diff --git a/assets/css/my-remark.scss b/assets/css/my-remark.scss
index 8d7adf109945fc08c3928035c11b58237322966c..48600c2922d40d7c8cd08f12661d47a345272424 100644
--- a/assets/css/my-remark.scss
+++ b/assets/css/my-remark.scss
@@ -144,16 +144,14 @@ table tbody td {
table thead {
background: $dark;
color: $light;
- border-bottom: 4px solid $dark;
border-left: none;
border-right: none;
}
-table thead th {
+table thead tr {
font-size: 0.7em;
font-weight: bold;
color: $light;
- border-left: 2px solid $dark;
}
table thead th:first-child {
border-left: none;
diff --git a/slides/3dprinting.html b/slides/3dprinting.html
index a311439dc68895150d082e7e8b44b565b44aca11..9bbbf9af6a47cabda7e886d4614b51909cdff19f 100644
--- a/slides/3dprinting.html
+++ b/slides/3dprinting.html
@@ -23,6 +23,7 @@ class:
## This is an important reminder
## Make sure zoom is running and recording!!!
+## Make sure captioning is turned on
---
layout:false
diff --git a/slides/bias-in-machine-learning.html b/slides/bias-in-machine-learning.html
index f35051455a7dcb42ef5615d061dc6346e9bd06dd..b50f09345a770e56bf8ac705fa783e254688004d 100644
--- a/slides/bias-in-machine-learning.html
+++ b/slides/bias-in-machine-learning.html
@@ -21,6 +21,7 @@ class:
## This is an important reminder
## Make sure zoom is running and recording!!!
+## Make sure captioning is turned on
---
[//]: # (Outline Slide)
@@ -54,10 +55,94 @@ But *how*?
- How does Machine Learning solve a problem?
+
---
-# Components of Machine Learning
+# Helping Computers Learn Patterns
-- Collect data (and lots and lots of it!)
+.left-column50[
+![:fa bed, fa-7x]
+]
+.right-column50[
+## How might you recognize sleep?
+
+- Can you come up with a yes/no question or a set of categories or simple description of sleep?
+ - Sleep quality?
+ - Sleep start/end?
+- What data would you learn from?
+- How might you need to take disabilites into account?
+]
+???
+
+(sleep quality? length?...)
+
+How to interpret sensors?
+
+---
+# How do we program this?
+
+Old Approach: Create software by hand
+- Use libraries (like JQuery) and frameworks
+- Create content, do layout, code up functionality
+- Deterministic (code does what you tell it to)
+
+New Approach: Collect data and train algorithms
+- Will still do the above, but will also have some functionality based
+on ML
+- *Collect lots of examples and train a ML algorithm*
+- *Statistical way of thinking*
+
+---
+# Shift in Approaches
+
+.left-column50[
+## Old style of app design
+<div class="mermaid">
+graph TD
+
+I(Input) --Explicit Interaction--> A(Application)
+A --> Act(Action)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
+
+class U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
+
+
+--
+count: false
+.right-column50[
+## New style of app design
+<div class="mermaid">
+graph TD
+
+U(User) --Implicit Sensing--> C(Application)
+S(System) --Implicit Sensing--> C
+E(Environment) --Implicit Sensing--> C
+C --> Act2(Action)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
+
+class U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
+---
+# This is *Machine Intelligence*
+
+Often used to process sensor data
+
+Goal is to develop systems that can improve
+performance with more experience
+- Can use "example data" as "experience"
+- Uses these examples to discern patterns
+- And to make predictions
+
+Not really intelligent, just my word for Machine Learning, AI, Neural Programming, etc etc
+
+---
+# Components of Machine Intelligence
+
+- **Collect data (and lots and lots of it!)**
- Discern patterns
@@ -73,49 +158,370 @@ But *how*?
- Who do we collect data from?
