Using AI & Analytics to Stop Police Brutality Against Minorities
By: Gary Cheung
Tags: Racial Justice, Artificial Intelligence, Police Brutality, Data Science, Big Data

Recently, there have been many debates on how law enforcement uses AI to watch citizens and violate our privacy. However, turning this idea on its head, why can’t AI be used to watch police officers, thus ensuring they also follow the rules? Applied artificial intelligence can provide a more automated and objective way to ensure justice for racial minorities and prevent police brutality.

There may be hesitation in using machine learning for surveillance due to the moral nature of the issue. However, I will note that this is already happening in white-collar workplaces. For example, investment banks routinely monitor all communications for insider trading and money laundering. This monitoring is critical to ensuring that banks do not incur massive penalties for violating regulations.

The modern-day police force has regulations and rules of engagement in place to handle day to day operations such as resisting arrest or addicts overdosing. Police departments already collect data in the form of incident reports and arrest reports. Unfortunately, data quality can sometimes be lacking. As the New York Times reported, the police report for Breonna Taylor’s incident completely lacked the necessary details to investigate the issue.

Police also collect data on dash cameras and body cameras. These became especially popular in 2014 after the tragic incident resulting in the unarmed shooting of Michael Brown, which led to nationwide riots and social unrest.

Let’s use this data to train a machine learning model and generate a scorecard for each officer. We want to know for each interaction with a given officer, was the officer compliant, and how did she or he handles the interaction compared to a baseline. This scorecard isn’t a replacement for internal auditors who review police incidents and complaints, but rather a tool to help with monitoring police interactions. Manually sifting through every police interaction is not feasible, why not use machine learning to automate the process and remove bias.

How Do We Do It

The first step is to train the existing dash/body camera and voice data to determine if the actions taken by officers were compliant. To do this, we need to extract information from the video/audio, such as sentiment analysis and the sequence of events. Next, we want to train a machine learning model to determine if the actions were compliant. Finally, we want to build a dashboard to visualize and score each officer.

Prepare The Data

Audio Analysis

The first step for audio analysis is to transcribe the audio to text. We can use a combination of using Spacy and neural networking packages, such as Keras or Tensor Flow, or cloud services such as Google Voice AI and Amazon Transcribe to achieve this.

Post-processing, there are many insights we may want to extract, such as Sentiment analysis based on language to determine if the language used by officers were overly hostile or violent.

NLP and time series analysis can also be applied to determine if the order of sentences follows the expected procedure for a given situation.

The challenge here is distinguishing between officers, bystanders, and the suspect. There are two possible routes here: 1. have the algorithm recognize the officer’s voice, through speech recognition input, and identify the voice uniquely. 2. Determine the role based on the context of speech.

Video Analysis

In our video analysis, we want to use a neural network package or leverage cloud solutions such as AWS Rekognition or Google Video AI to tag interactions in the video. This model will identify specific behaviors such as resisting arrest and physical altercation, being handcuffed. Once these behaviors are labeled, a time-series sequence of events is created to understand what happened in the interaction.

Train a Neural Network

Going through a set of police reports with the body cam videos, an auditor can tag whether or not the incident complies with the documented procedure. Next, train the data using a Neural Network such as Keras or Pytorch. In this case, the objective function will be- Was the officer compliant in the given situation? This machine learning model should specify for future interaction if the actions taken by officers were or were not compliant with procedures.

Metric Scoring

Using this information we want to build a scorecard to create a compliance score for each officer. This scorecard will be used to identify officers at high risk of using excessive force or using racial bias.

Example Metrics-

Language sentiment (hostile, friendly, neutral)

Was protocol followed- example Miranda rights

Was there racial bias in the interaction?

Did the officer try to de-escalate the situation before applying force? For example, the time between conversation and force used.

Example dashboard

The tool’s intended usage is to assist the police force in preventing incidents instead of punishing officers. Today, law enforcement is highly subjective; however, there are many data points collected by police to create a more objective way to view police interactions. Ultimately, I think a tool like this would be highly beneficial and effective in building trust within the community and holding law enforcement to a higher standard.