How can algorithms be biased?
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n this talk, we will walk through the steps of how to build an algorithm to predict property prices from a dataset of property listings, focusing predominantly on finding the right features to include in building the model.
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How can algorithms be biased?
- 1. How Do Algorithms Become Biased? Eva Sasson @evasasson #DataDayMX
- 2. @evasasson
- 3. What you’ll learn today 1. How to build a predictive model 2. Where in the building process bias can be introduced 3. What are real - world ramifications
- 4. What are all these “buzzwords” ? ● Data science produces insights ● Machine learning produces predictions ● Artificial intelligence produces actions
- 6. First off, let’s define bias 1. Sample bias 2. Systematic value distortion 3. Prejudice or stereotype bias
- 7. Let’s build our model! Predicting Property Prices
- 8. Clean & Prepare 2 Get data 1 Predict 5 Train & Test 3 Improve 4
- 9. Step 1: Gathering the data
- 10. Getting Data ◂ Public datasets ◂ APIs ◂ Existing datasets ◂ Surveys ◂ Web scrapers
- 11. Getting Data ◂ Public datasets ◂ APIs ◂ Existing datasets ◂ Surveys ◂ Web scrapers: import.io, beautiful soup, scrapey
- 12. Our Model: Predicting Housing Prices
- 13. $$$ Is our chosen prediction variable 13
- 14. Biggest concentration of bias is in the training data itself!
- 16. ◂ Variables remaining can proxy race ◂ If race is a useful predictor, then you have a hole in the data ◂ Indirect discrimination Removing ‘race’ from the dataset doesn’t remove the problem
- 17. Now we know the risks of training data... What do we do now?
- 18. Step 2: Explore and Clean Data
- 19. Clean & Prepare 2 Get data 1 Predict 5 Train & Test 3 Improve 4
- 20. 80% Of data science is cleaning and preparing data 20
- 21. Examples of data cleaning 1. Remove Duplicates 2. Remove Empty columns 3. Remove Not-relevant variables 4. Find averages for empty rows, or mark as 0 5. Remove rows that are blank for the features most important for you 6. Standardize units
- 22. 9,500 Rows started with 5,360 Rows of data remaining 4140 Duplicates and blank data removed 22
- 23. Be careful in the cleaning process! Some variables can be tampered with, dropped, and some cannot. 23
- 24. “ Collecting and cleaning data is an inherently subjective process. -Fabliha Ibnat, researcher at the University of Washington
- 26. Supplement your dataset with new features that might help you!
- 27. Adding additional variables by zip code ◂ Yelp count of stars ◂ Yelp average of stars ◂ Average household income ◂ Per capita income ◂ High income households (% > $200k/yr)
- 28. Yelp data seems pretty democratic, that can’t cultivate bias right?
- 29. Not so fast!
- 31. “ As people talk about authenticity more online, star ratings decrease, independent of food quality -Sara Kay, food educator in NYC
- 32. Low housing prices with many ethnic restaurants High housing prices with few ethnic restaurants Housing prices from Yelp stars
- 33. What happened with Amazon Express?
- 35. We have our data and it’s cleaned and wrangled Now, we’re ready to build our model with it
- 36. Step 3: Train your model & test it
- 37. Clean & Prepare 2 Get data 1 Predict 5 Train & Test 3 Improve 4
- 38. 80:20 split Pick your train: test ratio 38
- 40. Results table
- 41. Variable Importance for Random Forest
- 42. Variable Importance for XGBoost
- 43. 13% Of prediction power based on high household income 43
- 44. “ “Algorithms replicate the status quo” -Cathy O’Neil. Author, speaker, professor.
- 45. Step 4: Improve Hyperparameter tuning
- 46. Clean & Prepare 2 Get data 1 Predict 5 Train & Test 3 Improve 4
- 47. Experiment with Hyperparameter tuning ◂ Increase or decrease number of trees ◂ 10-fold cross validation ◂ Look at depth ◂ Random seed ◂ Where to split the data
- 48. Removing outliers leads to lowest MAPE
- 49. “ Algorithms will do more justice to the people who are easiest to understand at the expense of those who aren’t. -Michael Veale, Phd in Responsible ML at UCL
- 50. Error-Modeling for XG Boost
- 51. Step 5: Predict Put your model into action to uncover predictive insights
- 52. Clean & Prepare 2 Get data 1 Predict 5 Train & Test 3 Improve 4
- 53. Actual Value vs Predicted Value
- 54. Issues and areas of bias with prediction
- 55. Problems of Big Data Hubris
- 57. Algorithms make the same prediction every time.