---
-# "Goodness" of Data
+# Problems with Data
+- System timeouts that are trained on movement speeds of <q>typical</q> people
+- Biometrics that cannot function on a person who isn't still for long enough
+- Inferencing about people that doesn't account for height; stamina; range of motion; or AT use (e.g. wheelchairs)
+
+When groups are historically
+marginalized and underrepresented, this is
+.quote[imprinted in the data that shapes AI
+systems... Those who have borne discrimination in the past are most at risk of harm from
+biased and exclusionary AI in the present. (Whittaker, 2019)]
+
+--
+This can cascade -- e.g. measurement bias can exacerbate bias downstream. For example, facial mobility, emotion expression, and facial structure impact detection and identification of people; body motion and shape impact activity detection; etc.
+
+---
+# How might we address bias/fairness in data sets
+
+We need to know it is there (Aggregate metrics can hide performance problems in under-represented groups)
+
+We need to be careful not to eliminate, or reduce the influence, of outliers if that erases disabled people from the data because of the heterogeneity of disability data.
+
+---
+# Approaches to measuring fairness
+
+We may need to rethink <q>fairness</q> in terms of individual rather than group outcomes, and define metrics that capture a range of concerns
+- Movement speed might favor a wheelchair user
+- Exercise variety might favor people who do not have chronic illness
+- Exertion time might covers a wide variety of different types of people.
+
+Defining such unbiased metrics requires careful thought and domain knowledge, and scientific research will be essential to defining appropriate procedures for this.
+
+---
+# Small Group Discussion [Post on Ed]({{site.discussion}}2514887)
+
+Who might be excluded in the data set you found?
+
+How was fairness measured in the data set you found, if it was discussed?
+
+How would you go about testing for fairness in that data?
+
+
+---
+# Best Practices For Data Fairness
+
+How do we motivate and ethically compensate disabled people to give their data?
+
+What should we communicate at data collection time?
+
+Is the data collection infrastructure accessible? Does it protect sensitive information about participants adequately given the heterogeneous nature of disability?
+
+
+Does the meta data collected oversimplify disability? Who is labeling the data and do the have biases affecting labeling?
+ - Whittaker (2019) discusses the example of clickworkers who label people
+as disabled <q>based on a hunch</q>.
+
+---
+# Components of Machine Intelligence
+
+- Collect data (and lots and lots of it!)
+
+- **Discern patterns**
+
+- Make predictions
+
+
+---
+# Two main approaches
+
+![:fa eye] *Supervised learning* (we have lots of examples of what should be
+ predicted)
+
+![:fa eye-slash] *Unsupervised learning* (e.g. clustering into groups and inferring what
+they are about)
+
+![:fa low-vision] Can combine these (semi-supervised)
+
+![:fa history] Can learn over time or train up front
+
+---
+# Our Focus: Supervised Learning
+
+![:fa eye] *Supervised learning* (we have lots of examples of what should be
+ predicted)
+
+![:fa eye-slash] *Unsupervised learning* (e.g. clustering into groups and inferring what
+they are about)
+
+![:fa low-vision] Can combine these (semi-supervised)
+
+![:fa history] Can learn over time or train up front
+
+---
+# Supervised Learning
+
+.left-column50[
+## Training Process
+
+<div class="mermaid">
+graph TD
+
+L(Label) --> MI(Training Algorithm)
+D(Input Data) -- Extract Features--> MI
+MI --> C(Symbolic Predictor)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
+
+class D,U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
+
+
+.right-column50[
+## Extracting Features
+
+Symbolic requires feature engineering (humans deciding how to *summarize* data using features. Tends to be more *interpretable* (you can figure out why they make predictions)
+
+]
+
+---
+# Supervised Learning
+
+.left-column50[
+## Training Process
+
+<div class="mermaid">
+graph TD
+
+L(Label) --> MI(Training Algorithm)
+D(Input Data) --> MI
+MI --> C(Neural Predictor)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
+
+class D,U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
+
+
+.right-column50[
+## Designing Networks
+
+Neural approaches (e.g. ChatGPT) use massive amounts of data to train a network according to base principales. Designing the right network is critical. Cannot be sure *why* they make the predictions they do.
