Tag : Data privacy

Increasing public trust in AI is a complex challenge that requires transparency, ethical development, accountability, data privacy, bias reduction, trustworthiness, public education, collaboration, and possible regulation. Trust must be earned and maintained through ongoing effort.

Sustainable AI means developing and using artificial intelligence in a way that minimizes its negative environmental, social and ethical impacts and maximizes its positive contributions to a sustainable future.

Privacy and Surveillance in the Age of AI explores how AI technologies collect, analyze, and use vast amounts of personal data, often at the expense of individual privacy. Key concerns include mass data collection, facial recognition, predictive analytics, and the risks of re-identification of anonymous data. AI-powered tools are widely used in public surveillance, targeted advertising, and workplace monitoring, exacerbating ethical issues such as bias, discrimination, and lack of transparency. Legal frameworks such as GDPR and CCPA offer some protections, but rapid technological advances often outpace regulations. Strong policies, ethical AI design, and public oversight are needed to strike a balance between reaping the benefits of AI and protecting privacy.

Create a Dashboard with More Options like table view of visualization data like Export Data, Remove Data, Maximize view.

Edge computing is a distributed computing pattern that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.