Utilization of Artificial Neural Networks in the Prevention of Child Abuse

By Jeffrey D. Jacob

What is an Artificial Neural Network?

An artificial neural network is a computational model inspired by animal central nervous systems (in particular the brain) which is capable of machine learning and pattern recognition. It is usually presented as a system of interconnected "neurons" that can compute values from inputs by feeding information through the network.  Throughout this presentation, the term “Artificial Neural Network” refers to a feed-forward, multilayer perceptron type of neural network. “Feed forward” means that the values only move from input to hidden to output layers; no values are fed back to earlier layers.

How can an Artificial Neural Network help in the prediction and prevention of child abuse?

This presentation is dependent upon the ASSUMPTION that:

Child Abuse, in its varying degrees, is always a “deterministic output” which is always preceded by a common pattern of variables. This is true, even when the child abuse incident

seems to be a “random” act.

If this ASSUMPTION is true, then an Artificial Neural Network, which has proven to be an extremely useful method[1][2] of accurately determining pattern recognition, could potentially be a powerful tool in recognizing the common patterns of variables that precede child abuse, and predict the potential for abuse with a high degree of accuracy.

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Possible Roadmap For The Creation of a Neural Network That Can Predict Child Abuse

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Development Life Cycle To Prototype: 10-12 Months

Theory DefinitionDefine Theory of Child Abuse Prediction (TCAP)

Live Data AcquisitionAcquire real world data and reorient and redefine TCAP

Outputs & DescriptorsDefine “Predicted Output Identifiers” and “Child abuse Descriptors/Inhibitors

Train CAP-ABUSE-NET Use acquired data to assign synaptic weights and train data

Test CAP-ABUSE-NETAcquire 2nd data set to test and compare predicted results. Re-train if necessary.

Development Life Cycle of CAP-ABUSE-NET

The artificial neural network will be denoted as: Correlation-Analysis-Prediction of Abuse Network or CAP-ABUSE-NET

Funding of CAP-ABUSE-NET

There will be no cost to the Los Angeles County Taxpayer!

A 501(c)(3) non-profit entity, Creative Cyber Systems ( C2Systems ) will be formed.

C2Systems will fund the project.

C2Systems will offer a platform through which scientists, information technology experts, etc. can donate their knowledge and time to building CAP-ABUSE-NET

All the science and technology will be open source, for any municipality to use at no charge

The anticipated cost to the non-profit entity is projected to be under $750,000

MILESTONE 1: Define Theory of Child Abuse Prediction

The available academic literature indicates that there are four main areas of abuse that can be categorized [3]:  

PHYSICAL – SEXUAL – PSYCHOLOGICAL/EMOTIONAL – NEGLECT

Within each category, there are varying degrees, which will need to be decomposed into the output neurons and labeled “Predicted Output Identifiers.” 

Because of the critical nature of this endeavor all pride, assumptions, prejudices, and bias must be sacrificed in favor of determining the Output neurons that most accurately represent the realities of child abuse!

MILESTONE 2: Live Data Acquisition

Developers will reorient and redefine “Theory of Child Abuse Prediction” as necessary based on the acquired data using John Boyd’s Observation-Orientation-Decision-Action (OODA) Loop [4].

MILESTONE 3: Define Outputs & Descriptors

The goal is to hire 65-75 post-graduate college students for the summer. They will attend a 3 day training symposium that instructs the decomposition of WIC 300 petitions*, social worker reports*, social worker case notes*, dismissed WIC 300 petitions*, cases where “substantiated” allegations changed to “unfounded”* and other data* into definitive “Predicted Output Identifiers” (PRE-IDs) and further define Child Abuse Descriptors / Inhibitors (CHILD-ADs / CHILD-AIs) for each PRE-ID.

Slides 8,9,10 that follow represent example PRE-IDs, CHILD-ADs, and CHILD-IDs extracted from the live data.

*It is assumed that any identifying information as to child’s / parent’s name, address, will be removed before data provided.