+]
+
+---
+# Supervised Learning
+
+.left-column50[
+## Training Process
+
+<div class="mermaid">
+graph TD
+
+L(Label) --> MI(Training Algorithm)
+D(Input Data) --> MI
+MI --> C(Symbolic/Neural Predictor)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
-Let's consider this example: **We wish to train a ML model to recognize if a given picture is that of a nurse.**
+class D,U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
+
+
+.right-column50[
+## Prediction Process
-What steps do we take?
+<div class="mermaid">
+graph TD
-Is the data "good"?
+D(Input Data) --> C(Symbolic/Neural Predictor)
+C --> P(Prediction)
+
+classDef normal fill:#e6f3ff,stroke:#333,stroke-width:2px;
+
+class P,D,U,C,A,I,S,E,Act,Act2 normal
+</div>
+]
-What other examples can we think of?
---
-# "Goodness" of Data
+# How do we Evaluate Predictors?
+Compare to Prior probabilities
+- Probability before any observations (ie just guessing)
+- Ex. ML classifier to guess if an animal is a cat or a ferret based on the ear location
+ - Assume all pointy eared fuzzy creatures are cats (some percentage will be right)
+
+Compare to simplistic algorithms
+- Ex. Classifying cats vs ferrets based on size
+- Your model needs to do better than these too
+
+Surprising how often this doesn't happen in published work/before deployment
+
+???
+We did this to study gender's impact on academic authorship; doctors reviews
+
+---
+# Adding Nuance
+.left-column50[
+## <q>Confusion Matrix</q>
+]
+.right-column50[
+Don't just measure accuracy (percent correct)
+
+Lots of other metrics based on false positives and negatives
+- Precision = TP / (TP+FP) Intuition: Of the positive items, how many right?
+- Recall = TP / (TP+FN) Intuition: Of all things that should have been positive, how many actually labeled correctly?
+- ... Many More
+]
---
-# Disability Bias in Machine Learning
+# Using Proper Methods
-Data sets used to train biometric systems are biased: they rarely if ever have a comprehensive
-representation of the range of people they might encounter. *(Mankoff, 2021)*
+.left-column50[
+**Symbolic Methods Can Easily Overfit**
-- Human bias
+When your ML model is too specific for data you have, it might not generalize well
-- Machine bias
+Best test is a data set you haven't seen before
-- Underrepresentation
+
-- Measurement Errors
+]
+
+.right-column50[
+**Neural Methods Can Have Hidden Biases**
+
+
+
+]
---
-# Minimizing Bias
+# Disability Biases to Watch Out For
-- Appropriate representation
+Norms are baked deeply into algorithms which are designed to learn about the most common cases. As human judgment is increasingly replaced by biometrics, *norms* become more strictly enforced.
+- Do outliers face higher error rates?
+- Do they disproportionately represent and misrepresent people with disability?
+- How does this impact allocation of resources?
-- Large training data
+---
+# How does norming harm people with disabilities?
-- Validating the models
+Machine intelligence already being used to track allocation of assistive technologies, from CPAP machines for people with sleep apnea (Araujo 2018) to prosthetic legs (as described by Jullian Wiese in
+Granta and uncovered in Whittaker et al 2019), deciding who is <q>compliant enough</q> to deserve them.
-- Montoring the performance of the models
+Technology may also fail to recognize that a disabled person is even present (Kane, 2020), thus <q>demarcating what it means to be a legible human and
+whose bodies, actions, and lives fall outside... [and] remapping and calcifying the boundaries
+of inclusion and marginalization</q> (Whittaker, 2019).
---
-# Discussion
+# How does norming harm people with disabilities?
