The Ethics of STEM Education
in the Age of AI

Jedediyah Williams, PhD
Nantucket High School

April, 2023


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

  • Math-based technologies are ubiquitous, often do not work, and are capable of broad and arbitrary harm.
  • To what extent should ethics be incorporated into secondary mathematics education?
"Yes, train these young people to get these skills, but integrate into that not only the technical capacity but the critical capacity to question what they're doing and what's happening. To me, it is not true empowerment unless people can have the power to question how these skills are going to be used."

Isn't math objective and neutral?

Let's ignore philosophy, paradoxes, incompleteness, decidability, the unfinished state of mathematics; there is still complexity.

"However, two major discoveries of the twentieth century showed that Laplace's dream of complete prediction is not possibe, 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)

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

Data modeling applications

  • Search engine
  • Recommendation systems
  • Ranking systems
  • Application / resume filtering
  • Computer vision
  • Chat bots
  • Policing
  • Sentencing and parole
  • "Self-driving" vehicles
  • ...
"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

What are some consequences of data technologies?

Some of the more well known harms

Anatomy of an AI system, Crawford and Joler
Adversarial attack
Algorithms are brittle - Melanie Mitchell
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
You are being surveilled.
You are being experimented on.

Big Picture

When handing over the tools of mathematics,
we are responsible as educators
for teaching their responsible use.

It is a sin of omission when we fail to acknowledge the consequences of the content we teach; Consequences which include ethical and technical pitfalls.

Subtle picture

  • There is no simple solution. There is no checklist that if you've done these things then you won't cause harm.
  • Many ethical concerns are technical concerns.
  • Predicting, detecting, and mitigating harm and discrimination in data technologies are complex and active areas of research.

1. Get the data
2. Clean up the data
3. Explore the data
4. Model it
5. Share the results

1. Get the data
2. Clean up the data
3. Explore the data
4. Model it
5. Share the results

Data Modeling Process


Data Modeling Process



A framework for critical analysis

• Harmful data collection, lack of consent, insecure / lack of privacy, historical, representational, or measurement bias, ...

• Labor exploitation, labeling by non-experts, incorrect labeling, trauma experienced by labelers, ...

• Feature selection bias, bias in interpretation of data visualization, data manipulation, feature hacking, ...

• Bias in model choice, model-amplified bias, environmental impact, learning bias, evaluation bias, peripheral modeling, ...

• Biased model interpretation, ignoring variance, rejecting model, deploying harmful products, deployment bias, ...

• "Pernicious feedback loops", runaway homogeneity, susceptability to adversarial attack, lack of oversight or auditing, ...

"A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle", Harini Suresh and John V. Guttag
Critical Questions:
  • 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?

How high does a bouncy ball bounce?






How high does a bouncy ball bounce?

• Data problem: What will be the bounce height \(h_{bounce}\) of my bouncy ball when dropped from rest from a given drop height \(h_{drop}\)?
• Record several slow-motion videos.

• Randomly choose a subset of videos as the training set.
• Parse the training set videos into a table.

• Create a scatter plot of \(h_{bounce}(h_{drop})\)
• Look for features! Notice and wonder. Consider models.

• Find a best-fit model on the training data.
• Validate the model on the testing data.

• Reflect on the process.
• Share out.

Bounce Prediction Error


Bounce Prediction Error


Bounce Prediction Error


Bounce Prediction Error


Bounce Prediction Error


Bounce Prediction Error


Bounce Prediction Error


Training Data Testing Data
Break models
"How high does a bouncy ball bounce?"
"How high does a bouncy ball bounce?"


"How much can we minimize the error of a linear model when predicting how high this particular bouncy ball will bounce in this room on this surface at this temperature and humidity when dropped from rest at a height of no more than two meters?"


  • Mathematics leads to important ethical considerations.
  • We should help students avoid becoming evil.