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Automated Prediction 
Perception, Law, and Policy
Overview 
• Author 
• Introducing Automated Prediction 
• The Case For AP 
• Other Considerations & Challenges 
• Take Aways 
• Discussion
Author 
Dr. Tal Zarsky
Automated Prediction (AP) 
BIG DATA 
Predictive Analytics
Automated Prediction (AP) 
(Personal Information) 
AP 3000 
IN 
OUT 
CRIME 
PREDICTION
Automated Prediction (AP)
Automated Prediction (AP) 
KEYWORD 
ASSOCIATION 
IP ADDRESS WEBSITE 
PURCHASE 
TARGET POOL 
Probability 
High
War on Terror
nctc.gov 
-McKean, Virginia
The Greater Good? 
Privacy 
vs Safety
Cost & Efficiency 
Human Driven Computer Driven
Vast Amounts of Data 
- Hard for humans to analyze 
- Easily analyzed by computers 
- Programmed by humans 
- Errors and Bias
We Make Mistakes
Hidden Biases 
Values 
Prejudice 
Beliefs 
Identity 
Culture 
Status 
Emotion
Equality & Fairness
Take Aways 
• AP will be hard to do right but 
the positives outweigh the negatives 
• Good for some complex and costly 
problems - hopefully terrorism 
• If done right, AP will increase 
equality & fairness
Questions Comments 
- Robot Fortune Teller London, England 1934

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Automated Prediction

Editor's Notes

  1. Dr. Tal Zarsky – is the Senior Lecturer in the Faculty of Law at the University of Haifa in Israel. His specializations are listed as Property, Cyberlaw, Information Privacy, and Telecommunication Law and Policy and much of his research centers around Data Mining issues. He did his undergrad in Law & Psychology at the Hebrew University of Jerusalem and both his Masters and Doctorate of Law at Columbia University.
  2. So Automated Prediction, what exactly is it? Well there are a handful of interchangeable terms all of which fit within the larger and growing field of Big Data and Predictive Analytics. This has been a growing field that has garnered a great deal of attention.
  3. I realize we’ve all likely heard of it but the idea is that they can tap into the growing pool of available data, often and increasingly what they refer to as Unstructured Data, and they work to extract new knowledge and information from it. In this article Zarsky uses the term Automated Prediction and focuses specifically on the technologies application towards predicting and fighting crime.
  4. In the context of crime, Zarsky defines Automated Prediction, or “AP” as we will refer to it here, as the practice of using computerized algorithms to analyze human behavior patterns to predict, prevent, and police antisocial behavior.
  5. This may be obvious but the key takeaway is that this technology is not explicitly predicting the future, it still depends on the science of probability. It looks to create actionable and accurate likelihoods from data that humans could not generate without the technology. Governments are the key player that Zarsky focuses on here. Zarsky mentions a few different specific crimes, or Anti-Social Behaviors – as he refererred to them, that governments use AP to combat, they included Tax Fraud, International Parcel and Travel Crime, Insider Trading, but the crime he chose to focus mostly on was Terrorism.
  6. Zarsky mentions how in America the policy decisions and public response to 9-11 created an opening that allowed the government to move towards more and more invasive tactics, in terms of privacy. (Needs checked, just typing thoughts in here, I am not sure if he said this exactly.)
  7. Zarsky explains that today, the National Counter-Terrorism Center is the lead government agency using AP to take personal information and other data to build enormous databases with the aim of helping to combat terrorism.
  8. Zarsky explains that, “ When Personal Information is Available & The Cost and Difficulty of Enforcment is High, Governments now “Consider” Automated Predictions. To talk specifically about laws Zarsky highlighted two key principles of the European Data Protection Directive. The first is for cases when information is used for purposes other than their original intent, in this case under the law, consent should then be received from the individual the data relates to. But Zarsky admitted that this is currently not the case. The second principle is that all AP processes that could potentially have substantial impact on an individual should have some element of human review.
  9. Zarsky explains that, “ When Personal Information is Available & The Cost and Difficulty of Enforcment is High, Governments now “Consider” Automated Predictions.
  10. He did mention that outside of commercial success application in the areana of crime is unproven and hard to prove. One interesting point he made and admitted was that AP has only proven to be successful in the commercial realm. He explains that AP is still unproven in government applications and that it will be challenging for the public to be able to measure success there.
  11. Zarsky admits that systems make errors but systems driven more by human intervention are much worse.
  12. Because we also allow Bias to enter into our decision making process. Computer or Automated systems do not and thus they promote increased equality and fairness into any such process.
  13. Because we also allow Bias to enter into our decision making process. Computer or Automated systems do not and thus they promote increased equality and fairness into any such process.
  14. Successful application can only be achieved if it is logical and respects individual liberty. This will be difficult but the positives far outweigh the negatives. Though the author is strongly in favor of AP he gave extended and fair consideration to many of the counter arguments throughout this article.
  15. Photo is of the Robot Fortune Teller, from a 1934 British Department Store anniversary celebration. exhibited as part of the 25th anniversary celebrations for Selfridges department store in London in March 1934.