model-
centric knobs’ that can be overwhelm users and be ineffective[6, 31, 32].

Semantic Interactions are an attractive alternative to input widgets. Semantic
interactions shield end-users from the complexity of the model while using a
visual metaphor that interprets interactions with visual encodings to the
underlying model parameters [33]. Moreover, these interactions are
incorporated into model learning and provide feedback to end-users through the
visual metaphor. Endert et al. demonstrated the utility of these semantic
interactions with the ForceSPIRE [33] system, with further extension by Bradel
et al. to develop StarSPIRE [34]. Semantic interaction techniques have also
been applied to standard text modeling algorithms, as evidenced by iVisCluster
[35] and Utopian [36]. While this early work focused on spatial metaphors,
others have shown their limitations [37] and have explored complementary and
alternative approaches.

Other research has explored direct manipulations with data via visual
encodings but does not explicitly refer to themselves as semantic
interactions. Saket et al. [38, 39, 40] have explored possible interactions
with different encoding types; we leverage their ideas and approach in our
design probes. Other systems explore visual encodings more coarsely than [38,
39, 40], but prioritize the development of techniques for model steering.
Brown et al. [41] demonstrates how interactions with spatialized data can
learn and refine and update a distance function. Similarly, Podium learns a
ranking function through users’ interactions [42]. More recent work on
collaborative semantic inference [43] uses MLhooks to capture interactions and
update a deep learning model. Finally, there exist techniques that more
directly scrutinize the effects of data changes, and not just parameter or
function modifications, on the model changes. Systems like iCluster [44] and
VIAL [45] demonstrate that visual clustering and labeling of data can
outperform online learning alone [46]. The Chameleon [47] prototype enables ML
practitioners to visualize the effects of their data changes on the model’s
features and performance.

Here, we list only a subset of the many techniques that exist for
incorporating human actions to steer ML models via visual and interactive
interactions. We refer the reader to more recent systematic reviews for in-
depth and comprehensive overview[30, 48, 49, 50]. To reflect on the utility of
our approach, we compare the interactions we elicit design probes to existing
and varied techniques for human-AI/ML interaction and model steering.

###  2.3 Design Probes for Interactive Machine Learning

Design probes, generative toolkits, and prototypes are all complementary
approaches for designing and evaluating human-centered technological system
[51, 14]. A framework by Sanders et al. [14] emphasizes the particular
importance of probes for conducting pre-design work that aims to find
“inspiration in users’ reactions to [the probe’s] suggestions”. They contrast
probes to prototypes that aim to test a specific idea and tend to be used in
evaluative research [52]. Prior research has demonstrated that design probes
can be especially useful for creating a human-centered experience for machine
learning [53, 54], which becomes increasingly essential in light of the
increasing diversity of those using (and impacted by) AI/ML technology. While
methods for developing and using design probes vary, a recent framework by
Subramonyam et al. [17] has synthesized these common approaches into a Model
Informed Prototyping Workflow that “combines model exploration with UI
prototyping tasks”. Recently, they also propose the use of data probes to co-
create Ml/AI interactive experiences for non-expert analysts [55]. The
mechanisms for capturing user interactions and an ML model’s response varies
by according to the approach. For example, design probes can be coupled with
Wizard of Oz approach that simulates ML model behavior [56, 57, 58].
Prototypes like Gamut [59] automatically capture interactions and display
model responses. While visualization researchers use a variety of approaches,
we note a preference for high-fidelity prototypes assessed in summative
evaluations [52]. We see opportunities to further explore approaches for co-
creating visual and interactive ML systems together with users in pre-design
research [60].

##  3 Data and Visual Design Probes

In this section, we describe the construction of a data probe and a set of
visual design probes that we use in a subsequent elicitation study.
Supplemental materials for our probes have been made available online:
https://osf.io/8wbgf.

The primary objective of our data and design probes is to elicit interactions
with a machine learning model with the intent of steering the model’s
training. We do not limit ourselves to uncovering only novel interactions, but
also to collect the broad scope of users’ desired interactions. In addition to
collecting these interactions, we wanted to also explore the factors
motivating those interactions including reasons why interaction is not
desirable. Collecting both desired interactions and users’ expressed intents
allows examine and contrast what is both desirable and feasible.

The possible design space for these data and visual probes is vast. We
prioritize understanding how users directly manipulate data-encoding marks of
visual encodings to steer an ML model. The result is that our visual design
probes emphasize different chart types, similar in spirit to the approach
taken by Saket et. al. [38], and not all aspects of the interface. Instead, we
use our probes to elicit the other aspects of the user interface (i.e.,
performance diagnostic plots, explainability features, etc.) they believe
would be useful. Finally, we aimed to minimize potential attribution issues
that impact visual IML systems [5]. We created our data and visual design
probes with these objectives in mind.

###  3.1 Data Probe

Data probes are a useful tool for encouraging divergent thinking about the
behaviors on IML systems, considering their boundaries and limitations, and
imagining (or proposing) alternative behaviors [55]. For our purposes, data
probes are useful for reflecting on the efficacy of the different visual
encoding choices and the interactions they afford. We created a design probe
for a synthetic movies dataset. We use synthetic data, over an actual dataset,
because it allows us to control the shape and distribution of the data,
including the introduction of noise and errors, to probe end-users perceptions
and their desired interactions.

Overview. Our data probe explores the output of a topic classification model.
We created a dataset of 50 movies that we compiled from IMBD and Rotten
Tomatoes, harvesting their title, a brief synopsis, and primary genre (i.e.,
its classification label). We select five movies belonging primarily to one of
ten genres: Action, Animation, Comedy, Drama, Fantasy, Holiday, Horror,
Romance, Sci-Fi, and Thriller. To construct our synthetic dataset, we first
deliberately introduce errors into the labels. For example, Dr. Strangelove is
a comedy movie, but we set its predicted genre as a romance; Die Hard is a
‘Holiday’ movie but is predicted to be ‘Action’)111The movie ‘Die Hard’ takes
place on Christmas Eve and has prompted a (sometimes facetious, sometimes
serious) debate of whether it is a holiday film or not. Among our
participants, 60% said it was solely an action movie, 35% thought it was both
an action and holiday movie, and 5% were unsure.. Next, we use a Gaussian
Mixture Model (GMM)s to simulate the classification probabilities as well as a
set of two-dimensional XY coordinates for each data point; the 2D coordinates
are a substitute for computing a dimensional reduced representation of the
dataset. We explored different parameter settings for the GMM and its
components, to modify the number spread of each cluster, their proximity and
orientation to each other, and the extent that they overlap (see supplemental
materials). We selected one set of simulated results for the final data probe.

