An AI-based computer system can gather data and use that data to make decisions or solve problems – using algorithms to perform tasks that, if done by a human, would be said to require intelligence. The benefits created by AI and machine learning (ML) systems for better health care, safer transportation, and greater efficiencies across the globe are already happening. But the increased amounts of data and computing power that enable sophisticated AI and ML models raise questions about the privacy impacts, ethical consequences, fairness, and real world harms if the systems are not designed and managed responsibly. FPF works with commercial, academic, and civil society supporters and partners to develop best practices for managing risk in AI and ML and assess whether historical data protection practices such as fairness, accountability, and transparency are sufficient to answer the ethical questions they raise.
Authors: Stephanie Wong, Amber Ezzell, & Felicity Slater As an increasing number of organizations utilize artificial intelligence (“AI”) in their patient-facing services, health organizations are seizing the opportunity to take advantage of the new wave of AI-powered tools. Policymakers, from United States (“U.S.”) government agencies to the White House, have taken heed of this trend, […]
This post is the second in a four-part series on Brain-Computer Interfaces (BCIs), providing an overview of the technology, use cases, privacy risks, and proposed recommendations for promoting privacy and mitigating risks associated with BCIs. Click here for FPF and IBM’s full report: Privacy and the Connected Mind. In case you missed it, read the […]
BCIs are computer-based systems that directly record, process, analyze, or modulate human brain activity in the form of neurodata that is then translated into an output command from human to machine. Neurodata is data generated by the nervous system, composed of the electrical activities between neurons or proxies of this activity. When neurodata is linked, or reasonably linkable, to an individual, it is personal neurodata.
By Brenda Leong and Dr. Sara Jordan Machine learning-based technologies are playing a substantial role in the response to the COVID-19 pandemic. Experts are using machine learning to study the virus, test potential treatments, diagnose individuals, analyze the public health impacts, and more. Below, we describe some of the leading efforts and identify data protection […]