Hi, I'm Jen.
And today I'm going to talk to you about our paper tracing and visualizing
human ML/AI collaborative processes through artifacts of data work
I'm presenting on behalf of my coauthor, Anamaria Crisan,
from Tableau Research and Salesforce on our work, stemming from collaboration
with a team at Salesforce Building, an AutoML System.
AutoML is democratizing data science
by lowering the barrier to entry for data science work.
But to get started. What is AutoML now?
AutoML stands for Automated Machine Learning,
and the term is used to describe tools or systems that automate parts
of the data science pipeline, such as this one we use as a reference
By automating parts of the data science pipeline,
we can make it more accessible to people with domain or business knowledge,
but lacking the technical knowledge to implement such a pipeline themselves.
For example, Chef Charles here
to best explain how AutoML works, we have a short example.
Charles wants to be able to predict whether.
A customer is likely to cancel their reservation.
Answering questions like this requires considerable technical skills,
making it inaccessible for Charles to do on his own.
This AutoML Pipeline automates aspects of the process,
but also provides recommendations for Charles.
So take or not
Charles upload some customer data from his restaurant.
The AutoML Tool recommends some data cleaning steps, and Charles
follows some of them, but ignores others.
Charles explores
his data and the influencing factors that contribute to a given prediction.
Let me find some unexpected things in this, such as age
being ranked the top factor for the prediction.
Charles isn't happy with the performance of the deploy model on a larger dataset,
so it goes back and adjusts parameters in a previous phase
and the pipeline.
Charles wants to understand why his model performed poorly, and we do too.
And we'll return to this in a little bit,
though AutoML could help Charles answer some interesting questions with his data.
And despite the rapid advancement in AutoML Technologies
and Systems, AutoML has its challenges.
For one, there is yet to be a tool or system that fully automates
the pipeline in its entirety, so it still requires humans in the loop.
And in this regard, we can view this process as a human ML collaboration
and information transfer between human and machine is hard,
especially with the higher levels of automation,
and especially if something goes wrong
in the process.
In addition, human male AI collaboration adds uncertainty into the capabilities
and outputs of these systems, making it hard to scrutinize.
For this project, we collaborated with a team at Salesforce designing
and developing an AutoML tool for customers.
We wanted to build a visual tool to trace Human and AutoML processes across a data
workflow to facilitate understanding of this pipeline
and how it changes
not just for the users of the AutoML System.
They need to understand how things change and what is affected by that change,
but also to provide a structure
and scaffolding for collaboration, specifically with the team building
this AutoML tool of Salesforce, which had a diverse set of expertise.
This included machine learning experts, software engineers, user researchers
and user experience designers.
But before we could build a tool,
we had some important questions that we needed to figure out.
For one, how do we make this process traceable and what are we even tracing
research into?
Human human collaboration and knowledge sharing
has highlighted the importance of capturing artifacts for tracing complex
collaborative processes, artifacts or captured
aspects of a process that can look vastly different for different processes.
And we'll come back to this.
In our work, we consider an artifact to be traceable if
there's a clear definition of what it is, how and when it was generated,
and if there exists a lineage of how it changed
these investigations and how to make this process
traceable led to one of the contributions of this work.
A definition of traceability for ML.
Any AI which encompasses provenance, transparency and context.
Provenance involves recording individual artifacts and their origins.
What generated the artifacts and other artifacts dependent upon it?
Transparency concerns the ability
to understand how the model arrived at its conclusions.
And context is where the artifact exists
within within the analysis.
So we know capturing artifacts is important
and we know we need to make the process traceable.
What exactly can we capture from these pipelines that we can use to trace?
So these questions led to the primary contribution of this work
an Ottoman artifact taxonomy that defines
a set of artifacts and their properties.
The capture artifacts are used to trace an analysis process using an AutoML tool.
The benefit of formalizing these artifacts along the pipeline
is that it creates a common language for the members of the team,
as well as make comparisons between different AutoML pipelines.
So this taxonomy describes
what generated the artifact.
Does it cross the boundaries between human
and AutoML processes?
What shape does the artifact take
and what is its intended purpose?
So there are a variety of ways to create a taxonomy,
and this graphic shows an overview of our approach.
We used a method by Nickerson to develop taxonomies for information systems.
We describe a systematic approach to taxonomy development
in an iterative process with definitive stopping criteria.
The approach
we took most often in our development was an empirical to conceptual approach,
which meant that we gathered, identified classified and grouped artifacts.
We extracted from systems to form our taxonomy.
If you want more details about this process, I encourage you
to read our paper.
So we developed our taxonomy.
We collected artifacts from our collaborators, code base APIs,
and existing parts of the system.
And with
this, we designed and developed our third contribution
an interactive sketch to operationalize our taxonomy,
which we call AutoML Trace.
So the purpose of this sketch is the surface
and visualize artifacts from an end to end automated data science
flow that spans the whole analysis pipeline.
This is presented to the team that we were working with
so they can see and understand how the artifacts are dependent on one another,
as well as the breakdown of human and machine generated artifacts
in the process.
AutoML consists of three main views that surface
process artifacts the origin view in the context of the pipeline.
What is human generated versus machine generated?
The dependency view?
What is the cascade of dependencies in the pipeline and the history of you?
What changed and how did that influence dependent artifacts?
Charles wants to understand exactly what was the influence
from something change in the pipeline and how specifically,
why did his model perform poorly?
Luckily for Charles, we created the history of you
to start to dig into what artifacts changed
and what was influence in the pipeline.
When it changes, Artifacts that are not change are represented as triangles.
Artifacts that did change are represented as circles.
So like Charles, one of our collaborators, was particularly interested
in understanding when humans took machine suggestions and applied them,
and when they ignored these suggestions.
So what are the implications of this work
in aid in the
design and evaluation of human centered autonomous systems?
It can also facilitate collaboration so this can be used to make what was done
more transparent and facilitate collaboration of members of a team
that are responsible for different parts of the process
for visual analysis tools.
Our taxonomy takes a broader view of what can be captured and surfaced
from an ML pipeline beyond the model or analysis phase.
And this can benefit the design and the scope of visual analysis systems
to expand the breadth of what is explained and what is visualized.
More than just explaining the math or explaining the model as well.
It can enable regulatory oversight so traceability supports oversight
and governance that will be increasingly required for legal purposes.
Without, I'd say, thank you for listening to our talk.
Get the full story by reading our paper and check out our taxonomy
for yourself on this tiny URL.