We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables, is identified up to permutation and scaling of the columns. We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems. Consequently, any identifiability result based on the mixing matrix for one model translates to an identifiability result for the other model. We characterize to what extent the causal models can be identified under a two-part faithfulness assumption. Under only the first part of the assumption (corresponding to the conventional definition of faithfulness), the structure can be learned up to the causal ordering among an ordered grouping of the variables but not all the edges across the groups can be identified. We further show that if both parts of the faithfulness assumption are imposed, the structure can be learned up to a more refined ordered grouping. As a result of this refinement, for the latent variable model with unobserved parentless causes, the structure can be identified. Based on our theoretical results, we propose causal structure learning methods for both models, and evaluate their performance on synthetic data.

Our second main contribution is to characterize the extent of identifiability of the causal model under our faithfulness assumption.

Privacy watchdogs in Europe are considering a complaint against Apple made by a former employee, Ashley Gjøvik, who alleges the company fired her after she raised a number of concerns, internally and publicly, including over the safety of the workplace. Gjøvik, a former senior engineering program manager at Apple, was fired from the company last September after she had also raised concerns about her employer's approach towards staff privacy, some of which were covered by the Verge in a report in August 2021. At the time, Gjøvik had been placed on administrative leave by Apple after raising concerns about sexism in the workplace, and a hostile and unsafe working environment which it had said it was investigating. She subsequently filed complaints against Apple with the US National Labor Relations Board. Those earlier complaints link to the privacy complaint she's sent to international oversight bodies now because Gjøvik says she wants scrutiny of Apple's privacy practices after it formally told the US government its reasons for firing her -- and "felt comfortable admitting they'd fire employees for protesting invasions of privacy", as she puts it -- accusing Apple of using her concerns over its approach to staff privacy as a pretext to terminate her for reporting wider safety concerns and organizing with other employees about labor concerns. A spokesperson for the ICO told TechCrunch: "We are aware of this matter and we will assess the information provided."

Temperatures in the Arctic have been rising so fast in recent decades they have confused a computer designed to measure them. Scientists monitoring a site in Alaska have found that an algorithm at the weather station, which has been recording temperatures for nearly 100 years, deleted all of its data from 2017, and even some from 2016. In what the experts are now calling an'ironic exclamation point' to rapid climate change, the algorithm flagged the abnormal temperatures observed at the station, as it assumed they were too high to be accurate. When scientists set out at the beginning of December to review the previous month's climate data, they noticed something'odd': everything from Utqiaġvik, Alaska was missing. The data from 2017 and some of 2016 had been flagged as artificial.

This is the first, non-technical, part of this series. See the second part for more detail. I was recently looking for a good machine learning task to try out, and I thought that doing something NFL-related would be interesting, because the NFL season is about to start (finally!). Why was I looking for a good machine learning task to try out? I have mostly done my data analysis work in R, but recently, I have been moving over to Python.