Bias, privacy, deepfakes in the age of Artificial Intelligence
as artificial intelligence becomes more deeply woven into everyday life—from hiring processes and banking to how we consume news—ethical concerns are no longer academic. They are urgent, real-world issues that demand public understanding and policy responses.
One of the most pressing issues is algorithmic bias. This occurs when AI systems unintentionally produce unfair outcomes due to flawed or biased training data. The AI Ethics Lab at Rutgers University notes that such systems can amplify existing social inequalities if not properly audited and designed. In the UK, AI tools used by local councils have been found to underrepresent women’s health concerns in official summaries—a reminder that AI doesn’t just reflect data; it shapes lives.
AI systems require enormous amounts of data to function. But who owns that data? And how is it being used?
In the Philippines, the National Privacy Commission (NPC) recently issued guidelines reminding developers that AI applications must comply with the Data Privacy Act. This includes transparency, accountability, and “privacy by design.” A 2024 report by KPMG Philippines also highlights risks to consumer data, especially in sectors like finance and healthcare, where AI is being rapidly deployed without robust safeguards.
AI now enables the creation of “deepfakes”—hyper-realistic but fake audio, video, and images. These are already being used to mislead the public, create fake endorsements, or even impersonate public officials. The Department of National Defense in the Philippines has gone as far as banning the use of AI-generated photo apps among its personnel due to identity theft and security concerns.
Globally, researchers warn that the ease of creating misinformation could erode public trust. A paper from ScienceDirect warns that AI, if left unchecked, could lead to what they call “truth decay”—where manipulated content is indistinguishable from real evidence.
Experts agree that legal frameworks and ethical standards need to catch up. Bias audits, clearer regulation, public awareness campaigns, and better training for developers are all part of the solution. More importantly, consumers must ask harder questions: Is this system fair? Is it transparent? Can I opt out?
In the era of AI, ethics isn’t optional—it’s foundational.