Design Considerations. While movies can belong to multiple genres, we
deliberately imposed a single classification label per movie to observe if
participants would be motivated to modify this. If participants were to
introduce multiple labels per movie, then they would be introducing a change
to the model that would go beyond simple parameter updating. We similarly use
errors and ambiguities as prompts for discussion around the data and the
model. In both instances, we are interested in assessing if participants would
discover these prompts via the visual design probes and how they would respond
to them.

The overall size of the data probe is smaller than a typical ML workflow,
which features thousands, millions, or even billions of points depending on
the dataset. The smaller size of 50 points was pragmatically selected to make
the design and interaction of the visual design probes feasible, whilst still
having diversity and variation in the dataset. Visualization research has not
proposed a singular dataset for clustering data, nor has prior research
concerning clustering with visualization discussed the relationship between
dataset size, total marks displayed, and interactions. Lacking such guidance,
we opted for a dataset size amenable to our research objectives. However, we
argue that while datasets are massive, end-users rarely interact with all data
points at once. They may filter or aggregate information, leaving them with a
more reasonable number of points to interact with. While the design of our
data probe may not be suitable for answering all possible questions about
model steering through visual encodings, it does allow us to explore the
fundamental questions of how end-users interact with data encoding marks where
each mark represents one data point.

Generality and Transferability. We derived the probe data from two commonly
used machine learning datasets with modifications to deliberately introduce
errors and ambiguity. Moreover, the movies that we selected for our probe are
popular such that the majority, if not all, participants were familiar with
them and their premise. The choice of dataset also served to ameliorate two
potential confounding factors in this study: dataset characteristics and
participant’s dataset familiarity. Our dataset is not intended to serve as
such a benchmark, others may find our approach transferable to other datasets
and applications.

###  3.2 Visual Design Probe

We used the data probe to create a set of visual design probes. Visual and
interactive ML interfaces can be complex and multifaceted and include
modalities for the user both to interact with data, through both existing and
bespoke visual encodings [49], and get feedback on the effects of their
interactions [6]. The complexity of these interfaces can also lead to
attribution challenges [5]; it is not clear what the value of individual
interface components is. We construct visual design probes that examine the
relationship between the visual encoding design choices and participants’
interactions with individual data marks. Our visual design probes would
constitute one component of an overall IML system and we use these probes to
also elicit from users what the other interface components should be.

Overview. We create visual design probes for a simple text classification
problem. We mapped the elements of the data probe to marks and channels to a
set of four visual encodings: a table, a bar chart, a scatter chart, and a dot
chart. While the table is potentially an unusual type of visual encoding, it
remains a common way to view and report on machine learning results and serves
as an important baseline. Encoding choices are summarized in Table I and the
four visual design probes are shown in Figure 1.

TABLE I: Mapping of elements of the data probe to visual encoding Figure 1:
Visual design probes created for our elicitation study

Table: Each row displays one movie along with its labeled and prediction
genres and the classification probability.

Bar Chart: Each rectangular mark in the bar chart represents a single movie.
The marks are vertically stacked and positioned along the x-axis according to
the predicted genre. The x-axis is also sorted by the number of movies
predicted to belong to the genre. The color of the individual marks represents
the labeled genre. The model’s prediction probability is shown via the mark’s
outline; a solid outline indicates higher confidence, whereas the dashes
indicate lower confidence.

Scatter Chart: Each point in the scatter chart is a single movie. The marks
are positioned according to x and y coordinates that are simulated from the
GMM. The marks are colored according to their predicted genre and sized
according to the model’s predicted probability; smaller mark sizes indicate
lower prediction probabilities. A red stroke outline around the marks
indicates whether the predicted genre matches the labeled genre (black
outline) or not (red outline). The scatter chart is also intended to represent
lower dimensionality data representations or embeddings that are routinely
presented alongside ML models.

Dot Chart: Each point in the dot chart is a single movie. The points are
positioned along the x-axis according to their predicted genre and along the
y-axis according to the predicted probability. As with the bar chart, the
genres are sorted according to class size from largest to smallest. The colors
of the point marks encode the labeled genre.

Alternative Chart Types: We recognize that different individuals may expect
different visual encoding types, or may not find the default encoding choices
effective. In considering how we implement our probes we deliberately sought
to provide participants with the tooling to challenge our design choices and
even propose new visual encodings.

Design Considerations. We choose these three visual encodings (and table)
because, based on our experience, they are common ways to visual ML model data
and results. Others have also previously explored these visualizations in
prior work of direct manipulations of data encoding marks [38]. The goal of
consistently encoding data across probes is to reduce the potential for anyone
visualization to have an outsized effect on the interactions. Others who
leverage data and visual probes in their work may find greater variability in
the probe design desirable. However, in a pilot study (Section 4.6) we found
that too much variability was disorienting. We modified our probes ahead of
the full study (Section 5) to the versions shown in Figure 1.

We use the same visual design probes for both ML and non-ML Experts. While
these two groups have different expertise, which influences their needs, we
believe that visualizations are a common and reasonable baseline for both
groups. We rely on both the expressivity of our probe medium and our
elicitation study structure to capture additional elements participants would
need, as well as, the similarity and differences between these groups.

###  3.3 Visual Design Probe Implementation

We developed visual design probes that are situated between a paper prototype
and a web-based app. Through our prototype, an end-user can interact by
directly manipulating marks in the chart (i.e., change its position, color,
size, etc.). The responses to these interactions are driven by a human
’wizard’ (i.e., the study administrators) and assessed by the end-users.

Overview. We considered different materials for implementing our visual design
probes and found Google Slides to be a surprisingly compelling medium to meet
our goals. As a medium, Slides allowed us to implement the different visual
encodings, provided a way for participants to easily change them (i.e. change
position, color, size, shape, etc.), and introduce new encoding elements. It
also allowed us to securely share and save individual sessions with different
users. The slides environment was also familiar, reducing concerns about the
effects of systems novelty [5]. Finally, Slides was also collaborative
allowing for an administrator to create interactive responses to end-user
interactions.

Affordances of Visual Design Probes. The four visual encodings in Figure 1
were implemented in Google Slides with individual marks and channels capable
of being directly manipulated (i.e., moving a point, changing its size, or
color). We chose this medium because it provided considerable encoding and
interaction affordances. At a minimum, we were able to support the full
spectrum of interactive operations reported in Saket et al. [38] (i.e.,
modifying position, size, color, height, and width encodings) in addition to
others such as adding text annotations, new shapes, or comments. We also
created tooltips for each mark by making use of hyperlink previews.