MILESTONE 3: Define Outputs & Descriptors Cont’d

Example : Predicted Output Identifier (PRE-IDs)

Predicted Output

Identifier

Predicted Category Predicted Reporting Candidate

PHYSICAL ABUSE  P1D Death SubstantiatedP1S Severe injury SubstantiatedP1J Injury SubstantiatedP1P Temporal pain SubstantiatedP10 No indication of abuse Unfounded

PSYCH/EMOTIONAL ABUSE  E1S Severe psych/emotional abuse SubstantiatedE1J Injurious psych/emotional abuse SubstantiatedE1P Temporal hurt feelings SubstantiatedE10 No indication of Psych/emotional abuse Unfounded

SEXUAL ABUSE  S1P Evidence of sexual abuse SubstantiatedS10 No indication of sexual abuse Unfounded

NEGLECT  N1S Severe Neglect SubstantiatedN1J General Neglect SubstantiatedN10 No indication of neglect Unfounded

MILESTONE 3: Define Outputs & Descriptors Cont’d

Example : Child Abuse Descriptors (CHILD-ADs)

Child Abuse Descriptors

Correlating to Predicted Output Identifiers

Synaptic Weight

   Broken bone P1D, P1S .2, .72nd Degree Burn P1D, P1S, P1J .2, .4,.7

3rd Degree Burn P1D, P1S  .4, .5Prior Referral x2 P1D, P1S, P1J, P1P, P10  .3, .4, .5, .2, 0PEREX Death Threat P1D, P1S, P1J, P1P, P10  .3, .1, .1, .1, 0

Example : Child Abuse Inhibitors (CHILD-IDs)

Child Abuse Inhibitors

Affecting Predicted Output Identifiers Synaptic Weight

   Stable Family Life P1D, P1S, P1J, P1P, P10 -.5, -.3, -.2, -.1, 0History of Lying P1D, P1S, P1J, P1P, P10 -.6, -.4, -.3, -.2, 0

Cooperative PEREX P1D, P1S, P1J, P1P, P10 -.5, -.3, -.2, -.1, 0P1D, P1S, P1J, P1P, P10 -.6, -.4, -.3, -.2, 0P1D, P1S, P1J, P1P, P10  

MILESTONE 3: Define Outputs & Descriptors Cont’d

Example : Additional Child Abuse Descriptors (CHILD-ADs)

Frequent Nightmares Frequent Referrals Unexplained Anger at

PEREX Specific anger at PEREX Unexplained Crying Fits Alcohol, strange smell on

child Animal abuse by PEREX Sleeping Area -prevalence

of urination Hospitalization Suicide Attempt Suicide Verbalization Suicide Attempt Sibling Marks, Scars in Unlikely

place(s) (behind ears, under armpits, on bottom of foot)

Suicide Verbalization Sibling

Bruises, Scars, Unexplained marks

Evidence of Cutting Self Esteem PEREX Perception of Child Female Intuition CSW Intuition Fear PEREX Criminal Record Mental Health Issues

(PEREX) person of exposure

Academic Deviation School Behavior Deviation Inconsistent Explanation

of Bruises, Scars, Unexplained marks

PEREX Life Change (loss of job, retirement, etc.)

Evidence of Damage within the Internal Living Space

Admission of Physical Assault from Victim

Admission of Physical Assault from Perpetrator

Admission of Physical Assault from Witness

Pornography in open Significant Presence of

Alcohol PEREX treated for

alcoholism PEREX Related Suicides PEREX Related Deaths

MILESTONE 4: Train Model for CAP-ABUSE-NET

Once the data is decomposed and compiled into Child Abuse Descriptors & Inhibitors (CHILD-ADs / CHILD-IDs), these inputs will be fed into a decision neuron. This decision neuron can have any number of inputs from one to n, where n is the total number of inputs. The inputs may be represented  therefore as x1, x2, x3… xn. And the corresponding weights for the inputs as w1, w2, w3… wn.

