graph of the day & AI4good versus AI4bad

Apart from the above graph from Nature, rendering in a most appalling and meaningless way the uncertainty about the number of active genes in the human genome, I read a couple of articles in this issue of Nature relating to the biases and dangers of societal algorithms. One of which sounded very close to the editorial in the New York Times on which Kristian Lum commented on this blog. With the attached snippet on what is fair and unfair (or not).

The second article was more surprising as it defended the use of algorithms for more democracy. Nothing less. Written by Wendy Tam Cho, professor of political sciences, law, statistics, and mathematics at UIUC, it argued that the software that she develops to construct electoral maps produces fair maps. Which sounds over-rosy imho, as aiming to account for all social, ethnic, income, &tc., groups, i.e., most of the axes that define a human, is meaningless, if only because the structure of these groups is not frozen in time. To state that “computers are impervious to the lure of power” is borderline ridiculous, as computers and algorithms are [so far] driven by humans. This is not to say that gerrymandering should not be fought by technological means, especially and obviously by open source algorithms, as existing proposals (discussed here) demonstrate, but to entertain the notion of a perfectly representative redistricting is not only illusory, but also far from democratic as it shies away from the one person one vote  at the basis of democracy. And the paper leaves us on the dark as to whom will decide on which group or which characteristic need be represented in the votes. Of course, this is the impression obtained by reading a one page editorial in Nature [in an overcrowded and sweltering commuter train] rather than the relevant literature. Nonetheless, I remain puzzled at why this editorial was ever published. (Speaking of democracy, the issue contains also warning reports about Hungary’s ultra-right government taking over the Hungarian Academy of Sciences.)

scientific societies start to address sexual harassement

As ISBA releases a letter of her president to the members about the decision by the ISBA Board [taken in Edinburgh] to exclude three of its members following multiple complaints of harassment, the ASA publishes an update on the activities of the task force created to address this issue last November. And Nature reports on the report published by the US academies of Sciences, Engineering, and Medicine, which points out the limited impact of the current policies and mechanisms at play in US institutions.

“The analysis concludes that policies to fight the problem are ineffective because they are set up to protect institutions, not victims.” Nature, June 12, 2018

A common feature between the ASA and the Academy approaches is to rely on a survey of their respective members, soon to come for ASA members. Another feature of major relevance is the issue of anonymous reporting and counselling. So that victims and witnesses of harassment can trust the procedure strongly enough to report  a case without being afraid of being known to a large number of people. In my opinion, having identified individuals that represent the diversity of a scientific society such as ISBA, rather than an anonymous email account or a web form, is more likely to induce testimonies or complaints.

Nature snippets

Besides this remarkable picture of a fox and an eagle fighting for a rabbit, posted in Nature of 7 June, I noticed [in Nature 24 May] an editorial by Richard McEalreath, author of the remarkable Statistical Rethinking, about a paper by González-Forero & Gardner developing a model for brain vs body growth, incorporating social and ecological challenges. The goal was to fit the actual growth in body mass and brain mass. As in the one below.Without reading the supplementary material, I cannot tell how much statistics was involved in preventing the “best fit” to turn to overfitting. But Richard McEalreath points out that this modelling goes away and presumably beyond the “purely statistical”, including regression approaches, without elaborating more on the methodological aspects.

rage against the [Nature] Machine [Intelligence]

Yesterday evening, my friend and colleague Pierre Alquier (CREST-ENSAE) got interviewed (for a few seconds on-line!, around minute 06) by the French national radio, France Culture, about the recent call to boycott the incoming Nature Machine Intelligence electronic journal. Call to the machine learning community, based on the lack of paying journals among the major machine learnings journals, like JMLR. Meaning that related conferences like AISTATS and NIPS also get their accepted papers available on-line for free. As noted in the call

“Machine learning has been at the forefront of the movement for free and open access to research. For example, in 2001 the Editorial Board of the Machine Learning Journal resigned en masse to form a new zero-cost open access journal, the Journal of Machine Learning Research (JMLR).”

certified randomness, 187m away…

As it rarely happens with Nature, I just read an article that directly relates to my research interests, about a secure physical random number generator (RNG). By Peter Bierhost and co-authors, mostly physicists apparently. Security here means that the outcome of the RNG is unpredictable. This very peculiar RNG is based on two correlated photons sent to two measuring stations, separated by at least 187m, which have to display unpredictable outcomes in order to respect the impossibility of faster-than-light communications, otherwise known as Bell inequalities. This is hardly practical though, especially when mentioning that the authors managed to produce 2¹⁰ random bits over 10 minutes, post processing “the measurement of 55 million photon pairs”. (I however fail to see why the two-arm apparatus would be needed for regular random generation as it seems relevant solely for the demonstration of randomness.) I also checked the associated supplementary material, which is mostly about proving some total variation bound, and constructing a Bell function. What is most puzzling in this paper (and the associated supplementary material) is the (apparent) lack of guarantee of uniformity of the RNG. For instance, a sentence (Supplementary Material, p.11) about  a distribution being “within TV distance of uniform” hints at the method being not provably uniform, which makes the whole exercise incomprehensible…

Nature snapshots

In this 15 March issue of Nature, a rather puzzling article on altruism that seemed to rely on the Hamilton rule:

linking fitness and number of offspring and benefits through kth-order moments. In a stochastic environment represented by distribution π. Hard to fathom what comes from data and what follows from this (hypothetical) model. Plus a proposal for geoengineering meltdown delays on some Greenland and Antarctica glaciers. Scary. And a film review of Iceman (Der Mann aus dem Eis), retracing Ötzi’s life in an archaeologically acceptable rendering. Including a reconstituted local language, Rhaetic.

gender gaps

Two of my colleagues [and co-authors] at Dauphine, Elyès Jouini and Clotilde Napp, published a paper in Science last week (and an associated tribune in Le Monde which I spotted first) about explaining differences in national gender inequalities in maths (as measured by PISA) in terms of the degree of overall inequality in the respective countries. Gaps in the highest maths performer sex ratio. While I have no qualm about the dependency or the overall statistical cum machine learning analysis (supported by our common co-author Jean-Michel Marin), and while I obviously know nothing about the topic!, I leisurely wonder at the cultural factor (which may also partly explain for the degree of inequality) when considering that the countries at the bottom of the above graphs are rather religious (and mostly catholic). I also find it most intriguing that the gender gap is consistently reversed when considering higher performer sex ratio for reading, because mastering the language should be a strong factor in power structures and hence differences therein should also lead to inequalities…