The goal of our visual design probes was to hone in on direct manipulations
with data encoding marks, not a whole system. We believe this focused approach
can collect useful empirical data by providing a canvas for expressing desired
interactions with visual encodings. Moreover, the affordances of our probe can
provide sufficiently generative power to meaningfully extract the
relationships between data, interactions, and desired model updates.

Simulating Model Modifications. We used a Wizard-of-Oz [57] approach to
simulate model updates and system behaviors. Our Wizard made two assumptions
about participant interactions with visual design probes. First, any
interactions with data encoding marks were valid. For example, if a user
wanted to change the size, position, and/or color of a mark to express the
same intent, such as changing a class label of one or more data points, these
were all valid actions. Second, participants could introduce new data marks
and the wizard could learn from them. For example, adding an ellipse around a
group of points to denote a class. Given these interaction assumptions, the
possible responses by the Wizard were broad, but allowed us the flexibility to
collect a variety of participant perspectives as part of a pre-design
empirical phase. Notably, we made no assumptions towards the back-end ML model
for the Wizard’s responses. We assumed that some of the participants’
interactions could conform to existing models, including the GMM we used to
generate the data, but we also wanted to capture possible instances were they
did not.

A study administrator would craft a model’s possible responses in response to
participant interactions and the visual encoding choice. For example, if an
end-user changed the position of the mark, a study administrator could respond
by changing its color or moving additional points that were proximal to the
one that moved. The administrator prompted participants for their expected
rate of update, for example, with each interaction or following a sequence of
interactions. Lastly, before the model was updated participants were asked to
indicate their expected response. The wizard used these utterances to craft a
response by changing marks in the visual encoding. We did not have a
prescribed procedure for how the Wizard should interpret the utterances; it
was left to them to interpret based on session and the context. Participants
were not informed that their utterances were used to carry out the response.
After the update was carried out, participants were prompted again to see if
the changes aligned with their expectation. Given the medium, participants
were aware that a human was driving the updates, but they did not know how the
updates were being made. This approach allowed for co-creation processes of
ideating around different individual expectations of model responses.

##  4 Elicitation Study

We conducted an elicitation study using the visual design probes. Elicitation
interviews are a qualitative technique that uses an iterative approach to
reveal increasingly granular details of a participant’s experience within a
situated scenario, in this case steering a classification model [61]. Our goal
of the elicitation study is to explore “approaches, processes, and subjective
experiences” [61] of interacting with ML models. We argue that this approach
allows us to explore the semantic distance [15] between a variety of encoding
and interaction affordances and the user’s intended model modifications. To
that end, we seek to not only elicit novel interactions, but to examine how
interactions are linked to possible modifications of ML models and what the
intended effects of these interactions are.

###  4.1 Research Questions

We are motivated by the challenges that domain experts experience when needed
to update machine learning models, often without the use of code [20, 62, 11].
We also wanted to understand if and how the needs of these domain experts
differ from individuals that have formal ML training. Our study sought to
examine the following research questions :

  * •

RQ1 : How do participants choose to interact with marks of visual encodings to
steer a model?

  * •

RQ2 : What are participants’ perspectives or concerns towards these
interactions?

  * •

RQ3 : What differences exist (if any) between ML and non-ML experts?

We use a the same visual design probes to assess these research questions with
both ML and non-ML experts. While the needs of both groups are different at
face value, we were interested in understanding to what extent and in what
ways expectations of these two groups overlap or differ relative to a common
baseline. We posit that using a common baseline as part of pre-design
empirical work [60, 14] helps to calibrate and evaluate IML interfaces.

###  4.2 Participant Recruitment and Background

ML and non-ML expert definition. We use the definition of a non-ML expert
articulated by Qian et. al. [11], which is that they are “people who are not
formally trained in ML and are actively building ML solutions to serve their
needs in the real world”. This definition excludes lay people that, in
addition to lacking forward knowledge, do not actively build or develop ML
workflows even though they may encounter ML models in their daily work [12].
We define an ML expert to be someone with formal training (i.e., undergraduate
or graduate-level education) in ML or DS.

Recruitment. We recruited an even number of participants with and without ML
expertise to participate in our study. We posted calls to participate on
Twitter and various machine learning and data science Slack Channels, in
addition to using snowball sampling [63] to recruit and screen eligible
participants and determine if they were classified as ML or non-ML experts. We
also sought to have some gender balance in our recruiting, specifically
reaching out to individuals that identified as female to participate. We
recruited a total of 20 participants;  half of the participants identified as
female or non-binary. The initial four recruits were used in a pilot study
(Section 4.6) that served to both refine the data and design probes and also
to establish an appropriate sample size. Following the initial full study data
analysis, we reflected on the findings and established that we had reached
analytic saturation. Based on this analysis we determined another round of
recruitment was not required.

Background. A total of 8 of 20 (40%) identified as data scientists, 6 (30%) as
data or machine learning engineers, 6 (30%) as software engineers, 3 analysts
(15%), and finally 3 (15%) as visualization researchers. A total of 6% of
individuals did not identify with any of these roles. Note that participants
could select more than one category to report their role. Aligning with our
definition of ML and non-ML experts, all participants had regular exposure to
ML models but reported a varied focus on model work in their daily activities.
In total 8 (40%) reported that training machine learning models were a daily
component of their job; note that building an ML solution does not require
training a model, as it is possible to use pre-trained models. An additional
10 (50%) report primarily interpreting machine learning results in their daily
work.

###  4.3 Session Procedures

On boarding. All sessions took place over a video conferencing platform. We
participants were provided with an overview of the study after which we
obtained consent to participate to record audio and video of the session.
Participants were then provided with a unique study link to access a set of
training materials and the study probes. For the training materials,
participants were given an overview of the data probe (Section 3.1). They were
also provided with a simple bar chart encoding and asked to complete a set of
basic interaction tasks (i.e., move a point, change its color or position,
hover to reveal a tooltip) to familiarize themselves with the affordances of
Google Slides. As most participants had experience using Slides, or some other
presentation tool, all found this task to be straightforward.

Interacting with Visual Design Probes. Participants were then presented with
the design probes(Figure 1) in one of four possible orders (Section 4.6). For
each visual encoding, participants were given a brief overview of the data
that is encoded and were provided with a set of possible interactions (i.e.,
move a point, change its color). Importantly, participants were informed that
it was valid to indicate that they did wish to make any modifications.
Participants were prompted to speak out loud to describe what they were doing
and why. In response to both participant interactions and responses, the study
administrator would simulate a response (see Section 3.3). These were
sometimes simple responses (filtering, sorting/re-ordering, or highlighting)
or were model responses (updating the positions of other points, reclassifying
data according to new labels, updating classification probabilities). Lastly,
participants were encouraged to consider what additional feedback would help
them understand and contextualize their actions; if they chose not to
interact, they were asked to discuss why.

