Sensitive Dependence on
Initial Conditions

How AI Predictably
Fails Unpredictably

Jedediyah Williams
AAPT Summer Conference 2024
AI in K-12 Classrooms

There is too much to talk about!

  • Power
  • Surveillance
  • Privacy
  • Security
  • Consent
  • Access
  • Fairness
  • Education
  • Energy
  • Military
  • Misuse
  • Adversarial Attacks
  • Disinformation
  • Liberty
  • Discrimination
  • Labor
  • Environment
  • Exploitation
  • Law and Oversight
  • Accountability
  • Justice

  • Data Ethics
  • AI Ethics
  • Fair ML
  • Fair AI
  • Algorithmic Bias
  • Data Bias

Motivations for this talk

  1. The largely uncritical view of AI being presented to educators.

  2. A push to educate, not critical thinkers, but fateful consumers, i.e., "AI literacy" as a consumer skillset instead of a technical skillset
cheese not sticking to pizza
... add about 1/8 cup of glue

how many rocks should I eat
... at least one small rock per day

A common misconception is that

data + compute → solutions

If the problem isn't solved yet, it's just because you haven't added enough technology yet!

"However, two major discoveries of the twentieth century showed that Laplace's dream of complete prediction is not possible, even in principle...

It was the understanding of chaos that eventually laid to rest the hope of perfect prediction of all complex systems, quantum or otherwise." (Mitchell, 2019, p. 20)
"But even if it were the case that the natural laws had no longer any secret for us, we could still only know the initial situation approximately.
it may happen that small differences in the initial conditions produce very great ones in the final phenomenon.
Prediction becomes impossible."
(Poincaré, 1908, as cited in Mitchell, 2019, p. 21)

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

"The lack of humility before nature that's being displayed here staggers me." - Malcolm, Jurassic Park

A lot of recent hype stems from
anthropomorphizing text generation machines
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

"LLM chatbots have been designed in a way, known by psychologists and ethicists, to trick humans into believing they are intelligent."

"The hype is a lie"

"Not only do many of the hiring tools not work, they are based on troubling pseudoscience and can discriminate"
Hilke Schellmann tried the myInterview tool to check her "hiring score":
  • Honest interview in English: 83%
  • Reading a random wikipedia page in German: 73%
  • Getting a robot voice to read her English: 79%

"Our success, happiness, and wellbeing are never fully of our own making. Others' decisions can profoundly affect the course of our lives...

Arbitrary, inconsistent, or faulty decision-making thus raises serious concerns..."

- Fairness and Machine Learning, Barocas, Hardt, and Narayanan

Educational technologies have a long history of failures.

Ed Tech companies promoting new tech:

"This time, things will be different"

"We can be impressed by their performance on some inputs,
but there are infinitely many inputs where they must fail."
- Iris van Rooij (26:02)
Algorithms are brittle - Melanie Mitchell
Humble Requests
Be scientists (curious and reasonably skeptical)
  • Ask: What does the tech claim to do? Is that useful?
  • Ask: Does this tech actually do what it claims to do?
  • Recognize that advertisements are not peer reviewed research

Be ethical educators
  • Do not give sensitive student data to tech companies
  • Do not volunteer your students as experimental subjects
  • Ask: What are the consequences *when* a technology breaks?
AI technologies are contributing to an environment where those "in the know" about how to game these broken systems have advantages.

Let's play a game!

It's called phisiognomy!
Given a face, you try to guess are they good or bad.

"He is a person of large vital force and chest capacity; great intellectual power and command of language...

Physically considered, he is a splendid animal"
Bad :(

"Here is a nature that will want to receive money without having to work hard for it...

judging from this picture she has a free and easy style of conduct and not very conscientious as to right and wrong"
Bad :(

"Here is a mouth that looks beastly and the expression of the eyes is
anything but pure...

There is little good to be seen in this face;
it is indicative of a low,
coarse and gross type of character."

"What a noble countenance, and what a magnificent head in the top part where the moral faculties are located!

... The expression of the eyes is pure, wise and honest."

"The perceptive faculties are very largely developed in this gentleman. Observe the immense development directly over the nose and eyes, which imparts an observing, knowing, matter-of-fact and practical cast of mind."

"These small, black eyes are insinuating, artful, suggestive and wicked.

The face, though pretty, is mere animal beauty; nothing spiritual about it."

"This is an artful, evasive, deceitful, lying, immodest and immoral eye; its very expression is suggestive of insincerity and wickedness...

The mouth also has a common and fast look."
Highway robber!

"An unprincipled looking face; the eyes have a sneaky appearance...

The upper part of the forehead in connection with the hair seems to say, I prefer to make my living by my wits..."
Galton coined the term "eugenics" in 1883.
Pearson was a student of Galton's and they worked with Fisher.
The three were pioneers of Statistics, which developed with their attempts to support bigotry on a scientific foundation.
"Wu and Zhang’s sample ‘criminal’ images (top) and ‘non-criminal’ images (bottom)." 2016
To what extent are we training the next generation of pseudoscientists?
To what extent are we training the next generation of pseudoscientists?
Many ethical pitfalls are technical pitfalls.
"Simplistic stereotypes is really not a basis for developing AI, and if your AI is based on this then basically what you're doing is enshrining stereotypes in code." (11:42)
You may wonder:

Are there any consequences of developing and deploying broken technologies into critical decision making scenarios?

Some of the more well known harms

Standford Med blames algorithm

Anatomy of an AI system, Crawford and Joler
Adversarial attack
Lack of oversight or auditing
The act, by those in power, of making decisions for us is a display of the imbalance of power.
- Sun-ha Hong, Prediction as Extraction of Discretion
We are being surveilled.

Critical Questions about Data Projects:
  • What are the motivations for the project?
  • What is the intended use?
  • What is the unintended use or misuse?
  • Where does the data come from?
  • Who collects the data?
  • Who owns the data?
  • How is the data collected?
  • How is the data stored?
  • How old is the data?
  • When will the data expire?
  • How will the data be secured?
  • What happens with the data when the company is sold?
  • Who does the labeling?
  • What labels will they decide to use?
  • Are the labelers experts?
  • Are the labels accurate?
  • What biases are represented in the data?
  • How is data included or excluded?
  • How are outliers addressed?
  • What subpopulations are represented?
  • What subpopulations are over- or underrepresented?
  • What portions of the data are inspected?
  • What features are selected for modeling?
  • What model is chosen?
  • What features do we think are being modeled?
  • What latent features are actually being modeled?
  • What is the domain of the model?
  • What are the consequences of error?
  • What decisions will be made with the model?
  • What biases are perpetuated?
  • Where will the model be deployed?
  • What could go wrong?
  • Who is responsible when things go wrong?
  • How can issues be reported?
  • Will new data be fed back in to update the model?

"AI Literacy" should include:

  • Understanding that advertisements from tech companies are advertisements. They are trying to make money by selling you things.
  • AI systems frequently do not work, and deploying AI haphazardly causes harm.
  • Being a good scientist includes being reasonably critical and aware of biases