As rich biomedical data streams are accumulating across people and time, they provide a powerful opportunity to address limitations in our existing scientific knowledge and to overcome operational challenges in healthcare and life sciences. Yet the relative weighting of insights vs. methodologies in our current research ecosystem tends to skew the computational community away from algorithm evaluation and operationalization, resulting in a well-reported trend towards the proliferation of scientific outcomes of unknown reliability. Algorithm selection and use is hindered by several problems that persist across our field. One is the impact of the self-assessment bias, which can lead to mis-representations in the accuracy of research results. A second challenge is the impact of data context on algorithm performance. Biology and medicine are dynamic and heterogeneous. Data is collected under varying conditions. For algorithms, this means that performance is not universal - and need to be evaluated across a range of contexts. These issues are increasingly difficult as algorithms are trained and used on data collected in the real-world, outside of the traditional clinical research lab. In these cases, data collection is neither supervised nor well controlled and data access may be limited by privacy or proprietary reasons. Therefore, there is a risk that algorithms will be applied to data that are outside of the scope of the intent of the original training data provided. This workshop will focus on approaches that are emerging across the researcher community to quantify the accuracy of algorithms and the reliability of their outputs.