Predicting Potential Domestic Violence Re-offenders Using Machine Learning. Rajhas Balaraman Supervisor : Dr. Timothy Graham

Transcription

1 Predicting Potential Domestic Violence Re-offenders Using Machine Learning Rajhas Balaraman Supervisor : Dr. Timothy Graham

2 INTRODUCTION Before we get started, a few definitions : Machine Learning : Branch of computer science, more specifically a branch of AI that enables a computer to learn without being programmed explicitly [1] Predictive policing : An upcoming strategy in law enforcement around the world that focuses on predicting crimes through algorithms that are capable of learning from some historical data [2] Domestic violence : Violent behavior exhibited at a household level - can be emotional, physical, psychological or sexual. [3]

3 Existing approaches to problem (Fitzgerald & Graham, 2016) - Logistic regression approach [4] (Hamilton, et al) - Usage of random forests and neural networks for a classification approach [5] (Zeng, et al) - interpretable classification models for recidivism prediction [6] There are many other approaches in the field that use exclusively/a combination of various classification and regression techniques and models that are available in the supervised learning field including random forests, linear vector machines, neural networks and other statistical models as well *.

4 MOTIVATION( the why) on average 1 woman is killed every week by a current/former partner ~33.3% of women have experienced some form of physical violence ~20% of women have experienced sexual violence ~25% have experienced this violence from a current partner ~25% of women have experienced emotional abuse from a current or former partner. Statistics credit : Domestic Violence Prevention centre, inc. [7]

5 PROJECT GOAL The aim of the project : Explore and compare techniques available in the data mining and supervised/semi-supervised learning fields using various metrics to determine a suitable approach for similar data in the future Main issue to be wary of : Most models and techniques available can be quite black box to the potential user. Making sure the models outputs are interpretable while being scalable is quite important If predictions of recidivism are to be of useful/taken seriously, the predictions are to be at a certain level of accuracy while being easily interpretable.

6 RESEARCH QUESTIONS Can better results(prediction accuracy metrics) be obtained when using more complex models when compared to a standard logistic regression approach? When dealing with the bias that exists in the dataset, can some machine learning techniques(supervised, unsupervised, semi-supervised) offer a better prediction rate as opposed to some others? What kind of prediction results can be used to make it seem less black-box? What kind of balance should there exist between scalability to larger datasets and the readability and interpretability of results?

7 QUESTIONS?

8 References [1] W. Hosch, "Machine Learning", Encyclopedia Brittanica, inc [2] Ferguson, Andrew Guthrie, Predictive Policing and Reasonable Suspicion (May 2, 2012). 62 Emory Law Journal 259 (2012). Available at SSRN: [3] L. Mitchell, "Domestic violence in Australia an overview of the issues Parliament of Australia", Aph.gov.au, [Online]. Available: /DVAustralia#_Toc [Accessed: 26- Mar- 2017]. [4] R. Fitzgerald and T. Graham, "Assessing the risk of domestic violence recidivism", NSW Bureau of Crime Statistics and Research, [Online]. Available: divism-cbj189.pdf. [Accessed: 26- Mar- 2017].

9 References [5] Z. Hamilton, M. Neuilly, S. Lee and R. Barnoski, "Isolating modeling effects in offender risk assessment", Journal of Experimental Criminology, vol. 11, no. 2, pp , [6]J. Zeng, B. Ustun and C. Rudin, "Interpretable classification models for recidivism prediction", Journal of the Royal Statistical Society: Series A (Statistics in Society), [7]"DOMESTIC VIOLENCE STATISTICS", Domesticviolence.com.au, [Online]. Available: [Accessed: 27- Mar- 2017].