The summation of the weights multiplied by the inputs be written as x1w1 + x2w2 + x3w3 …. + xnwn,  which is the activation value. This may also be mathematically represented by the following equation:

If activation value u is greater than threshold value θ, the decision

neuron outputs 1. If activation value u is less than threshold value θ, the decision neuron outputs 0.

MILESTONE 4: Train Model for CAP-ABUSE-NET Cont’d

The feed forward process can be represented by the following diagram where x0, x1, x2… xn represent inputs CHILD-AD / CHILD-ID and the corresponding weights for the inputs as wk0, wk1, wk2… wkn ( k can be implemented as a bias factor).

Output as yk will represent Predicted Output Identifiers (PRE-IDs) which will either be 1 or 0 (1=Positive Prediction; 0=Negative Prediction)

MILESTONE 5: Test Model for CAP-ABUSE-NET

Once CAP-ABUSE-NET has been trained, a second segment of data should be fed into the network and the yk and the positive/negative Predicted Output Identifiers (PRE-IDs) should be compared to the actual positive/negative results (Vk) of the test data. (Model can be re-trained as necessary)

Practical Implementation of CAP-ABUSE-NET

The CAP-ABUSE-NET should be packaged into a simple smart phone, desktop, or web application format that correlates simple form based input to its respective CHILD-ADs / CHILD-IDs. This can be accomplished by posing simple survey questions to case workers with confidence values (can also be programmed into a playful format that children can answer):

Child Abuse Descriptor

Correlating to Predicted Output Identifiers

Synaptic Weight

   

Unexplained Marks

P1D, P1S, P1J, P1P, P10 .0, .0, .2, .3, .4

The survey should not take longer than 15 minutes per incident visit to perform.

CAP-ABUSE-NET Accountability Feature

CAP-ABUSE-NET can be programmed to with the ability to allow an export function whereby two xml encoded files containing the results of any IDABUSE jobs (containing all +/- results of per PRE-IDs) may be uploaded into the case worker’s management system and attached to the respective record of the child.

One is confidential and for use only by DCFS (IDABUSE-Confidential.xml) and the other may be provided to an oversight board for accountability purposes (IDABUSE-oversight.xml)

 IDABUSE Job – Confidential For DCFS use ONLY Contains identifying information in IDABUSE_Confidential.xml

output file

IDABUSE Job – Oversight For Oversight usage if necessary Contains no person identifying information in

IDABUSE_Oversight.xml output file

What CAP-ABUSE-NET is Not and Should Not Become

Providing a Case Worker with the CAP-ABUSE-NET tool SHOULD NOT add an unnecessary burden through either a steep learning curve or time required to use the tool. The tool should be a separate and standalone utility from the

Case Worker’s management system to avoid integration headaches.

It is NOT part of the Case Worker’s management system. The tool will exist as a smart phone, desktop, or web application

for the Case Worker, as well as a reader (sanitized of identifying information) for any required oversight board.

 

Final Thoughts

The social workers are the ultimate neural network.

They are responsible for accurately predicting the potential for child abuse and inhibiting the manifestation of actual abuse thousands of times per year.

When they get it right, they are rarely, if ever congratulated or thanked.

When they get it wrong, irrespective of insufficient predictive information or just a bad judgment, the thousands of correct judgments and children they have protected up to that point, mean nothing, as all of the focus shifts to the one wrong/bad decision.

Any technology or utility that is designed to predict a future likelihood of child abuse should exist simply as a tool for a case worker to utilize in appropriate situations; It should not add an undue burden to an already difficult job! 

Bibliography

1. Johnson, G. E., Mimic Nets, IEEE Transactions on Neural Networks, Vol. 4, No. 5, September 1993, pp. 803-815.

2. Patterson, D. W., Artificial Neural Networks, Prentice Hall, Singapore, 1996.

3. Finkelhor and Baron 1986; Beitchman et al. 1991; Russell 1986; Peters 1988; Mullen et al. 1993. Various Child Abuse Publications

4. Boyd, John R. Destruction and Creation, US Army Command and General Staff College, September 3, 1976