Machine Learning — An Ethics Review
By: Mike Erb
Tags: Machine Learning, Ethics, Data Science, Artificial Intelligence

Photo by Ümit Bulut on Unsplash

I’m finishing up my Data Science Program at the Flatiron School, looking back at the five projects I did, and considering what ethical concerns should I have considered if the projects were going to be put into production and affect real people. While an introduction to machine learning ethics was part of the Flatron program, integrating an ethical analysis into each project was beyond its scope.

Five Principles

I will be using the five principles that Floridi and Cowls put forth in their 2019 paper A Unified Framework of Five Principles for AI in Society to do a top level review of my projects. The principles being beneficence, non-maleficence, autonomy, justice and explicability. Beneficence meaning “promoting well-being, preserving dignity, and sustaining the planet”. Non-maleficence meaning “privacy, security and ‘capability caution’”. Autonomy referring to the “balance between the decision -making power we retain for ourselves and that which we delegate to artificial agents”. Justice meaning “promoting prosperity, preserving solidarity, avoiding unfairness”. And explicability meaning “enabling the other principles through intelligibility and accountability”.

Project #1 — Movie Genre Recommendations to a New Business

My first project was based upon making recommendations to a fictional new movie studio in regards to what genre of movies to create. It didn’t actually involve machine learning, it was purely based upon data analysis; however, creating a machine learning model that made the recommendation instead of the human analyst is not hard to imagine nor would it be hard to implement.

What would it mean in relation to the Five Principles for a movie studio to ask a machine learning model to tell them what genre of movie to make? In this project the business was to be considered a for profit corporation. It would be interested primarily if not exclusively in financial gain, and from the analysis the genre that I recommended was animation. I used data from The Movie Database and the possibilities for what genre to recommend were limited. A model could have made a similar recommendation and updated its recommendations as the dataset changes over time. With entertainment and employment being seen as goods it could be viewed as being beneficial. There seems to be little possibility for privacy or security violations and passing the autonomy test would depend upon implementation. Would the decision be the model’s alone or would there be a combination between some humans and the model? If left to the model alone, considerations other than short term financial gain may never be considered. When considering justice, this would also depend upon implementation, but if the only question is genre, then the aspects of justice would be dependent on the human implementation of the model’s recommendation. With regards to explicability, it would again depend on the implementation of the model. However the fact that the dataset from The Movie Database is publicly available would go along way towards the intelligibility of the model.

Because no machine learning was actually done with this project, the analysis is mainly speculative and would depend upon the actual implementation a machine learning model. Let’s move on to the next four projects that did use a machine learning model.

Project #2 — Housing Price Prediction as a Service

This project was based upon a fictional service that would provide housing recommendations to people moving to the King County, Washington State area or wanting to move to a new location in that area, and to provide similar data to businesses so that they could make informed salary decisions.

The machine learning aspect of this project was housing price prediction using a modified version of the King County House Sales dataset.

Let’s start the ethical analysis. I’ll be looking at the project as a whole not just the machine learning aspect. For beneficence, the knowledge that could be provided to potential homebuyers and their employes could be seen as promoting the homebuyers well being by providing them with information that might be hard or time consuming to compile themselves. The dataset does not use personal information that isn’t already publicly available from King County and so would cause no harm to people’s privacy. In terms of autonomy, this service gives almost all the autonomy to the human actors. The consumers of the information can use the advice given by the service or not with no repercussions. With regard to justice, the service may end up contributing to gentrification and price out existing members of neighborhoods that the service recommends as being a preferable balance of price and convenience without regard to the people currently living there. For explicability I’ll look at both the model and the dataset. First, the dataset is publicly available. The model used for housing price prediction is linear regression which has a lot of explainability built into it. It can provide a list of parameters that are being used and their importance to the calculated housing price.

The only potential ethical issue with this service is its potential contribution to gentrification. How could this service be more just so that housing costs in specific neighborhoods do not quickly escalate? This a complex question that the community would need to participate in solving, but one idea would be to add a level of complexity to the service so that pricing trends could be taken into account and homebuyers would be discouraged from buying in neighborhoods where prices are rising too quickly.

