This document discusses the challenges of machine learning systems for human-computer interaction. It argues that while classic theories of interaction were based on symbolic models, modern machine learning systems are based on statistical models trained on large datasets. This raises new questions for how to design systems that are humane and give users a sense of agency, control, and self-determination over systems whose behaviors emerge from complex statistical inferences rather than explicit symbolic rules. It also questions how to ensure proper attribution of content and address issues like bias, privacy, and accountability in these inferred statistical worlds.

1. Interacting with an Inferred
World: The Challenge of
Machine Learning for
Humane Computer
Interaction
+ Aarhus 2015
- Alan F. Blackwell
/김민준
x 2016 Fall

2. Alan Blackwell
• Visual Representation
• End-User Development
• Interdisciplinary Design
• Tangible, Augmented and Embodied Interaction
• Psychology of Programming
• Computer Music
• Critical Theory
1975-1985-1995-2005 — the decennial Aarhus
conferences have traditionally been instrumental
for setting new agendas for critically engaged
thinking about information technology. The
conference series is fundamentally
interdisciplinary and emphasizes thinking that is
firmly anchored in action, intervention, and
scholarly critical practice.
Aarhus Conference

3. Summary
4
4
1. Classic theories of user interaction have been framed in relation to symbolic models of planning
and problem solving.
But…
2. Modern machine-learning systems is determined by statistical models of the world rather than
explicit symbolic descriptions.
Therefore…
3. We must explore the ways in which this new generation of technology raises fresh challenges
for the critical evaluation of interactive systems. — Humane Interaction

4. Presentation Contents
5
Background
The New Critical Landscape
Case Study to Critical Questions
Towards Humane Interaction
1
2
3
4
5 Conclusion
6

5. Background
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6
“Good Old-Fashioned AI” and Human Computer Interaction
“GOFAI has long had a problematic relationship with HCI
— as a kind of quarrelsome sibling”
• Both fields brought together knowledge from Psychology and Computer Science
• In the early days of HCI, it was difficult to distinguish HCI from AI or Cognitive Science

6. Background
7
7
Expert Systems Boom of the 1980s and Critical Reactions
The possibility of a Strong AI
vs.
Symbolic problem-solving algorithms neglect
issues central in HCI
• Social context
• Physical embodiment
• Action in the world
argued by Winograd, Flores, Gill, Suchman
Situated Cognition
— The failure of formal computational models of planning and action to deal with the complexity of the real world

7. The Critical Landscape
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8
“Good Old-Fashioned AI” vs. Modern Machine Learning
GOFAI vs ML
• symbols were not grounded
• the cognition was not situated
• no interaction with social context
• operate purely on ‘grounded’ data
• ‘cognition’ is based wholly on information
collected from the real world
• ML systems interact with their social context
through data — eg. SNS data

8. 9
9
“Good Old-Fashioned AI” vs. Modern Machine Learning
GOFAI vs ML
• symbols were not grounded
• the cognition was not situated
• no interaction with social context
• operate purely on ‘grounded’ data
• ‘cognition’ is based wholly on information
collected from the real world
• ML systems interact with their social context
through data — eg. SNS data
Turing Tests
The Critical Landscape

9. GOFAI vs ML
• symbols were not grounded
• the cognition was not situated
• no interaction with social context
• operate purely on ‘grounded’ data
• ‘cognition’ is based wholly on information
collected from the real world
• ML systems interact with their social context
through data — eg. SNS data
Turing Tests
The Critical Landscape
“Good Old-Fashioned AI” vs. Modern Machine Learning
10
“What if the human and computer cannot be distinguished because
the human has become too much like a computer?”

10. Background
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11
Brieman and ‘Two Cultures’ of Statistical Modeling
1. The Traditional Practice
Predictive Accuracy > Interpretability
2. ML Techniques in which the model is
inferred directly from data
Occam’s Razor
— “The models that best emulate nature in terms of predictive
accuracy are also the most complex and inscrutable

11. Case Study: Reading the Mind
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Reconstructing visual experiences from brain activity — Jack Gallant
https://www.youtube.com/watch?v=nsjDnYxJ0bo
A blurred average of the 100 film library scenes
most closely fitting the observed EEG signal

12. Critical Questions
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Question 1: Authorship
The Behavior of ML systems is derived from data (through a statistical model)
Statistical models as an index of the content
ex) Library of Babel
A library that contains every possible book in the universe
that could be written in an alphabet of 25 characters
This is possible right now..!

13. Critical Questions
14
14
Question 1: Authorship
The Behavior of ML systems is derived from data (through a statistical model)
Statistical models as an index of the content
ex) Library of Babel
A library that contains every possible book in the universe
that could be written in an alphabet of 25 characters
Is every digital citizen an ‘author’ of
their own identity?
who makes the data?

