Assessment of uncertainty in traditional risk assessment is relatively well-established, and though it is applied to varying extents, it is recognized as a critical component in decision-making. Guidance for the conduct of uncertainty generally involves identification of the types of uncertainty (e.g., standard and non-standard; limitations in data and models, assumptions, etc.) and characterization using qualitative and quantitative techniques. In theory, these aspects readily translate to assessment of uncertainty in next generation risk assessment (NGRA), an exposure-driven approach utilizing non-animal methods to assess safety. To date, available guidance does not specifically address uncertainty in NGRA, and few have attempted to operationalize such. The objective herein is two-fold: first, to advance quantitative uncertainty assessment in NGRA by matching potential quantitative methods to each source of uncertainty based on the type of data and/or step in NGRA, and second, to assess various quantitative techniques to assess overall uncertainty. Test method uncertainty, for example, as a measure of intrinsic uncertainty, can be quantitatively characterized via statistical uncertainties within and across in vitro bioassays. Distributions can be aggregated within and across tiers – as appropriate – pending the levels of targeted-bioassays needed within an NGRA.  Model uncertainties are similarly subject to Bayesian approaches and can include assessment of distributional uncertainties in the in vitro to in vivo pharmacokinetic parameters. These techniques aid in quantitatively characterizing uncertainty around bioactivity:exposure ratios (and inputs leading to this step). Quantitative assessment of knowledge, mechanistic understanding, and predictivity, however, presents larger challenges; currently, semi-quantitative and/or machine-learning approaches are being considered as possible tools to combine with the use of defined approaches and AOPs (or lack thereof) to quantitatively assess uncertainty for these aspects. The resulting combination of considering the specific needs for each respective quantitative method generated in this research will facilitate further discussions within the community as to operationalize quantitative uncertainty assessment in NGRA. It is anticipated that the utility and feasibility of propagating the probabilistic information through each step of the NGRA will be best assessed through various case studies involving high- and low-volume datasets, human and ecological endpoints, defined and undefined toxicity pathways, as well as comparison of traditional and NGRA uncertainty inputs, outputs and metrics in context of that needed by decision-makers to have confidence based on quantitative uncertainty findings.