XAI in Security and Surveillance: A Double-Edged Sword

XAI in Security and Surveillance: A Double-Edged Sword

XAI in Security and Surveillance: A Double-Edged Sword

Introduction

Security and surveillance systems are increasingly powered by AI—whether in facial recognition, anomaly detection, or predictive policing. Here, XAI (Explainable Artificial Intelligence) plays a paradoxical role: it can strengthen accountability and fairness, but it can also expose system weaknesses to bad actors. Balancing transparency and security is the central challenge.

Key Terms

  • False Positive Rate (FPR): The proportion of innocent people incorrectly flagged as threats.
  • Threshold: The score cutoff where a model issues an alert.
  • Fairness metrics: Ways of measuring equity across groups (e.g., equal false positive rates).
  • Algorithmic Impact Assessment (AIA): Pre-deployment review of risks, harms, and safeguards.

Case Study A: Facial Recognition

Facial recognition is controversial due to bias and misuse risks. XAI can provide confidence scores, error distributions, and demographic breakdowns. For example, an arrest based on face-matching should come with an explanation showing the system’s confidence, comparison pool, and historical error rates across demographics.

Case Study B: Public-Space Anomaly Detection

Airports and cities use AI to detect anomalies like unattended bags or unusual movement patterns. XAI explanations (e.g., “flagged due to prolonged loitering” or “object left behind”) help operators respond appropriately. But detailed explanations must be carefully restricted to avoid adversaries exploiting the logic.

Case Study C: Predictive Policing

Predictive policing tools can reinforce existing biases if not carefully monitored. Explainability tools allow communities and regulators to inspect which variables influenced predictions (e.g., location-based arrest records vs. socioeconomic factors). Transparency here is vital for legitimacy—but also politically sensitive.

Comparison: Role-Based Transparency

StakeholderWhat They SeePurpose
CitizenNotices, rights, appeal channelsAwareness and accountability
OperatorReason codes for alertsActionable oversight
AuditorFull system metrics, bias auditsEnsure fairness and compliance
AdversaryNothing sensitivePrevent exploitation of the system

Governance Spotlight

Strong governance requires Algorithmic Impact Assessments, independent audits, and public transparency reports. A layered approach ensures that explanations improve accountability without undermining security.

Pause & Probe Questions

  1. How should transparency differ for citizens, operators, and auditors?
  2. What details should remain hidden to prevent adversaries from gaming the system?
  3. How often should fairness audits be conducted, and by whom?
  4. What effective redress processes should exist for those harmed by false positives?

© 2025 • XAI in Security and Surveillance • Part of the Industry Case Studies in XAI Series

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