Facilitating Model Updates. Model updates proceeded at a cadence in accordance
with participants’ stated preferences (see Section 3.3); in practice, nearly
all participants wanted updates after completing a sequence of interactions,
not with each individual interaction. For the update step, the wizard would
manipulate individual data encoding marks, or groups of marks, depending upon
the participants’ interactions and their expressed model steering intents.
Given the size of the data probe and the simplicity of the visual design
probes the ‘model update’ could be carried out in under a minute in most
instances. Participants could see individual marks being moved in real-time.
At the end of the update, indicated by a prolonged pause, participants were
asked to comment on the ‘responses’ and whether the cadence of change and
magnitude of change aligned with their expectations. Once again, the specific
responses were tailored to each individual and the particulars of that
session. The process of model refinement would continue until participants
indicated they wished to conclude.

Wrapping Up. Participants were asked to reflect on their experience overall
and provide any final thoughts.

###  4.4 Data Collection and Analysis

We collected and analyzed approximately 20 hours of recordings. We retained
modifications participants made to the design probes and took notes throughout
the sessions. Video recordings captured participants’ interactions with the
design probes and audio was transcribed for analysis. We manually annotated
the video recordings for participants’ direct manipulation of visual encoding
marks. We record single instances of an interaction per visualization, not per
mark; for example, if a participant changes the color of 10 different point
marks in the scatter chart, this is tallied as just one instance of semantic
interaction and not 10. We use descriptive statistics to summarize the types
and frequencies of interactions across sessions. We use participant utterances
to add context to both the purpose of the interactions, and any hesitations.

###  4.5 Session Overview

Sessions lasted on average 50 minutes; the shortest session was 40 minutes and
the longest was 1h and 7 minutes. Participants spent on average 5 minutes and
32 seconds with each encoding; administrators checked in with participants
either at the five-minute mark or when they observed a prolong paused (one
minute or greater). In total participants spent 22 minutes and 40 seconds
(roughly 50%) of the overall session time across all four encodings. The rest
of the session duration consisted of consent, overview, practice, and wrapping
up and addressing technical issues (video, connectivity, screen sharing) if
any arose. On average participants spent 5m33s with the table, 5m31s with the
bar chart, 5m56s with the scatter chart, and 5m17s with the dot chart. The
interactions with the scatter chart were slightly slower because it took more
time for participants to identify a mark they were interested in with the
hover action. The longest a participant spent with any one encoding was
approximately 9 minutes. We did not precisely time the responses of the
Wizard, but did not note any response that took especially long to craft for
any encoding. This is largely because the Wizard was reasonably familiar with
correspondence between marks and certain movies and given the dataset size was
able to quickly respond.

Participants did not continuously interact with an encoding during a session,
instead they would perform as a set of interactions after which they would be
prompted by the study administrators to explain the meaning behind them.
Participants varied in how much or little they chose to discuss their
interactions; again while we lack precise timing, anecdotally some
participants were more verbose than others, which may have contributed to
longer time spent with an encoding.

Figure 2: Frequency of Interactions (x-axis) and their Data Targets (facets)
elicited from a study using the visual design probes

###  4.6 Pilot Study

We conducted a preliminary pilot study with four participants to refine the
visual design probes (i.e., spelling errors, complex flow, and design
inconsistencies). The pilot study also revealed the ordering effect of our
visual probes, which introduced an anchoring effect [64]. To mitigate this
effect, we grouped the table (T), bar chart (B), scatter chart (S), and dot
chart (D) into two sets and varied the order in which they would be presented
to the participants as follows: [(TS)(DB)], [(TD)(BS)], [(ST)(DB)],
[(DB)(ST)]. Given these presentation orders, our study would need to recruit
at least eight participants (4 ML and 4 non-ML). We wanted at least each order
to be viewed twice within each group, which resulted in a minimum study size
of 16 participants.

##  5 Elicitation Study Findings

We summarize the results of our elicitation study according to our research
questions (Section 4.1).

Figure 3: A subset of common target-interaction pairs elicited from
participants in our elicitation study. The difference between the initial
($V_{0}$) and resultant visualization ($V_{1}$) can be used to refine an ML
model without directly exposing the person to its parameters. Figure 4:
Examples of larger changes participants made, which include changing the
visual encoding, adding new elements, and adding new components to the visual
interface like comments

###  5.1 RQ1: A Spectrum of Interaction

We summarize participants’ interactions with visual encoding marks as target-
interaction pairs. Targets are the data attributes that participants sought to
modify and interactions are the mechanism they used to do so. Participants
demonstrated a variety of strategies and consistency when manipulating marks
of our visual design probes. The full set is enumerated in Figure 2 and a
subset of the most common target and interaction pairs are shown in Figure 3.
Across all of our study sessions, we recorded a total of 114 examples of
interactions that we summarize as a set of 43 unique target-interaction pairs.
These interactions were not gathered uniformly across participants; two (one
ML and one non-ML) provided no interactions at all (per the session procedures
(Section 4.3) it was valid to indicate no desire to interact). Conversely, one
participant demonstrated 16 (of 43) unique interactions; the median was 8.
Participants targeted their interactions both at individual data encoding
marks, and sets of marks. Critically, we found the interactions were
deliberate and that participants could articulate their interaction intents
and expectations.

####  5.1.1 Data Targets

We identified a total of 9 data targets; this number exceeds the total number
of raw attributes in the dataset because participants also introduced new
attributes through the encodings. As an example, one participant added a
column to the table probe to introduce a weight to each movie in the dataset.
The participant wanted the weight column to inform the model of data points to
prioritize in retraining: “I want to tell the model to worry about these
points and don’t worry as much about getting these [others ones] right […]
which would allow me to say, I really care about these and I don’t so much
care about those” [ML:07]. One participant indicated wanted to augment the
data through the interactions, a reflection of the expressivity of our design
probes: “when I think about what I am feeding the machine learning model
there, it’s essentially creating another dimension for it to run through[..] I
am saying ‘Here’s a user dimension that I want you to consider [..] with the
other dimensions that you have’[ML:04]. Lastly, nearly all participants also
introduced multiclass labels for each movie, which for analysis we treated as
separate from the original single class label (the primary genre).

