The document discusses user experience considerations for machine learning applications, outlining key insights from a literature review conducted by the EDP UX team. It highlights user issues such as the importance of technology literacy and cognitive biases, as well as design opportunities to improve transparency and trust in ML products. Additionally, the document emphasizes the need for effective communication design to enhance user understanding and acceptance of machine learning technologies.

1. User Experience of Machine Learning
Leslie A. McFarlin
Sr. UX Architect, EDP Value Stream

2. Introduction
• The EDP UX Team conducted a literature review of Machine
Learning (ML) research.
• Literature reviews provide a history of a topic, including struggles and
opportunities for use.
• Goal: Be prepared to design and deliver an outstanding
user experience around applications incorporating machine
learning.
• Leverage existing guidelines.
• Create our own guidelines where appropriate.
• Enable CA to become leaders in crafting quality ML user experiences.

3. Introduction
• Two topics emerged from the literature review:
USER ISSUES
Watch list for users interacting with Machine Learning applications.
DESIGN ISSUES
Considerations when designing Machine Learning applications.

4. Introduction
• Three opportunities for user research emerged.
END USER AWARENESS OF MACHINE LEARNING
What users know about Machine Learning, including how it is used and what
expectations they might have for it.
APPROPRIATE LEVELS OF TRANSPARENCY
What information to surface about a Machine Learning application and when.
ASSESSING TRUST IN ML PRODUCTS
Developing or incorporating metrics to monitor trust in our Machine Learning
products.

5. Introduction to Machine Learning
Understanding What Is Machine Learning

6. What is Machine Learning?
• An algorithm enabling computers to mimic human
tasks.
• ‘Learning’ in machine learning refers to the
algorithm’s ability to improve performance in its
designated task over time.
• As algorithms improve performance, people can
step away to focus on other issues.

7. About Algorithms
• Rules describing a sequence of steps meant to resolve
a class of problems.
• Perform calculation, data processing, and automated
reasoning.
• Machine Learning algorithms fall into 1 of 3 algorithm
categories.
• Supervised learning
• Unsupervised learning
• Reinforcement learning

8. Supervised Learning

9. Supervised Learning
• Using a set of predictor variables, the algorithm
produces a prediction.
• Supervised learning algorithms receive training
until they reach an acceptable level of accuracy.
• Example: Identifying objects in pictures as human.

10. Unsupervised Learning

11. Unsupervised Learning
• Based upon an input’s characteristics, it is grouped
into a category.
• Accuracy is not evaluated as in supervised learning.
• Occasionally used in conjunction with supervised
learning algorithms.
• Example: Detecting anomalous behavior in a
system.

12. Reinforcement Learning

13. Reinforcement Learning
• Features in an environment are used to make
decisions.
• Past experience and partial feedback help the
algorithm continually improve the quality of its
decision making.
• Example: Automated control over a system
(videogame AI, for example).

14. Algorithms and User Experience
• Some algorithms execute faster than others based
upon their structure and complexity.
• Factors to consider from a UX perspective:
• Amount of input.
• Type of input, the interactions needed to provide it, and
when the input is given.
• Detail of output.

15. User Issues in Machine
Learning
Emerging Topics

16. General ML User Personas
• Consumers, users who need to interpret ML outputs.
• Susceptible to cognitive biases and peripheral processing of ML outputs.
• Makers, users who integrate ML into problem-solving strategies.
• Receive opinions from consumers and experts, and must filter what is
a genuine concern and what is not.
• Work to find new opportunities.
• ML Experts, users with expertise allowing them to innovate in
ML.
• Design choices impact which cognitive biases manifest among consumers.
Emerging Topics

17. Common User Issues
• Interacting with ML applications becomes complicated due
to an interplay of technology literacy, cognitive biases, and
openness to persuasion.
• Technology literacy refers to knowledge about a specific
technology.
• End user awareness- early user research opportunity.
• Data literacy- understanding input into the ML application, and its output.
• Cognitive biases are misperceptions related to heuristic
processing (faster, less in depth thinking).
Emerging Topics

