Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks Numerous proposals, prototypes, and models have emerged for using artificial intelligence (AI) and machine learning to predict individual risk related to covid-19. In the United States, for instance, the Department of Veterans Affairs uses individualised risk scores to allocate medical resources to people with covid-19,1 and prisons have sought to detect symptoms by processing inmates’ phone calls.2 Further tools, such as vulnerability predictions for individuals3 and voice based detection of infection,4 are on the horizon. But use of AI for such purposes has given rise to questions about legality. When a state or federal government seeks to use AI models to predict an individual’s risk of covid-19, the key legal questions will ultimately turn on how effective the models are and how much they burden legal interests. We focus on two of the most salient legal concerns under US law: privacy and discrimination. Challenges on privacy or discrimination grounds might appear in a variety of contexts, including challenges to regulatory decisions, tort actions, or lawsuits under health privacy laws. We argue that the basic need to balance benefits against burdens runs through all of these legal regimes. Governments implementing risk scoring tools must show that their tools produce valid, reliable predictions and burden individuals’ civil liberties no more than necessary. In evaluating the legality of public health use of algorithms, courts will likely also probe how the output of these tools is used to shape policies and programs. But showing that a model performs well and does not exceedingly burden privacy and other interests are essential preconditions for lawful deployment. ### Privacy law Government intrudes on privacy when it forces people to reveal what …

AI is undoubtedly changing the healthcare industry, making it more efficient and driving better outcomes for patients. COVID-19 has served as an accelerator of adoption – a catalyst in helping the industry catapult itself forward, taking advantage of the best technology has to offer. Barriers to adoption persist, however, as many applications of AI in healthcare remain uncharted territory. The vast majority of the world's health systems are not using their data and AI to make helpful predictions that inform decision making, creating tremendous opportunity to use data and AI to help make more insightful healthcare decisions. But the challenge is in finding common, replicable use cases. To start, healthcare providers are looking to understand how the disparate clinical data they gather can be organised better into an efficient pipeline that can be used to tap into accurate, predictive data intelligence.

Machine learning is automating the more manually-based, routine incident analysis, and escalation tasks that are overwhelming security analysts today. Capitalizing on supervised machine learnings' innate ability to fine-tune algorythms in milliseconds based on the analysis of incidence data, endpoint security providers are prioritizing this area in product developnent. Demand from potential customers remains strong, as nearly everyone is facing a cybersecurity skills shortage while facing an onslaught of breach attempts. "The cybersecurity skills shortage has been growing for some time, and so have the number and complexity of attacks; using machine learning to augment the few available skilled people can help ease this. What's exciting about the state of the industry right now is that recent advances in Machine Learning methods are poised to make their way into deployable products," Absolute's CTO Nicko van Someren added.

Machine learning is automating the more manually-based, routine incident analysis, and escalation tasks that are overwhelming security analysts today. Capitalizing on supervised machine learnings' innate ability to fine-tune algorythms in milliseconds based on the analysis of incidence data, endpoint security providers are prioritizing this area in product developnent. Demand from potential customers remains strong, as nearly everyone is facing a cybersecurity skills shortage while facing an onslaught of breach attempts. "The cybersecurity skills shortage has been growing for some time, and so have the number and complexity of attacks; using machine learning to augment the few available skilled people can help ease this. What's exciting about the state of the industry right now is that recent advances in Machine Learning methods are poised to make their way into deployable products," Absolute's CTO Nicko van Someren added.

Amazon's digital assistant could soon do more than just turn on your lights or tell you the weather. The e-commerce giant has signaled a major leap into healthcare for Alexa, by rolling out an invite-only program for developers to create their own skills around health and medicine. It would allow users to ask Alexa to book a doctor's appointment, find an urgent care center and check for updates on prescription refills. Amazon's digital assistant could soon do more than just turn on your lights or tell you the weather. Amazon, which launched the program on Thursday, said the skills are all compliant with the federal Health Insurance Portability and Accountability Act, which ensures that personal health care information is protected. The firm told Wired that it has added extra security levels to how it treats the data collected through these skills, beyond the encryption, access controls and secure cloud storage it deploys for other skill data.