Project #3 — Tanzanian Waterpoint Classification

This project was to create a model that could be used to predict the classification of a waterpoint as either functional, functional needs repairs or non-functional so that the Tanzanian government can better use its limited resources to better provide clean water to its population.

Right away this project looks beneficial, its helping to provide clean water to people that need it. But when considering non-maleficence in combination with autonomy it is not as clear cut. There are no privacy or security concerns, but the model, if given too much autonomy, could lead to some people never having their local waterpoint serviced. False positives of a functional waterpoint would lead to those waterpoints being ignored. When considering justice, it is again unclear what the outcome would be. While having a model determine what waterpoints need to be visited for service could remove bias from the human decision making process, we do not know what, if any, bias exists in the source dataset. Did the people that made the inspections and recorded the results as well as information about each waterpoint do a good job? Did they inspect all the waterpoints with equal attention or accuracy? Were any waterpoints purposely ignored and therefore left out of the model? Without knowing the answers to questions like these we can’t know what biases may be built into the model via the source data. Now let’s look at the model itself in terms of explicability. The model that performed the best and chosen for the implementation was a random forest classifier, which has limited interoperability. It can tell us which features are important overall to the model, but being able to explain the classification of a particular water point would not be easy given the large number of trees involved in a random forest model.

The lack of true interoperability in a random forest model and the lack of information regarding what biases may be be embedded in the training dataset lead me to think that a more interoperable model should be used if one can be found that has comparable performance. That way some of the known misclassifications can be audited to understand why the mistake occurred, and improvements to the model be made.

Project #4 — Twitter Sentiment Analysis

This project was to build a sentiment analysis model to help a fictional company monitor and react to the public reception of its new phone and to do market research on its competitors.

It used a machine learning model to classify the sentiment of tweets, but left interpretation and response to human analysts and decision makers. The benefit of the project would be for the company to provide a better phone given the feedback from Twitter users. There isn’t any non-maleficence, no private information is used in the model, but the model is trained on a public dataset that does contain twitter handles. Although the dataset was not created for this project, whether or not to include those twitter handles should have been considered by the creator of the dataset who should have done some sort of anonymization of the data. While those tweets were made publicly, they now have a lifespan which the person who made the tweet no longer can control. This system gives full autonomy to the human actors by only giving them recommendations and observations. The question of justice doesn’t apply to this project because of its limited scope. The model used for the sentiment analysis is a naive bayes classifier which is interpretable and the dataset is publicly available making the project explicable.

Project #5 — Fake News Classification

This project was to create a model for a fictional social media company to classify news stories posted to its platform as fake or truthful to help them respond public accusations of allowing fake news to proliferate on its site and then monitor for fake news going forward.

The benefit of the project is the potential for removal or the limiting of fake news on this platform which would reduce the amount of dis and mis-information people are personally confronted with or members of their community are confronted with. Fake news being understood to cause harm to its readers or to the public because of its intention to misinform the reader for the benefit of the creator or people or groups that the creator supports. When addressing non-maleficence, freedom of the press and speech issues come into play. In this project no posts were to be altered or removed because of a classification, that would up to a human decision maker, but if the model were given the autonomy to remove posts based upon its classification, the question of censorship and bias would need to be raised. What made the story Fake? Is it actually mis or disinformation, or is it satire, sensational or questioning the dominant story line? As implemented, full autonomy is given to human decision making. When considering justice, the accuracy of the model must be considered. The model performs at 89% accuracy balance between the two classes, but what about the 11% that are incorrectly classified? Is it just to take actions based upon this model given the potential for misclassification of news stories that have real public value, or does their removal outway that potential value? Does the dataset that the model was trained on contain any bias towards certain types of fake news? Who gets to decide the true definition of fake news and how to they decide? This project used a random forest classifier, which, like was explained above, is only partially explainable. Using a model that is more explainable would be better given the high value given to the freedom of speech and the need to explain the classification of specific news stories not just present overall feature importances.

This project raises a lot of ethical questions in regards to non-maleficence, justice and explicability, some would have answers if it was actually implemented or had an implementation plan, but there are others that would still exist and would deserve thoughtful answers.

Conclusion

While these projects appear to be mostly ethical, they all raised a number of questions that would require different amounts of deeper investigation or introspection in order to complete a true ethical analysis. That said, I think this was a worthwhile exercise for me and I hope for you as well.