New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in documenting data transparency, tracing authenticity, verifying consent, privacy, representation, bias, copyright infringement, and the overall development of ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models' limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.

For more than 50 years, Davos, the annual meeting of the World Economic Forum, has been an important barometer of economic, political, social, and environmental issues affecting the future of the world. So, what topics are driving the agenda for Davos 2022? The global supply chain crisis has taken on a new meaning. As the pandemic spread rapidly in 2020 and lingered in 2021, the general consensus was that disruptions to the global supply chain would be temporary albeit costly. But in 2022, it is clear that fragile supply chain may exist in a perpetual state of disruption for quite some time. In fact, the global supply chain was always in a fragile state; the pandemic laid bare just how vulnerable it was all along.

Two key components for using ML responsibly provide a prudent "start here" for organizations: model explainability and data transparency. The inability to explain why a model arrived at a particular result presents a level of risk in nearly every industry. In some areas, like healthcare, the stakes are particularly high when a model could be presenting a recommendation for patient care. In financial services, regulators may need to know why a lender is making a loan. Data transparency can ensure there is no unfair or unintended bias in the training data sets used to build the model, which can lead to disparate impact for protected classes – and consumers have what is increasingly a legally protected right to know how their data is being used.

Amid advances in precision medicine, healthcare is facing the twin challenges of having to curate and tailor the use of patient data to drive genomics-powered breakthroughs. That was the takeaway from the AI & data sciences track of last week's Precision Medicine World Conference in Santa Clara, California. "There aren't a lot of physicians saying, 'Bring me more AI,' " said John Mattison, MD, emeritus CMIO and assistant medical director of Kaiser Permanente. "Every physician is saying bring me a safer and more efficient way to deliver care." Mattison recalled his prolonged conversations with the original developers of IBM's Watson AI technology.

Many governments worldwide have begun to see the deployment of artificial intelligence as strategic importance for their country. Whereas in decades past, only a few developed nations spent any of their budgets on AI research and advancement, now it seems almost every country has invested in it. However, these countries differ on their basic approaches to privacy, data transparency and the connection between the economy and governmental oversight. Western countries operate on varying levels of government oversight over business operations, while China has a closer cooperation between government and business activities while being slow to regulate privacy and data transparency. The problem with regulating AI is that it is not a discrete technology but a collection of different technologies and patterns that use machine learning to achieve different objectives.