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Effective ethical data privacy management ups participation in AI projects
By Jacia Ebubechi  |  Jul 14, 2023
Effective ethical data privacy management ups participation in AI projects
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AI implementation has not kept pace with tech breakthroughs since procedures to ethically manage privacy during data collection and use are cumbersome and opaque. ML engineer and creative technical writer Jacia Ebubechi offers some pointers and encouragement for the AI hesitant.

PORT HARCOURT, NIGERIA - Artificial intelligence (AI) products have set a pace, and this change is rapidly gathering momentum. AI is the most sought-after innovation in today’s world since it has proven its superior ability to draw conclusions from vast amounts of data by connecting disparate dots and lines.

Data are the driving force behind the many AI projects that are regularly developed  to support individual projects, industrial developments, decisions, predictions, and projections. This sounds exciting in theory, but actual implementation still lags further than most people think: From where and how can these voluminous amounts data be gathered to enable the implementation of most AI projects?

In an ideal world, data collection for the myriad AI research and applications ought to originate from people, organizations, or industries. However, as AI projects and applications become more widespread, most AI industry specialists will begin to encounter ever more legal and ethical conundrums, meaning that the path forward will not always be straight forward.

Ways to participate in AI projects

Applications and initiatives utilizing valuable AI can undoubtedly aid society and help it achieve social progress. There has also been more emphasis recently on putting together resources to build AI applications that can enable the growth of individuals in a way that goes beyond just having a social impact. Also, information research by different sectors and organizations can benefit from the specific impact of AI technologies. Such benefits are already clear and can be scaled in terms of the following four aspects: automation, engagement, insights/decisions, and innovation.

As the saying goes, AI is only as good as the quality and quantity of the data available to it and on which it is trained. When looking at the future of AI

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