Considering both initial datasets and additional participant-derived
attributes collectively, we found that the most common targets of interactions
are primary and predicted labels (genres) and model probabilities (Figure 2).
However, we also observed instances when participants also wanted to indicate
when they agreed with the predicted label instead of the initial label: “I
also have this urge to say that I like how this is being classified [over the
primary]” [non-ML:06]. These findings show that participants are not just
seeking to update the ML model parameters. They are operating on a meta-
modeling level by saying that the way the data is modeled might not be true to
the meaning of the data itself; they are challenging the “ground truth” of the
given data. Moreover, although our data probe is constructed for a simple
classification model, our findings show that the scope of modifications
participants wish to make would push the boundaries of such a model by
challenging the data assumptions it was trained on.

####  5.1.2 Mechanisms of Interaction

Participants demonstrated a variety of strategies for interacting with data
targets (Figure 2). The most common types of interactions were changing the
position, size (or height), and color of the mark. These types of interactions
were common to the bar, scatter, and dot plots. With the table data,
participants would frequently overwrite existing text, for example, to modify
a movie’s genre or its prediction probability. Interestingly, participants
also introduced graphical elements to the table. The most common was coloring
the rows to reflect the classification probability. Some participants added
marks to new columns (see Figure 4) that they wanted to use to modify the data
weight of the row.

Participants explored encoding designs beyond these defaults, a set of
examples from four participants is shown in Figure 4. One participant [non-
ML:03] used the design prob to prototype a “accordion view” with the bar
chart; surprisingly, and independently, another participant [non-ML:05] had a
similar request but described it instead of implementing it. Participant [non-
ML:03] proposed that clicking on a column within the stacked bar chart would
trigger the individual marks to reposition themselves next to each other. The
participant demonstrated how they would then inspect the individual marks and
modify their heights (prediction probability) and even introduce new genres by
recoloring segments of the mark. This was the most complex strategy we
observed and indicated to us that the visual design probes provided
affordances for participants to creatively express themselves. Such
expressivity is useful for challenging existing visual design ideas, as was
the case for our probes, and iterating on encoding and interaction design.
Other new marks participants proposed included adding links between marks
(ML-04) to indicate similarity, and adding columns to the tables to add more
data, such as data weights (see Section 5.1.1). Participants introduced new
marks by repeating an existing one. For example, when wanting to express that
a movie belongs to multiple classes, several participants choose the strategy
of copying a mark and pasting it into multiple genres. Lastly, participants
also explored introducing new, or alternative, channel encodings such as color
gradients and transparency to encode multiple classes and prediction
probability, respectively. Overall, both ML and non-ML experts used similar
types of interactions and targeted similar data attributes for model steering.

Reasons not to Interact. Only two (of 20) participants declined to interact
with any of the visual design probes. One of these participants was a non-ML
expert and expressed concerns about injecting bias (other participants also
expressed these concerns and we elaborate further in Section 5.2.1). The other
participant was an ML expert and felt that visualizations were primarily for
communication and not for model steering - they preferred to code a solution,
but this is not an option for many non-ML experts. Other ML expert
participants saw the value of interacting through visualizations to explore
model steering for certain tasks. These ML participants also indicated
instances when they did not want to refine the model but completely retrain it
or select a new model. Three participants indicated that they would want to
retrain the model on only the misclassified results. Two said that they would
use the visualizations to decide whether to choose a different model. In these
instances, ML experts felt it was better to drop into code instead of
interacting through an interface.

Additional Properties of Interactions. We observed that individual
interactions could also be both compounded and hierarchical. Compounded
actions affect multiple data targets simultaneously, for example, moving a
mark from one position to another, with the intent to change its class, should
be accompanied by an automatic change to its probability (higher for the new
class, lower for the new class), and vice versa. Actions were hierarchical
when the participants wanted to make changes that should cascade down to
individual points. Two participants described hierarchy actions via anchor
points; these were points participants chose to represent a cluster and any
modifications to those anchor points should be applied to others near it.

####  5.1.3 Feasibility and Utility of Interactions

We identified two factors impacting the feasibility of implementing the
interactions participants demonstrated via our probes: whether it was
reasonable to implement the interaction and whether it was possible to use the
data generated by the interaction to update an ML model. First, some
interactions, although possible to implement, might not be reasonable to
scale. An example is using gradients to indicate multiple classes (i.e, a
movie belongs to two categories). The design space we collect offers multiple
alternative strategies for encoding multiple classes, elicited by
participants, that may be more favorable according to multiple participants
and reasonable to implement, such as repeating a mark (see Figure 2). Another
factor of feasibility is whether the data generated by the interaction could
actually be used in an ML training regimen. For example, participants
expressed introducing new class labels, having multi-class assignments, as
well as introducing hierarchy to data points. Not all classification models
would be able to accommodate such interactions and prototype systems that
failed to anticipate them would not be able to support them at all. In this
case, the probes can suggest further directions for expanding ML models to
incorporate user feedback. Importantly, it may also be used to help refine an
ML model backed in a visual IML system. For example, if participants
consistently introduce multiple classes or data hierarchy, as participants did
in our study, then we could choose a model that supports the incorporation of
such data. It may be possible, but challenging, to revise a more complex
prototype to align with user expectations.

We can also consider the utility of interactions surfaced. We observed that
the interactions surfaced by our visual design probes have also been
identified in prior studies, which lends validity to the types of interactions
our probes are able to elicit. For example, others have reported [44], we also
observed behaviors of grouping points and annotating clusters, in one instance
to create completely new clusters with labels that did not exist in the
initial (training) dataset. When we compare our results to the comprehensive
summary of cluster interactions in Bae et al. [50], we also find overlapping
examples of participants adding or merging, modifications to the cluster’s
structure (i.e., moving marks in and out of clusters), adding constraints by
anchoring marks to clusters, as well as correcting and validating perceived
errors (i.e., Dr. Strangelove is not a Romance film, but a comedy). Actions to
overwrite text in tables are similar to the behaviors of systems like
StarSPIRE [34]. Moreover, some interactions that users undertook evoked model
steering mechanisms that have also been proposed by prior works. Participants’
desire to re-weight points is an updating approach that appears to be similar
to iteratively refining a weak learner via boosting [65], but with manual
intervention from a human. Re-weighting data in accordance with user
interactions has also been used in other IML systems [33, 66].

