ETHICAL CHALLENGES AND GOVERNANCE FRAMEWORKS IN BIG DATA ANALYTICS: ADDRESSING PRIVACY, CONSENT, BIAS, TRANSPARENCY, AND ACCOUNTABILITY IN THE ERA OF AI-DRIVEN DECISION MAKING

Abstract

Using lots of data to figure things out is now normal in almost every area, like hospitals, banks, government work, and businesses, helping them see patterns, guess what might happen, and make smarter choices in a big way. Even though these skills boost success and new ideas, they also create some tricky moral problems that still need fixing. Gathering and handling huge amounts of data pushes the limits of people's private information, because trying to hide who the data belongs to often doesn't work, and getting real permission becomes weak agreements that don't give people much actual control. Ongoing unfairness in computer programs is still a big issue, since these systems can accidentally keep old inequalities going in things like hiring, policing, or loan decisions, while complex models make it hard to understand and see who is responsible. More problems come up with who owns data, how safely it's kept, and what companies and governments should do about it. This writing looks into these problems using real examples and rules like the General Data Protection Regulation and the California Consumer Privacy Act, checks out tech solutions like designing for privacy and making AI easy to understand, and argues that good control will need stronger laws and tools, along with always focusing on fairness, accountability, and protecting basic rights.