Hippocratic oath for maths?

On a free day in Nachi-Taksuura, I came across this call for a professional oath for mathematicians (and computer engineers and scientists in related fields). By UCL mathematician Hannah Fry. The theme is the same as with Weapons of math destruction, namely that algorithms have a potentially huge impact on everyone’s life and that those who design these algorithms should be accountable for it. And aware of the consequences when used by non-specialists. As illustrated by preventive justice software. And child abuse prediction software. Some form of ethics course should indeed appear in data science programs, for at least pointing out the limitations of automated decision making. However, I remain skeptical of the idea as (a) taking an oath does not mean an impossibility to breaking that oath, especially when one is blissfully unaware of breaking it (b) acting as ethically as possible should be part of everyone’s job, whether when designing deep learning algorithms or making soba noodles (c) the Hippocratic oath is mostly a moral statement that varies from place to place and from an epoch to the next (as, e.g., with respect to abortion which was prohibited in Hippocrates’ version) and does not prevent some doctors from engaging into unsavory activities. Or getting influenced by dug companies. And such an oath would not force companies to open-source their code, which in my opinion is a better way towards the assessment of such algorithms. The article does not mention either the Montréal Déclaration for a responsible AI, which goes further than a generic and most likely ineffective oath.

weapons of math destruction [fan]

As a [new] member of Parliement, Cédric Villani is now in charge of a committee on artificial intelligence, which goal is to assess the positive and negative sides of AI. And refers in Le Monde interview below to Weapons of Maths Destruction as impacting his views on the topic! Let us hope Superintelligence is no next on his reading list…

the end of statistics [not!]

Last week I spotted this tribune in The Guardian, with the witty title of statistics loosing its power, and sort of over-reacted by trying to gather enough momentum from colleagues towards writing a counter-column. After a few days of decantation and a few more readings (reads?) of the tribune, I cooled down towards a more lenient perspective, even though I still dislike the [catastrophic and journalistic] title. The paper is actually mostly right (!), from its historical recap of the evolution of (official) statistics across centuries, to the different nature of the “big data” statistics. (The author is “William Davies, a sociologist and political economist. His books include The Limits of Neoliberalism and The Happiness Industry.”)

“Despite these criticisms, the aspiration to depict a society in its entirety, and to do so in an objective fashion, has meant that various progressive ideals have been attached to statistics.”

A central point is that public opinion has less confidence in (official) statistics than it used to be. (warning: Major understatement, here!) For many reasons, from numbers used to support any argument and its opposite, to statistics (-ians) being associated with experts, found at every corner of news and medias, hence with the “elite” arch-enemy, to a growing innumeracy of both the general public and of the said “elites”—like this “expert” in a debate about the 15th anniversary of the Euro currency on the French NPR last week equating a raise from 2.4 Francs to 6.5 Francs to 700%…—favouring rhetoric over facts, to a disintegration of the social structure that elevates one’s community over others and dismisses arguments from those others, especially those addressed at the entire society. The current debate—and the very fact there can even be a debate about it!—about post-truths and alternative facts is a sad illustration of this regression in the public discourse. The overall perspective in the tribune is one of a sociologist on statistics, but nothing to strongly object to.

“These data analysts are often physicists or mathematicians, whose skills are not developed for the study of society at all.”

The second part of the paper is about the perceived shift from (official) statistics to another and much more dangerous type of data analysis. Which is not a new view on the field, as shown by Weapons of Math Destruction. I tend to disagree with this perception that data handled by private companies for private purposes is inherently evil. The reticence in trusting the conclusions drawn from such datasets also extends to publicly available datasets and is not primarily linked to the lack of reproducibility of such analyses (which would be a perfectly rational argument!). It is neither due to physicists or mathematicians running those, instead of quantitative sociologists! The roots of the mistrust are rather to be found in an anti-scientism that has been growing in the past decades, in a paradox of an equally growing technological society fuelled by scientific advances. Hence, calling for a governmental office of big data or some similar institution is very much unlikely to solve the issue. I do not know what could, actually, but continuing to develop better statistical methodology cannot hurt!

weapons of math destruction [book review]

As I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

“Models are opinions embedded in mathematics.” (p.21)

The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame mathematics for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of mathematics throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.)