1. Ethics and Machine Learning
Villanova University Machine Learning Project

2. Ethics and Machine Learning
Why Ethics?
Machine learning systems can raise ethical questions.
Some of these are new, interesting issues, unique to machine learning.
More of them are reflections of ethical questions that occur in many environments and circumstances
Any decision-making system must face some of these decisions
Any system for summarizing, analyzing, or interpreting information requires some assumptions
There are not simple answers to these questions; it is worth discussing some of them
Villanova University
Machine Learning Project
Ethics and Machine Learning

3. Machine Learning Creation
Privacy.
Not unique to ML, of course, or even to the web.
Much greater awareness with the ubiquitous presence of modern systems
Who owns your data? Opt-in vs opt-out, can you remove your data?
Accuracy
Does the machine learning practitioner have an ethical obligation to make sure that the data used for learning are good?
Or an obligation to test and properly interpret the effectiveness of the system?
Is this different from the ethical requirements for any other software developer?
Villanova University
Machine Learning Project
Ethics and Machine Learning

4. Ethics and Machine Learning
Machine Learning Use
Bias
For some decision-making in the US there are protected categories. You cannot base a loan decision the applicant’s age, for instance.
If the decision is made by a machine-learning based system, such as a decision tree or neural net, the organization is nonetheless responsible for the result
Most obvious problem is when the training data themselves incorporate a bias, intentionally or not.
(recent example of ML system preferring caucasian pictures?_
Villanova University
Machine Learning Project
Ethics and Machine Learning

5. Machine Learning Use For Individuals
Privacy again
Systems inferring information about individuals which the individual would not reveal
Target case
Finding, for instance, potential terrorists
Accuracy again
Does the system take action about individuals based on the output on an ML system?
False negatives, false positives
One question is what you do with the decision.
One solution: allow “desirable” decision, refer “undesirable decision” to a human. EG, making a loan. System can say yes, human has to say no.
Villanova University
Machine Learning Project
Ethics and Machine Learning

6. Machine Learning Use for Society
Widespread use of machine learning systems may have broad-reaching consequences
Automation may lead to loss of jobs now captured by machine
Intelligent systems may open up new jobs to less skilled employees.
Scanners in grocery stores are good examples of both.
ML systems may improve overall efficiency of most processes
20-hour work week?
Assistive technologies
More efficient use of power and other natural resources
Villanova University
Machine Learning Project
Ethics and Machine Learning

7. Ethics and Machine Learning
Some questions
How many jobs in the US are “driver”, and could be replaced by a self-driving car?
Would you prefer a human or an artificial intelligence making triage decisions in an operating room?
Should a person in Spain be able to prevent Google from showing in a search in the US an accurate but outdated negative fact about him?
Should a system deciding whether to recommend an antibiotic for a sore throat err in the direction of false positive (unneeded prescription) or false negative (don’t give needed prescription)
What about to recommend major surgery?
Villanova University
Machine Learning Project
Ethics and Machine Learning