While there are limitations on the full spectrum of interactions our visual
design probes could capture, overall, the alignment between our findings and
prior work is an encouraging demonstration of the probes’ capacity for
capturing useful interactions. Moreover, we can identify novel interactions
with respect to the combination of targets and actions that prior work has not
identified. For example, prior work does not identify probability as an
interaction target for clustering and classification, yet the use of visual
design probes surfaced it and identified several consistent interaction
strategies (Figure 2 and Figure 3).

###  5.2 RQ2: Participants Perceptions and Concerns

In addition to capturing participants’ interactions, we also dive more deeply
into the factors that influence their interaction choices, including their
hesitations. What is noteworthy about participants’ responses is the
sensitivity of design choices on participants’ willingness to interact with a
system. For example, building a system with diagnostic plots to contextualize
the effects of interactions would have been not useful to others, especially
non-ML experts. Prior work has also shown that the magnitude of model changes
is not always easy to capture [67]. Overall, these findings show the value of
design probes to anticipate challenges proactively and influence the final
system design.

####  5.2.1 Factors Impacting Interaction

Effects of Encoding Design on Interactions The choice of visual encoding
impacted how ‘inviting’ it was to engage in an interaction. For one
participant, the table was more inviting because when they see visualizations
they are “not sure how moving things around [in the scatter plot] change
anything [and I] see a table as a natural place to provide row-level
feedback.”[non-ML:06]. For other chart types, certain interactions were less
desirable. For example, some participants would repeat a mark to indicate a
movie belonged to two or more genres. Most participants performed this mark
repetition interaction with the dot and scatter chart encodings but did not do
so with the bar chart even though it was possible. When prompted, one
participant offered that “it just feels wrong” (ML:04). There was also a
general consensus among both ML and non-ML experts that the scatter chart was
the most difficult to reason with. For many participants, the axes of scatter
charts are tied to single concrete attributes (i.e., age, height, eye color,
etc.) and it was difficult to reason about axes that represented embeddings or
dimensionally reduced data. Participant non-ML:05 described the dissonance as
‘L1-interference’, not to be confused with L1-regularization, which occurs
when concepts are translated from one’s native language (L1) to a new language
(L2). Their rationale was that using visualizations to steering models was
like learning a new language that was similar, but different, from one that
they were accustomed to.

Anticipating the Effects of Interactions. The choice of visual encoding was
also tied to participants’ ability to understand and contextualize the effects
of their interactions. Participants expressed preferring simpler visual
encodings because it was easier to anticipate the effects of their actions:“ I
feel like engaging with the other charts I feel more confident [..]
because[..] when I change position [on the scatter chart] I will change the
distance to all the other points.” [ML:06]. Visual encodings also evoked
‘degrees of freedom’ that a participant could use to interact, with too many
being overwhelming: “I think I prefer this vis [bar chart]. There’s just too
much freedom in the scatter plot”[ML:05].

One way to ameliorate this challenge is to include additional visuals for
contextual information. Our visual design probes were intended to give
participants the opportunity to raise these contextual needs - and they did
so. Model explanations were commonly requested, especially amongst the non-ML
experts. Participants with an ML background also wanted to view additional
diagnostic plots to assess model performance, which they felt would help them
better understand and contextualize the effects of their actions: “I would
love to be able to do this [action] and have the model spit back out, what’s
the AUC looks like” [ML:04]. Non-ML experts did not express interest in seeing
diagnostic plots. Interestingly, there was disagreement amongst participants
towards the value of model diagnostic and performance metrics, with one
participant summarizing this tension succinctly:  “I am little confused if
what we’re trying to do is see how good the model trains vs how well the model
represents reality […] you can have an accurate model or a representative
model”[ML:06]. Prior work in interactive ML systems also emphasizes the
importance of building representative, over strictly accurate, models [68].

Concerns about Injecting Bias. Three participants (two non-ML experts and one
ML expert) articulated strong concerns about injecting bias into the ML model
as a primary reason they would hesitate to engage with a visualization to
refine an ML model. For one non-ML expert, it was a reason not to engage at
all – even if they expressed disagreement with the model results, they would
still refrain from interacting due to concerns of bias: “maybe I really
disagreed with it[ the classification ] but it is my own view imposed on
something cultural [a movie]”[non-ML:08]. One non-ML expert also doubts their
expertise to introduce model refining changes but would feel more confident if
they knew safeguards existed: “I really think that human safeguard with the
expertise is a necessary step”[non-ML:03]. Participants also indicated that
the model refinements should be considered experimental and were not to be
applied to production settings. Overall, these findings show that participants
are aware of their potential to introduce bias and this impacts their
willingness to steer the model. While additional components to the IML systems
(i.e., model explanations) could help address these concerns, participants’
comments also point to the importance of socio-technical structures that
include peer safeguards as being important to them.

####  5.2.2 Learning from interactions

As participants interacted with visual encodings, the study administrators
initiated responses to their actions. We asked participants what their
expectations were toward what the model was learning, how often it should
update, and how they interpreted the responses.

Frequency of Model Updates. Participants wanted model feedback in real-time,
but they wanted to control the frequency of updates and to only ‘commit’ to
certain changes only once they were satisfied. This form of visual speculative
execution [69] aligns with the refine-forecast model update modality described
by Strobelt et al. [70].

Learn from Minor Examples, not Major Refinements. Participants expected the ML
model to learn from a small number of interactions. Participants consistently
interacted with the marks of movies they were more familiar with than others;
they did not express the desire to make changes to those they were unfamiliar
with. Additionally, all participants articulated prioritizing their
interactions and placing different emphasis on different types of errors: 1)
instances where the model was outright wrong (i.e., predicted that Dr.
Strangelove is a romance); 2) instances where the model was not completely
wrong but should be nudged in a different direction (i.e., whether Die Hard is
an action or holiday movie); 3) instances where the participant disagreed with
both the predicted and labeled genre and wanted to provide their own. Personal
notions of how incorrect the model was, and the ‘extent of error’, also
dictated how much weight the model should give these modifications, with error
correction given a higher weight than nudges; we discuss this observation in
the next subsection (Section 5.2.2).

It is unlikely that even using their own dataset a participant is intimately
familiar with each data point. We argue that participants will always have
some prioritization strategy based on familiarity with some subset of the data
over others. It is also important to consider that datasets are frequently
reused both in research and in practice [71]. It may be both interesting and
relevant to capture which subsets are explored and by whom. Interestingly, two
participants astutely observed that many other people view and interact with
these visualizations, and they wanted a way to see the changes others had
made.

