Healthcare Example:
AI diagnostic tools (e.g., IBM Watson) help doctors identify illnesses faster, improving patient outcomes. However, over-reliance on AI may lead to reduced human judgment or patient trust in automated diagnoses.

Self-Driving Cars Example:
Autonomous vehicle systems like Waymo need to interpret unpredictable human behaviour (e.g., jaywalkers or confused drivers). Users must trust the car to make safe decisions, which is a major barrier to adoption.

Healthcare Example:
AI uses personal data for diagnosis and treatment planning. Ethical concerns include patient consent, data security, and algorithmic bias—e.g., AI trained mostly on male or white patients may underperform for others.

Self-Driving Cars Example:
In crash scenarios, who should the AI prioritise—passengers or pedestrians? These moral dilemmas challenge developers and lawmakers.

🔗The 7 Most Pressing Ethical Issues in Artificial Intelligence

Healthcare Example:
AI can reduce strain on public health systems, helping underserved communities. However, if systems are only available in wealthy hospitals, inequity may grow.

Self-Driving Cars Example:
AI may reduce road deaths but could also cause mass job losses in trucking and taxi industries. This disrupts livelihoods and raises social justice questions.

🔗Positive impact on society

Healthcare Example:
Future-proof AI must update medical databases, comply with evolving data laws, and support a diverse range of conditions and patients. It should adapt to new diseases or treatments.

Self-Driving Cars Example:
Systems must be regularly updated with new road laws, handle different geographic driving styles, and resist hacking attempts. Long-term success depends on regulatory alignment and public trust.

Healthcare Example:
AI can optimise hospital energy usage or reduce unnecessary medical tests, supporting eco-efficiency. But large AI models (e.g., for image recognition) use significant energy, raising concerns.

Self-Driving Cars Example:
Self-driving cars could reduce emissions by avoiding traffic and choosing fuel-efficient routes. However, building and running these systems involves high energy costs, especially during development and testing.