18. Common User Issues
• Openness to persuasion relates to how willingly users
accept output from a ML application.
• Depends on how people process information.
• Potential for user research relating to ML application transparency.
• Central processing is very thorough, focused on the content of
the output.
• Peripheral processing is not thorough, and relies on things like
perceptions of who or what is delivering output.
• Perception of the ML application as expert, or infallible, can cause issues.
• May rely on heuristic processing
Emerging Topics

19. Common User Issues
• Mental model stability impacts the acceptance of ML
applications.
• Stable mental models mean better user acceptance of ML applications.
• Stability of mental models relies on a mix of factors:
• Quality of ML application transparency.
• Fit between ML application performance and user expectations.
• Technology literacy.
Emerging Topics

20. Design Issues in Machine
Learning
Emerging Topics

21. Experience Segments within ML
• Onboarding and familiarizing.
• What is the first time user experience?
• What is the risk of cold start experiences with CA products?
• Evolution.
• User transition from novice to expert.
• Changing of interactions as the ML application becomes smarter.
• Error recovery.
• O_oarding.
• Transition within the company, as when an ML application trained
by one person or group now needs to interact with new people.

22. Common Design Issues
• Design for ML applications extends beyond interaction and
visual design.
• Communication design becomes key.
• Interaction design rolls up into one overarching principle:
• Communication design focuses on explanation fidelity, a
combination of explanation accuracy and depth.
Emerging Topics
Allow the user to focus on using the system output, not training the system.

23. Interaction and Visual Design
• Help users discover unknowns.
• Show users what they have, and help them explore and understand it.
• Assist with decision-making processes.
• Allow users to reflect on the ML application output they receive.
• Clarity of language and visual depictions.
• Support users when uncertainty arises.
• Understand limitations and failures so they cease to be hindrances.
• Uncover through feedback, predict through research and internal
understanding of ML applications.

24. Communication Design
• Explanation fidelity changes on two factors.
• Soundness, faithfulness of an explanation.
• Completeness, breadth and depth of an explanation.
• Oversimplification may impact both soundness and
completeness, leading to incorrect or unstable mental model
creation.
• Overcomplication impacts the ability to process and understand
information, impeding mental model creation.
• Both lead to mistrust of the ML application.
Emerging Topics

25. End User Awareness
of Machine Learning
Research Opportunities

26. End User Awareness
• What is end user awareness?
• Knowledge on the basics of a technology, which may extend to include
knowledge of its pervasiveness and expectations for its use.
• Why does it matter?
• Contributes to user expectations.
• Impacts the quality of interactions with a system.
• How can we impact it?
• Education about the role of ML in a system.
• Explanations of ML algorithms.
Research Opportunities

27. User Education
• Helps users understands the strengths, opportunities, and
weaknesses of a technology.
• Reduces the impact of cognitive biases.
• Contributes to the creation of realistic user expectations.
• Helps users understand why they receive outputs like they
do, as well as how to train the ML application.
• Links directly to transparency.
Research Opportunities

28. Research Opportunity
• Assess familiarity with ML applications.
• Gauge how customers think ML functions.
• Determine whether customers see a fit for ML in their daily
job functions.
• Explore where customers think CA could offer ML to
improve our fit in their work ecosystem.
Research Opportunities

29. Research Outcome
• Gauge the level of user education necessary to ensure ML
applications from CA are successful within the marketplace.
• Correct misperceptions or incorrect assumptions.
• Understand how users would expect to interact with CA ML
applications, and work toward meeting and exceeding
expectations.
• Gather insight into the expected tone of interactions.
• Find new opportunities to insert ML into our products and
services, or to create new ML-based products or services.
Research Opportunities

30. Appropriate Levels of
Transparency
Research Opportunities

31. Transparency
• Provides insight into how a system functions, and why.
• Manifests in 3 ways:
• Instructional text.
• Feedback from ML application.
• Help documentation.
• Appropriate levels of transparency offer multiple benefits:
• Contribute to mental model stability.
• Build trust.
Research Opportunities