Incorporating Uncertainty to Interaction Inputs. Many participant concerns and
hesitations toward interacting with data encoding marks of visualizations can
be summarized by their desire to add uncertainty to their interactions with
the model. Nearly all participants articulated that different types of
interactions allowed them to convey a different amount of uncertainty. For
example, changing the size of the mark, its color gradient, or transparency
were seen as imprecise interactions meant to convey a larger sense of
uncertainty. Conversely, overwriting a value or changing a mark’s position was
meant to convey less uncertainty. Participants had different preferences for
conveying uncertainty. Some prefer less precision: “feel that the confidence
is more easily visualized here […] a circle is much easier to say ‘Oh I want
to make it bigger or smaller compared to a number where I have to guess some
random number [to change it to]” [non-ML:06]. While others wanted more
precision and control: “I would want to move the points individually, there
could be more precision like that […] because then I can have more control
over how confident I am reclassifying something.’’[non-ML:04]. The choice of
visual encoding did impact a participant’s ability and willingness to convey a
notion of uncertainty. Data targets that participants acted on in other visual
encodings were not targeted in others:

> Admin: In the previous vis you changed the confidence via the [mark] size,
> do you want to do that in the table?
>
> non-ML:07: No (emphatic) and I don’t know why. I mean I guess because it’s
> numbers, don’t ever change the numbers.

Prior work on semantic interactions had considered human inputs to be a kind
of ‘soft data’ [33] that the model must calibrate against what is learned from
the training data. However, we find that participants also want to convey
uncertainty more explicitly through their interactions than the notion of
‘soft data’ accommodates. Kim [72] introduced the concept of an interacted
latent variable that more closely approximates participants’ expectations.
Interacted latent variables explicitly include the input uncertainty from the
human to calibrate human inputs against what is learned from the data. In
applications of a Bayesian Case Model, Kim [72] asks participants to
explicitly provide their level of certainty via an input widget, but future
work could explore inferring uncertainty directly from interactions.

###  5.3 RQ3: Impacts of ML Expertise

Finally, we consider how ML and non-ML experts differed in their interactions
and perceptions. Individual differences aside, non-ML experts, in particular,
saw the benefit of using a visual and interactive ML system to help them
collaborate with their ML expert peers. This perspective from non-ML experts
impacted what they wanted out of an IML system.

ML Experts and not Substitutes for non-ML Expertise. The preceding results
have already alluded to some of the commonalities and differences between ML
and non-ML experts. Here we summarize the key points. If we consider only the
set of target-interaction pairs (Figure 2) we see an overlap between the
interactions ML and non-ML participants would undertake. We also see that both
groups would interact with similar data targets (predicted genre, and
probability). While participants from both groups would also introduce new
data targets (i.e., Multiclass Genres), in our study, ML experts introduced
more (data weights, similarity) compared to non-ML experts. The most evident
difference was toward additional information participants expressed they
needed to reduce cognitive load. Non-ML experts preferred explanations of the
model’s behavior, whereas ML experts wanted more performance metrics (Section
5.2.1). Both groups felt this additional information would inform their
interactions and participants assess their effects. While both ML and non-ML
experts at times declined to engage with the visual design probes, their
reasoning was different (Section 5.1.2). However, personal preferences were
more indicative of whether, and how, participants interacted with the visual
encodings. In summary, our findings support prior research [11]. that, despite
similarities, ML experts are not a sufficient proxy for non-ML experts when
developing IML systems.

Working Collaboratively with Others Several non-ML experts also expressed the
desire to comment on specific changes, either as a reference for themselves or
with ML experts they collaborated with. One participant indicated they would
use interactive model refinement to provide feedback to the ML experts on
their team: “I would love to prototype models because that would let me refine
my own notes and recommendations to the person that is potentially designing
the model”[non-ML:03]. Prior research [73, 74, 20] has shown that such
collaborative modes of interaction were desired but largely absent from
existing tools. Moreover, recent studies show both the necessity and value of
challenging the so-called ground-truth of ML data [75] by enabling wider
collaboration between data workers [76, 77]; our study suggests that
interactions could facilitate this collaboration.

###  5.4 Summary of Findings

Our study elicited 43 unique interactions that define a set of data target and
interaction pairs (Figure 2). Through these interactions, participants not
only modified existing data, but felt that they could introduce new data
(class genres, similarity, and data weights) to steer a classification model.
We were also able to capture a myriad of considerations that guided
participants’ interaction - including hesitating or refining from interacting.
We contextualize our findings between ML and non-ML experts, showcasing
similarities and differences between these groups. Overall, our findings both
align with and extend interactions discovered in prior work (i.e., [38, 50,
44]), which demonstrates the utility and value of data and design probes for
developing visual and interactive ML systems.

##  6 Discussion

Data and design probes provide a canvas for participants and researchers to
co-create elements of an interactive system [14]. Gathering empirical data
prior to the development and implementation of IML systems, including those
used for visual analysis, is critical to aligning the design of these systems
with the needs and expectations of end-users.  Our research and its findings
have explored the use of data and visual design probes for conducting pre-
design empirical research toward the creation of visual and interactive
machine learning systems. Collectively, our probes and elicitation study
revealed that users consistently target specific types of data with a varied,
yet consistent, set of actions (Figure 2). We demonstrate that probes are
capable of producing tangible artifacts, from participant utterances to their
design choices, and we suggest that these can be used to stimulate a more
informed discussion around the design and development of visualizations for
IML systems. Furthermore, as ML and AI systems grow in complexity and
implicate a wider group of stakeholders, it becomes even more important to
align these systems with human needs and expectations. Our findings also show
that probes are useful tools for teasing apart both differences and
commonalities across the continuum of ML/DS expertise. We now reflect on the
implications of our findings for developing visual and interactive machine
learning systems.

###  6.1 Designing Visual & Interactive ML Systems

Our initial goal was to use data and design probes as a precursor to
developing an IML system. However, the complexity of the interaction design
space and context for these interactions gave us pause. Our findings suggested
challenges and limitations of ML models and the incorporation of human
refinement that impacts IML system design. While data and design probes do not
resolve these issues, they appear to surface them and provide an empirical
basis for discussing trade-offs of different approaches without prematurely
committing to design choices

Human Goals and IML Brittleness. What surprised us about our findings was the
tensions they surfaced between what people wanted to do and what present ML
models are capable of. As an example, participants wanted to exercise control
over both the class label and classification probability; Die Hard should be
both a holiday and action movie but with a higher prediction probability on
the action genre. Accommodating this goal is not straightforward. Should an ML
model be refined to reflect an end-users expressed uncertainty toward specific
data points and classes? Should the initial classification problem be flipped
to be treated like a regression problem? Participants also added bespoke
classes of movies, which introduce out-of-distribution issues should they wish
to use their interactively refined model for further prediction. As one
participant indicated (see Section 5.2.2), the desired features for a system
tend to default to a model-centric perspective, focusing on accuracy
performance, and do not accommodate human-centric desires like the
representativeness of data.