-- Identify biases in existing ML-based tools/platforms
+Many biometric systems gatekeep access based on either individual identity, identity as a human, or class of human, such as <q>old enough to buy cigarettes.</q>
+Examples:
+- a participant having to falsify data because <q>some apps [don’t allow] my height/weight combo for my age.</q> (Kane (2020))
+- a person who must ask a stranger to ‘forge’ a signature at the grocery store <q>.. because I can’t reach [the tablet]</q> (Kane (2020))
+- at work, activity tracking may define <q>success</q> in terms that exclude disabled workers. (may also increase the likelihood of work-related disability, by forcing workers to work at maximal efficiency)
-- Identify ways to make ML models more inclusive in your area of expertise
+---
+# Components of Machine Intelligence
+
+- Collect data (and lots and lots of it!)
+
+- Discern patterns
+
+- **Make predictions**
+
+---
+# Concerns at Prediction Time
+
+Denial of insurance and medical care, or threaten employment (Whittaker,
+2019, p. 21).
+- HireVue, an AI based video
+interviewing company has a patent on file to detect disability (Larsen, 2018).
+- This is illegal under the ADA, which
+ - forbids asking about disability
+status in a hiring process (42 U.S.C. § 12112(a))
+ - forbids <q>using qualification
+standards, employment tests or other selection criteria that screen out or tend to screen out
+an individual with a disability</q> (42 U.S.C. § 12112(b)(6)).
+
+---
+# Concerns at Prediction Time
+
+Denial of insurance and medical care, or threaten employment
+
+Disability identification
+- Examples: detect Parkinsons from gait (Das, 2012), and mouse movement (Youngmann,
+2019); detecting autism from home videos (Leblanc, 2020).
+- What are the ethics of doing this without consent?
+- Many of these algorithms encode medical model biases
+
+Relatedly, failure to identify disability
+- Legally under the ADA, if you are treated as disabled, you are disabled. Yet biometrics cannot detect how people are treated.
+
+---
+# Concerns at Prediction Time
+
+Denial of insurance and medical care, or threaten employment
+
+Disability Identification / Failure to Identify
+
+Apps that Harm
+- Example: Training behaviors in <q>support</q> of autistic individuals without regard to debates about agency and independence of the target audience [Demo, 2017];
+- As with regular accessibility apps, AI based aps can harm, be disability dongles, etc
+- As with regular apps, AI based apps may not be accessible
+
+---
+
+# Concerns at Prediction Time
+
+
+
+---
+# Concerns at Prediction Time
+Denial of insurance and medical care, or threaten employment
+
+Disability Identification / Failure to Identify
+
+Apps that Harm
+
+AI with Baked in Biases
+- Consequences of biased data and lack of control over training results more nuanced than just accuracy (as with headlines we just read)
+- Privacy can also be a concern.
+ - For rare conditions, an algorithm may learn
+to recognize the disability, rather than the individual, reducing
+security when used for access control, allowing multiple people with
+similar impairments to access the same data.
+
+---
+# Concerns at Prediction Time
+
+Denial of insurance and medical care, or threaten employment
+
+Disability Identification / Failure to Identify
+
+Apps that Harm
+
+AI with Baked in Biases
+
+Transparency and Accountability
+- Power differences between builders and users
+- Representation of disabled people among builders
+- Algorithms that are not *interpretable* or *correctable*
+- Users of algorithms whose use them to enforce larger societal harms
+
+---
+# Small Group Discussion [Post on Ed]({{site.discussion}}2515387)
+
+Revisit the data set you chose
+
+Do you know what sort of predictions it was used for if any?
+
+What possible harms could be done with those predictions?
+
+Reminder of our list
+- Denial of insurance and medical care, or threaten employment
+- Disability Identification / Failure to Identify
+- Apps that Harm
+- AI with Baked in Biases
+- Transparency and Accountability
+
+
+---
+# End of Deck
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diff --git a/slides/proto-deck.html b/slides/proto-deck.html
index cea88919a869caa2b3571aa2bc718fe7ebf0f37b..678da34d20b4b1e8510e7b8e5f465f3eb315f415 100644
--- a/slides/proto-deck.html
+++ b/slides/proto-deck.html
@@ -23,6 +23,7 @@ class:
## This is an important reminder
## Make sure zoom is running and recording!!!
+## Make sure captioning is turned on
---
[//]: # (Outline Slide)