AI in Practice: What Can Go Wrong?

Introduction

As AI systems move from lab environments to real-world applications, new kinds of ethical and practical problems emerge.

Why does this matter?

AI systems may work well in testing but fail unpredictably in the real world.
Ethical risks are often hidden in data, deployment context, or incentives.
Failures can scale quickly and impact real lives: discrimination, safety issues, loss of trust.
Understanding how and why AI goes wrong is essential to preventing future harm.

Type of Issue	Description	Example
Bias and Discrimination	Biased training data leads to biased outputs. Replicates and reinforces social inequalities	Hiring tools, predictive policing
Lack of Transparency	Users don’t understand or cannot challenge AI decisions. Systems act as “black boxes”	Credit scoring, medical diagnosis
Overreliance / Automation Bias	People trust AI even when it is clearly wrong or misaligned with context	GPS directions, autopilot
Function Creep	AI used for one purpose expands silently into others (e.g., law enforcement use of commercial data)	Smart speakers, surveillance
Data Privacy Violations	Personal data collected without proper consent. Data being reused or sold	Smart homes, mental health apps
Unsafe Deployment	AI deployed in real-world contexts without sufficient testing or safeguards	Self-driving cars, robotic surgery
Feedback Loops	System learns from its own outputs, reinforcing narrow behavior (e.g., filter bubbles)	Recommender systems, ad targeting

Biased Training Data	Data reflects past human biases (e.g., racist policing records, gendered job roles)
Lack of Contextual Testing	Systems tested in narrow environments don’t generalize (e.g., from private roads to city streets)
Misaligned Objectives	Optimizing for engagement, clicks, or efficiency can ignore fairness or well-being
No Human Oversight	Systems make decisions without accountability mechanisms or intervention
Incentive Misalignment	Companies optimize for speed or profit, not ethics or safety
Lack of Regulation or Standards	No legal limits on harmful deployment or poor design

Risk Category	Description	Real-Worls Example
Epistemic risk	The system is wrong or misleading	Self-driving car misclassifies child as inanimate object
Moral risk	The system does harm or violates values	Hiring AI favors privileged groups
Political risk	The system shifts power, often unequally	Surveillance AI targets protesters. Data sold to authoritarian regimes
Social risk	The system changes relationships or behaviors	Recommender AI increases polarization and echo chambers
Legal risk	The system conflicts with the law or lacks legal clarity	Chatbot offers health advice that violates medical regulations

Preventive Action	Goal
Bias auditing	Detect and address fairness issues in data and model outcomes
Explainability features	Help users understand and contest AI decisions
Ethical impact assessments	Identify potential harms and affected groups before deployment
Stakeholder inclusion	Bring real-world users into design discussions
Transparency reports	Show how AI is trained, tested, and used
Independent testing	Test systems in realistic, high-variance environments
Fail-safe design	Build human override, logging, and rollback capabilities