Learning from Human Refinement. Refining machine learning models through human
feedback via visual encodings have been explored using both reinforcement [66]
and active learning [45]. However, these studies present users with a fixed
set of visual encodings and do not ask the critical question: do users want to
interact? The answer from our study appears to be that it depends. Our
findings suggest limitations in the amount of feedback participants wish to
provide (Section 5.2.2 and Section 5.1.1), are impacted by the choice of
visual encoding and their ability to understand them (Section 5.2.1), their
comfort initiating such interactions (Section 5.2.1), and finally, their
preferences to refine ML models via code not by interacting with visual
encodings (Section 5.1.2.) While reinforcement learning from human feedback
(RLHF) is showing promise for large language models [78], the efficacy of such
an approach for other types of data is under-explored as is the medium of
interaction (e.g., text prompts vs direct manipulation).

Our study may also have unintentionally captured refinement by voice because
study administrators prompted participants to discuss the rationale, which the
Wizard could also use to craft a response. Our study focused participants’
attention on direct manipulations of visual encodings, thus the effect of
voice was not directly under study and likely incidental. However, exploring
such multi-modal interactions could be an interesting trajectory of future
work.

Providing Feedback to Humans. Finally, an often overlooked dimension is
communicating to people what the effects of their actions were. We left this
consideration entirely open to participants to dictate, both with respect to
the frequency (Section 5.2.2) and type (Section 5.3) of feedback. Here we
found that expertise dictated what participants expected to see, with those
receiving more formal training expecting to see typical performance metrics
while non-experts preferred explanations. However, what was more surprising
was that the people also wanted feedback from other humans as part of the
refinement process. Our results suggest two modes for an IML system. An
individual mode that allows analysts of varying expertise to prototype and
refine ML models and a complementary feedback mode that allows participants to
collaborate with others in the refinement process. Per our results, the
feedback mode may extend further to also show how different individuals
interact with or annotate data encoding marks.

###  6.2 Limitations

The design choices and implementation of our probes as well as our study
population and size are limitations of our research. While we made these
deliberate choices with the intent to minimize the number of assumptions our
approach made toward user needs and expectations, we also acknowledge the
effects these choices have on our findings. We chose to develop a minimal set
of probes, that made the least assumptions about the components of an IML
interface, instead eliciting these aspects from participants and the ML model.
For purposes of feasibility, we also limited the number of data points in our
study to fifty. While we provide a rationale for these and other choices,
there are aspects of the interactive experience that our probes maybe not be
able to capture. Scaling design probes, without compromising the value of free
expression and creativity that these probes are intended to provide, is a
fruitful topic for future work. These design choices may limit some of the
generative power of the probes to elicit novel encodings and interactions. The
probes are best suited to identify the alignment between targets and actions;
the brittleness around this combination may stimulate discussion of the need
for novel encodings and interactions, but are not guaranteed to derive them.
To evaluate the trade-off between scalability and free expression of our
research approach, we would need to implement a system. However, for the
reasons described in Section 6.1, this too would have limitations for a proper
assessment. We speculate that it may require several different systems that
experiment with approaches to human refinement to fully evaluate these trade-
offs. Lastly, the study size of 20 participants, while commensurate with
visualization research on interactive machine learning and elicitation studies
[59, 38, 44, 42, 66, 61], may not to capture the total spectrum of non-ML and
ML expert perspectives. While these limitations are unlikely to hamper the
value and utility of data and design probes for visualization research, they
do provide an outline for the reasonable scope of our approach.

###  6.3 Future Work

There are several fruitful avenues for future work that expand on our research
methodology and its findings. An immediate next step is to use the findings of
our elicitation study and implement a visual and interactive machine learning
system. However, our probes as point to many interesting challenges that
cannot be solved by the implementation of a single system (Section 6.1) and
that remain open research problems for future work. One interesting area of
future work is to expand the definition of so-called “soft data” [33], by
allowing people to add their own perceptions of uncertainty into model
feedback. Prior work by Kim [72] provides an interesting avenue via interacted
latent variables, which could be explored concomitantly with visualization
research in Bayesian modeling [79, 80] and uncertainty [81, 82]. Another
interesting area of future work is to leverage semantic interactions to
investigate how people construct and label datasets. For non-ML experts, or
perhaps even the lay public more generally, interacting through visualizations
may be a more accessible way to communicate their own personal notion of a
“ground truth” [75] and even elicit richer contextual information than current
approaches afford [45, 83, 84, 76, 77]. Finally, we see promise in further
exploring the ways that semantic interactions can facilitate collaboration
amongst teams of data workers and especially between ML and non-ML experts.
This team collaboration appears to add safeguards to the processes of updating
ML models as a greater number of non-ML experts begin to undertake this task.
Of interest would be to examine whether semantic interactions could enable an
asynchronous and multi-user collaboration around model refinement.

##  7 Conclusions

Visual and interactive machine learning systems can provide avenues for data
analysts with varied ML expertise to steer ML models with human guidance.
However, designing such systems is uniquely hard and may make many assumptions
about the needs and expectations of end-users. We examined the use of data and
design probes to elicit how participants would interact with visual encodings,
via direct manipulation, to update an ML model. Our findings show that the use
of probes can not only capture a design space of interactions but also
participants’ perceptions and hesitations toward interacting with an ML model.
Overall, our study demonstrates the value of expanding the visualization
research toolbox to include data and design probes as a way of gathering
formative empirical evidence for developing IML systems.

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Anamaria Crisan is a Lead Research Scientist at Tableau. She holds a PhD in
Computer Science from the University of British Columbia. Her research
explores the intersection of data science and data visualization; her prior
work has emphasized biomedical applications.
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Maddie Shang Maddie Shang is a Sr. Research Engineer and Team Lead of
OpenMined RecSys. She has degree in Math from UWaterloo. Her open-source work
in Federated Learning and Privacy was funded by PyTorch. Her interests include
HCI via NLP and visual encodings, AI Ethics and Reinforcement Learning. She
was named as one of 100 Brilliant Women in AI Ethics in 2022.
---
Eric Brochu is a Software Engineering Architect at Tableau. He completed his
PhD at the University of British Columbia in 2010, working on human-in-the-
loop Bayesian optimization. He currently leads Tableau’s Machine Learning
Engineering team.
---