Knowledge Base of the Tool

The tool's knowledge base consists of several key components, including technical best practices, ethical AI principles, legal regulations, and AI$-$specific guidelines like the EU AI Act. Here's an analytical description of the tool's knowledge base:

Technical Knowledge:

Bias Mitigation:

The tool uses well$-$established machine learning principles that recommend the use of diverse datasets to reduce bias in AI models. This comes from research in natural language processing $($NLP$),$ which shows that models trained on biased data can unfairly discriminate against certain groups.

Privacy and Security:

The tool$$s knowledge base includes data privacy principles, especially from frameworks like GDPR$,$ which are essential for any system handling sensitive personal data. Privacy$-$preserving techniques like data anonymization, encryption, and access control are embedded in the tool's assessment.

Legal Frameworks and Regulations:

GDPR: The tool integrates key privacy principles from GDPR $($General Data Protection Regulation$),$ which is a cornerstone of data privacy law in Europe. GDPR provides rules for collecting, processing, and storing personal data, especially when that data includes personally identifiable information $($PII$).$Mitigations are based on AI development best practices $($for dataset diversity and privacy$)$ and legal regulations $($GDPR and AI Act$).$

Knowledge Base includes technical research on bias and fairness in AI$,$ legal requirements from GDPR and the AI Act, and ethical AI principlesDefine the TAI Protocol

LLM Risk Mapping $($Source: $)$

Associate Risks with Life$-$cycle phases

Associate Risks with TAI principles $($HLEG$)$ Risk Mapping & Mitigation Measures proposition

User Input:

$....$

User feedback:

Potential Risks$/$Risk Categories

Potential Mitigation Measures

Prompts for further analysis $($Customised prompts$)=>$ from our prompt library & user input

Potential Benchmarks Scenario Description:

A law enforcement agency deploys a real$-$time facial recognition system to monitor and identify potential criminal suspects in a busy urban environment $($e$.$g$.,$ a train station, airport, or public square$).$ The system analyzes live video feeds from public cameras and processes biometric data $($facial images$)$ to match individuals against a criminal database in real$-$time. The system aims to improve public safety by flagging individuals with active arrest warrants and preventing potential security threats.

However, this deployment introduces significant privacy, data governance, and bias concerns as it involves the processing of sensitive biometric data on a large scale.

User Inputs for the Tool:

AI System Lifecycle Phase:

Operation Phase $($The system is already deployed and actively being used in real$-$time$).$

TAI Principle for Testing:

Privacy and Data Governance $($Due to the sensitive nature of biometric data processing in public spaces$).$

Domain of Application:

Law Enforcement $($The system is deployed by law enforcement agencies to monitor and secure public spaces$).$

Type of Input Data:

Biometric Data $($Facial recognition data collected from real$-$time video feeds$).$

Type of AI System:

Predictive Models, Facial Recognition $($The system uses facial recognition algorithms to identify individuals and predict potential threats based on their movements and matches with the criminal database$).$

Intended Use:

To monitor and track individuals in real$-$time by law enforcement in a public space to identify potential security risks, flag criminal suspects, and enhance public safety.

Expected Results from the Tool:

Summary:

Lifecycle Phase: Operation Phase

TAI Principle: Privacy and Data Governance

Domain: Law Enforcement

Input Data: Biometric Data $($Facial Recognition$)$

AI System: Predictive Models, Facial Recognition

Intended Use: To monitor and track individuals in real$-$time, identifying potential threats and enhancing public safety.

Identified Risks:

Risk $1$: Potential Bias in Facial Recognition

Facial recognition systems have been shown to exhibit bias, particularly against individuals from certain demographic groups $($e$.$g$.,$ ethnic minorities, gender$).$ This could lead to disproportionate false positives, especially in high$-$traffic public spaces.

Risk $2$: Data Privacy Concerns

The real$-$time collection and processing of biometric data in public spaces pose significant privacy concerns, particularly under the GDPR$,$ as facial images are considered sensitive personal data.

There is a risk of over$-$surveillance and potential misuse of personal data by authorities.

Mitigation Measures:

Mitigation $1$: Regular Bias Audits and Dataset Improvements

Conduct regular bias audits on the facial recognition system and improve the diversity of the training datasets to reduce errors and ensure fairness across demographic groups.

Mitigation $2$: Data Encryption and Access Controls