14. Critical Questions
15
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Question 2: Attribution
Content of the original material captured in an ML model or
index should still be traced to the authors
Digital Copyright?

15. Critical Questions
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Question 2: Attribution
Counter-example: EDM Music Industry
Content of the original material captured in an ML model or
index should still be traced to the authors
Digital Copyright?
Sampled Chopped and Mashed New Song

16. Critical Questions
17
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Question 2: Attribution
Counter-example: EDM Music Industry
Content of the original material captured in an ML model or
index should still be traced to the authors
Digital Copyright?
Sampled Chopped and Mashed New Song
In symbolic systems, the user can apply a semiotic reading in which
the user interface acts as the ‘designer’s deputy’
If the system behavior is encoded in a statistical model, then this
humane foundation of the semiotic system is undermined

17. Critical Questions
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Question 3: Reward
“If you are not paying for it, you’re not the customer;
you’re the product being sold”
Ecosystem Players (Apple, Google, Facebook, Microsoft)
are attempting to establish their control through a combination of storage, behavior, and authentication services
that are starting to rely on indexed models of other people’s data
“The primary mechanism of control over users comes through
statistical index models that are not currently inspected or regulated”

18. Critical Questions
19
19
Question 4: Self-Determination
1. Sense of Agency
ML-based Systems
2. Construction of Identity
“In control of one’s own actions”
• system behavior becomes perversely
more difficult for the user to predict
• some classes of users may be excluded
from opportunities to control the system
ex) Kinect
• Submitting to a comparison between the
statistical mean
“The construction of one’s personal identity”
Narratives of Digital Media / SNS
• behavior of these systems becomes a
key component of self-determination
• users “curate their lives”
• what about moments that I don’t want?
“Regression to the Mean”

19. Critical Questions
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Question 5: Designing for Control
If a Machine Learning-based System is wrongly trained, how do we “fix” it?

20. Critical Questions
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Question 5: Designing for Control
“Re-train” by more
correct inputs
If a Machine Learning-based System is wrongly trained, how do we “fix” it?

21. Critical Questions
22
22
Question 5: Designing for Control
“Re-train” by more
correct inputs
If a Machine Learning-based System is wrongly trained, how do we “fix” it?

22. Towards Humane Interaction
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Features
Many very small features are often a reliable
basis for inferred classification models*
“How would a machine vision system might recognize a chair?”
* but, the result is that it becomes difficult to account for
decisions in a manner recognizable from human
• Judgements are made in relation to sets of features, and
• Accountability for a judgement is achieved by reference to those features
how many legs? people sit on it etc

23. Towards Humane Interaction
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24
Features
Many very small features are often a reliable
basis for inferred classification models*
“How would a machine vision system might recognize a chair?”
* but, the result is that it becomes difficult to account for
decisions in a manner recognizable from human
• Judgements are made in relation to sets of features, and
• Accountability for a judgement is achieved by reference to those features
how many legs? people sit on it etc
The semiotic structure of interaction with inferred worlds can only be
well-designed if feature encodings are integrated into the structure

24. Towards Humane Interaction
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Labeling
The inferred model, however complex, is essentially a summary of expert judgements
• ‘ground truth’ implies a degree of objectivity (may or may not be justified)
• experts may have a different approach compared to normal users
• what about “Amazon Mechanical Turk?” > cultural imperialism

25. Towards Humane Interaction
26
26
Confidence and Errors
99% Likelihood 5% Error Rate
Problems
• Many inferred judgements obscure the fact of its varying degrees of confidence
• An action based on 51% likelihood may be more beneficial to the user than 99% likelihood

26. Towards Humane Interaction
27
27
Confidence and Errors
99% Likelihood 5% Error Rate
Problems
• Many inferred judgements obscure the fact of its varying degrees of confidence
• An action based on 51% likelihood may be more beneficial to the user than 99% likelihood
Confidence should be given as a choice
User’s experience of models should be determined by the
consequence of errors, not the occasions

27. Towards Humane Interaction
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Deep Learning
Challenges
1. It is difficult for a Deep Learning algorithm to gain information about the world that is unmediated by
features of one kind or another
2. If the judgements are not made by humans, they must be obtained from an other source
Critical Questions
1. What is the ontological status of the model world in which the Deep Learning system acquires its
competence?
2. What are the technical channels by which data is obtained?
3. What ways do each of these differ from the social and embodied perceptions of human observers?

28. Conclusion
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1. Classic theories of user interaction have been framed in relation to symbolic models of planning
and problem solving.
But…
2. Modern machine-learning systems is determined by statistical models of the world rather than
explicit symbolic descriptions.
Therefore…
3. We must explore the ways in which this new generation of technology raises fresh challenges
for the critical evaluation of interactive systems. — Humane Interaction by…
1. Features
2. Labeling
3. Confidence
4. Errors
5. Deep Learning (Machine-based judgement)