32. Research Opportunity
• Main Focus: Assessing and maintaining appropriate level of ML
application transparency.
• Transparency ties directly to user expectations for a system.
• When expectations are met, transparency improves trust in the ML
applications and improves the user’s perception of their role and confidence in
interacting with a system.
• When expectations are not met, transparency does little to change the
situation.
• Be mindful of complexity, and measure perceived complexity regularly.
• Too much complexity impedes understanding and building of trust.
• Too little (oversimplification) can also impede understanding.
Research Opportunities

33. Research Outcome
• Develop and refine a set of standards for communication
between our ML applications and users.
• Improve learnability and usability of our ML applications.
• ML is meant to take the burden off of the users, and with
appropriate levels of transparency users can focus more on
protecting data rather than finding it and verifying that the findings
are accurate.
• Open up opportunities for user collaboration.
• ML applications benefit from better collaboration between the
experts, strategists, and users involved with them.

34. Measuring Trust in Machine
Learning Applications
Research Opportunities

35. Trust
• What is trust?
• Believing an entity will act according to an established pattern,
usually with the assumption that the acts will have a beneficial
outcome.
• What role does trust play in human-computer interaction?
• Persistent product use (exclusive reliance on product for tasks).
• Advocating product use among peers.
• Building trust in human-computer interactions:
• Consistent screen interactions.
• Well-defined visual cues.
• Clear and relevant messaging.
Research Opportunities

36. Trust for ML Applications
• ML applications must build trust on two fronts:
• In its model.
• In the predictions made.
• What does trusting a ML model mean?
• Believing that a ML application will behave in a way that end users
find acceptable.
• Instilling trust in the ML model:
• Educate users on the strengths and limitations of the ML model.
• Follow user centered design principles to reduce uncertainty in behavioral
expectations.
Research Opportunities

37. Trust for ML Applications [Continued]
• What does trusting ML predictions mean?
• Users acting on predictions in a recommended way.
• Instilling trust in ML predictions:
• Transparent communications around the prediction made.
• Create opportunities for users to verify prediction accuracy, and offer
feedback when possible.
• ML models precede ML predictions, so if users do not trust the
model, trust in the predictions may suffer.
Research Opportunities

38. Research Opportunity
• Main Focus: Assess a baseline of trust, and measure
changes over time.
• Trust in ML applications builds through:
• Reliable functionality.
• Providing clear value to users.
• Enabling users to interact with the ML application around outputs.
• Trust changes as the human-machine relationship evolves.
• How much trust exists at the start of ML application use, and what is the
magnitude and direction of the change over time?
Research Opportunities

39. Research Outcomes
• Monitor changes in trust as our ML applications change.
• Understand how user behaviors and expectations change
with trust levels.
• Low trust makes users hesitant to rely on ML application output,
but high trust can encourage overreliance on the ML application.
• At what point do users start second guessing their abilities and
deferring to the ML application?
• Gain insight into how trust levels interact with other user
characteristics, such as the biases one holds.
Research Opportunities

40. Tying It All Together
Linking Emerging Issues and Research Opportunities

41. Structure of Concepts
Research Opportunities
Trust in the Machine
Learning Model
Trust in Machine
Learning Predictions
Transparency
Communication DesignInteraction DesignVisual Design
User Awareness
User Expectations
User Education

42. Establishing a Research-Based
Strategy
• Explore what users know about the technology to understand
the extent of their experiences, acceptance of it, and what they
expect from it.
• Design informative communications from this knowledge to educate users
within and outside of the product.
• Monitor communication quality via user research.
• Like other design areas, communication design is an iterative process, so
messaging should evolve based on feedback.
• Follow established user centered design principles.
• Test designs before creating a final version.
Research Opportunities

43. Establishing a Research-Based
Strategy
• Research measures of trust in human-computer interaction, and
evaluate whether using an existing measure or creating a custom
one is best.
• Monitor trust over time.
• Determine if trust can be correlated to metrics already in use by CA.
• Future research goal: Plan to explore how trust relates to user
biases.
• Do more conservative criteria for ML-based judgments lead to greater
trust levels for certain types of data?
• Are these biases a user characteristic, or an industry characteristic?
Research Opportunities

44. Thank you!
For any questions or comments related to this presentation, please
contact Leslie A. McFarlin, Senior UX Architect- EDP Value Stream
leslie.mcfarlin@ca.com