- 62. Human bias treated as science. Opinion embedded in math. Cathy O’Neil.
- 64. What we can make from all of this Conclusions, Takeaways & Solutions
- 65. What can we do about it?
- 66. “ “When we look at bias as just a technical issue, we are missing the point” - Kate Crawford
- 67. Awareness We must check our training data
- 68. Python Packages FairML package Black Box AI Fairness 360
- 69. Transparency What assumptions were made? How decisions were made ? Who may be affected? What was underlying logic? Who is most at risk?
- 70. Purpose limitation (Just because it exists, doesn’t mean it should be used for new purposes)
Notas del editor
- Hello! My name is Eva. I am SO humbled and honored to be here today to talk to you about this topic that is so important to me. 2 years ago I completed an MSC in business analytics and management science where I learned all about algorithms, including their benefits and risks. Now I’m a PMM at Sentry in SF. Please tweet me with your questions or comments.
- A great example to understand the difference is in autonomous cars stopping at a stop sign. Data Science - understanding false negatives, insights like does time of day matter for the car to stop Machine Learning - gathering a dataset to predict which ones have stop signs or not AI - takes the action to apply the breaks at the stop sign
- Get data, clean prepare and manipulate (feature extraction), train, test, deploy and improve
- Used as a framework
- What are examples of this?
- The bias is still there, even though the variable is removed.
- How do we move forward with building our model?
- Get data, clean prepare and manipulate (feature extraction), train, test, deploy and improve
- Many data sets are collected by a particular entity to answer specific types of questions to accomplish a particular goal. To minimize bias at the data cleaning stage, get context on the data about how the raw data was collected and how certain variables should be interpreted.
- The research on Yelp data posted on eater shows that Mexican cuisine in the US had the highest number of people talking about “Authenticity” following by Chinese, Thai, Japanese, and Indian.
- How would using this data of yelp stars by zip code to predict housing prices affect our model?
- This is another dataset with human-informed, opt-in decisions. Already when using both Amazon Express and Yelp, you have Omitted Variable Bias since the service is voluntary and opt-in.
- Racial discrimination that comes through location-based opt-in data
- So back to our model
- Get data, clean prepare and manipulate (feature extraction), train, test, deploy and improve
- The “test” data is used for evaluation.
- We can’t and shouldn’t blindly look at prediction power, we also need to understand the variables that are predicting it
- This will build on itself in away that the people created it wouldn’t have neccesarily wanted. This will compound and continue a vicious cycle. Rich people’s houses would have a higher value BECAUSE they are rich.
- It’s a self-fulfilling prophecy - it’s reinforcing the vicious cycles of inequality in society, and disadvantages that already exist.
- We looked at this a bit in the last section, but we’re going to try to improve our model even more
- Get data, clean prepare and manipulate (feature extraction), train, test, deploy and improve
- Also notices, it gives rise to new variables for XGBoost
- Algorithms don’t do well with outlier data, like we learned when building our model.
- Bell curve slightly to the left, it is slightly undervaluing the property prices. This would be favorable to buyers but not favorable to sellers. Be mindful of who does this hurt
- Get data, clean prepare and manipulate (feature extraction), train, test, deploy and improve
- This phase is when we bring the bias to life. What’s the problem with using predictive algorithms?
- In 2013 Google miss-predicted the peak of the flu by 140%
- If mulitple companies used that same tool, women would have a hard time getting hired anywhere.
- Personal story
- I hope we We can’t blindly trust the algorithm.
- We can check the way that decisions that we make affect the models that we build which can affect real people’s lives in the world. Obviously, awareness. We’ve touched on this. That’s my whole point of talking to you about this.
- This is for now.
- Right now, it’s a fact, one small, homogenous group of people, make decisions that affect everybody. The people who create this generally look like each other, with a similar upbringing, look and talk the same way. When we have more people of color training image-recognition models, we’re less likely to have self-driving cars that can’t recognize people of color. When we have more women writing software and training data models, we’re less likely to have hiring algorithms that discriminate against women. When you bring your diverse perspective to the conversation, you change the conversation.