Policing and AI Applications Quiz

Policing and AI Applications

• AI-powered policing applications are discussed in relation to early intervention systems (EIS).

Machine Learning

• AUC (Area Under the Curve) in machine learning classification problems measures the model's ability to distinguish between classes.

Policing Applications

• The binary classification problem in this application is determining the likelihood of an individual committing a crime.
• The main goal of an early intervention system (EIS) is to predict and prevent future crime.

User-Centric Approach

• A user-centric approach focuses on the needs of marginalized communities to ensure digital safety.
• This ensures fairness and prevents bias in the development of digital platforms.

Data Privacy and De-identification

• De-identification involves removing or altering personal identifiable information from datasets.
• Irrevocably deleting personal information from datasets is a form of de-identification.

Regulatory Frameworks

• The General Data Protection Regulation (GDPR) is a key regulatory framework focused on data privacy in the era of big data.

Legal Information Accessibility

• Technology has significantly improved access to legal information for the general public.

UN Definitions

• Katrina Denver referenced the UN definition of human rights during the discussion.

Personalized Algorithms

• Personalized algorithms raise concerns about influencing user behavior.

Surveillance Capitalism

• Surveillance capitalism focuses on extracting data from individuals to create profit.

Web Server Personalization

• Web servers identify and personalize user experiences through cookies and browsing history data.

Modern Retail

• The main constraint for shoppers and store owners in the old world shopping experience was the lack of information and limited interactions.

Fraud Detection

• Soft fraud involves exaggeration or misrepresentation of information.

Privileged Learning in AI

• Privileged learning is an AI method that leverages additional information about data for better prediction.

Explainable AI

• Explainable AI is used in Shift's fraud detection algorithm to transparently explain the reasons behind decisions.

Insurance Industry Challenges

• The main challenge faced by the insurance industry due to digitization is the need to adapt to changing customer expectations and new risks.

AI Overfitting

• Overfitting occurs when a model performs well on training data but does not generalize to unseen data.

Big Data Characteristics

• The three V’s of big data are Volume, Variety, and Velocity.

AI and Big Data

• The primary advantage of using big data in the context of AI is the ability to train models and make accurate predictions.

Turing Test

• The Turing test assesses a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from a human.

Generalization in AI

• Generalizability in AI refers to a model’s ability to perform well on tasks that have never been encountered during training.

General AI

• General AI refers to an AI system with human-level intelligence capable of performing any task a human can.

AI Goals

• The main goal of artificial intelligence (AI) is to create intelligent machines that can solve problems and improve decision-making.

Big Data Value

• The main value of big data lies in its potential to extract insights, patterns, and knowledge.

Non-standard Data

• Non-standard data is characterized by being collected in a non-conventional or non-structured way, like unstructured text or sensor data.

Big Data vs. Classical Data Science

• The key difference between big data and classical data science is the focus on data volume and scale in big data.

Big Data vs. Alternative Data

• The distinguishing line between big data and alternative data is the source of the data. Alternative data refers to non-traditional data sources beyond traditional business data.

Big Data Characteristics Defined

• Doug Laney’s three V’s of big data are Volume, Velocity, and Variety.

Skills Needed for Big Data

• Data science skills are essential for individuals working with AI technologies in the age of big data.

ShotSpotter

• ShotSpotter uses acoustic sensors to detect shots fired.
• Ralph Clark changed the ShotSpotter business model to focus on proactive crime prevention by providing real-time information to police.

Data Scale

• A zettabyte has 21 zeros.

Distributed Data Access

• Cloud computing allows access and interaction with large amounts of data across various locations globally.

Deep Fakes

• Generative adversarial network (GAN) models are primarily responsible for generating deep fakes.

AI Image and Video Generation

• Generative AI creates hyper-realistic images and videos of non-existent entities.

AI Training

• The "test" in AI training is used to evaluate the model’s performance on unseen data.

Big Data Challenges

• The main challenge of working with big data is managing its sheer volume and complexity.

Energy Consumption in AI Models

• Factors contributing to energy consumption in large models include training on large datasets and model size.

Smaller AI Models

• Larger language models are adopting smaller, more efficient architectures to reduce energy consumption.

Transformer Models

• Transformer models have a core feature called attention that allows them to efficiently process sequences of information.

Word Embeddings

• Word embeddings like Word2Vec achieved a breakthrough by representing words in a continuous vector space, capturing semantic relationships.

Natural Language Processing Limitations

• Statistical models used in early natural language processing were limited by relying on explicit rules and lacked the ability to handle complex language structures.

AI Governance

• A governance model is suggested for implementing AI in public services to ensure fairness, accountability, and transparency.

This quiz explores the intersection of policing and artificial intelligence, focusing on early intervention systems and their role in crime prediction. It also examines machine learning concepts such as the Area Under the Curve (AUC) and the importance of a user-centric approach to ensure fairness in digital platforms. Moreover, it discusses critical aspects of data privacy, including de-identification techniques.