Below, we address the main points raised by the Reviewers. "I'm not sure how well this is aligned with the goal of the paper" "I would have liked [...] evaluation of uncertainty calibration" "The paper does not compare with [...] deep ensembles [...] non-Bayesian "Why did you use SGHMC instead of full HMC?" But for this simple example we don't expect it to make big difference (the R-hat statistic showed its convergence). We will add some traces and the setup in the supplement. "Why do you assume a fully factorized Gaussian posterior Our parameterization in case of output dimension 1 will be equivalent to mean field.

Events over the past few years have revealed several human rights violations associated with increasing advances in artificial intelligence (AI). Algorithms created to regulate speech online have censored speech ranging from religious content to sexual diversity. AI systems created to monitor illegal activities have been used to track and target human rights defenders. And algorithms have discriminated against Black people when they have been used to detect cancers or assess the flight risk of people accused of crimes. As researchers studying the intersection between AI and social justice, we've been examining solutions developed to tackle AI's inequities.

AI and automation are receiving a boost during the coronavirus pandemic that in the short term is creating a new hybrid workforce rather than destroying jobs outright. The big picture: While the forces of automation and AI will eliminate some jobs and create some new ones, the vast majority will remain but be dramatically changed. The challenge for employers will be ensuring workforces are ready for the effects of technology.