Archive for review

the privacy fallacy [book review]

Posted in Statistics with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on May 3, 2024 by xi'an

“The World changed significantly since 1973.” (p.10)

I read this book, The Privacy Fallacy: Harm and Power in the Information Economy, by Ignacio Cofone, upon my return from Warwick the past week. This is a Cambridge University Press 2023 book I had picked from their publication list after reviewing a book proposal for them. A selection made with our ERC OCEAN goals in mind, but without paying enough attention to the book table of contents, since it proved to be a Law book!

“People’s inability to assess privacy risks impact people’s behavior toward privacy because it turns the risks into uncertainty, a kind of risk that is impossible to estimate.” (p.31)

Still, this ended up being a fairly interesting read (for me) about the shortcomings of the current legal privacy laws (in various countries), since they are based on an obsolete perception that predates AIs and social media. Its main theme is that privacy is a social value that must be protected, regardless of whether or not its breach has tangible consequences. The author then argues that notions that support these laws such as the rationality of individual choices, the confusion between privacy and secrecy, the binary dichotomy between public and private, &tc., all are erroneous, hence the “fallacy” he denounces. One immediate argument for his position is the extreme imbalance of information between individuals and corporations, the former being unable to assess the whole impact of clicking on “I agree” when visiting a webpage or installing a new app. The more because the data thus gathered is pipelined to third parties. (“One’s efforts cannot scale to the number of corporations collecting and using one’s personal data”, p.93) For similar reasons, Cofone further states that the current principles based on contracts are inappropriate. Also because data harm can be collective and because companies have a strong incentive to data exploitation, hence a moral hazard.

“Inferences, relational data, and de-identified data aren’t captured by consent provisions.” (p.9)

“AI inferences worsen information overload (…) As [they] continue to grow, so will the insufficiency of our processing ability to estimate our losses.” (p.75)

As illustrated by the surrounding quotes, the statistical and machine-learning aspects of the book are few and vague, in that the additional level of privacy loss due to post-data processing is considered as a further argument for said loss to be impossible to quantify and assess, without a proper evaluation of the channels through which this can happen and without a reglementary proposal towards its control. This level of discourse makes AIs appear as omniscient methods, unfortunately.

“Inferences are invisible (…) Risks posed by inferences are impossible to anticipate because the information inferred is disproportionate to the sum of the information disclosed.” (p.49)

“The idea of probabilistic privacy loss is crucial in a world where entities (..) mostly affect our privacy by making inferences” (p.121)

The attempts at regulation such as opt-in and informed consent are then denounced as illusions—obviously so imho, even without considering the nuisance of having to click on “Reject” for each newly visited website!—. De- and re-identified data does not require anyone’s consent. Data protection rights, as of today, do not provide protection in most cases, the burden of proof residing on the privacy victims rather than the perpetrators. The book unsurprisingly offers no technical suggestion towards ensuring corporations and data brokers comply with this respect of privacy and on the opposite agrees that institutional attempts such as GDPR remain well-intended wishful thinking w/o imposing a hard-wired way of controlling the data flows, with the “need of an enforcement authority with investigating and sanctioning powers” (p.106) . The only in-depth proposal therein is pushing for stronger accountability of these corporations via a new type of liability, with a prospect of class actions (if only in countries with this judiciary possibility).

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE.]

an introduction to MCMC sampling

Posted in Books, Kids, Statistics with tags , , , , , , , , , on August 9, 2022 by xi'an

Following a rather clueless question on X validated, I had a quick read of A simple introduction to Markov Chain Monte–Carlo sampling, by Ravenzwaaij, Cassey, and Brown, published in 2018 in Psychonomic Bulletin & Review, which I had never opened to this day. The setting is very basic and the authors at pain to make their explanations as simple as possible, but I find the effort somehow backfires under the excess of details. And the characteristic avoidance of mathematical symbols and formulae. For instance, in the Normal mean example that is used as introductory illustration and that confused the question originator, there is no explanation for the posterior being a N(100,15) distribution, 100 being the sample average, the notation N(μ|x,σ) is used for the posterior density, and then the Metropolis comparison brings an added layer of confusion:

“Since the target distribution is normal with mean 100 (the value of the single observation) and standard deviation 15,  this means comparing N(100|108, 15) against N(100|110, 15).”

as it most unfortunately exchanges the positions of  μ and x (which is equal to 100). There is no fundamental error there, due to the symmetry of the Normal density, but this switch from posterior to likelihood certainly contributes to the confusion of the QO. Similarly for the Metropolis step description:

“If the new proposal has a lower posterior value than the most recent sample, then randomly choose to accept or
reject the new proposal, with a probability equal to the height of both posterior values. “

And the shortcomings of MCMC may prove equally difficult to ingest: like
“The method will “work” (i.e., the sampling distribution will truly be the target distribution) as long as certain conditions are met.
Firstly, the likelihood values calculated (…) to accept or reject the new proposal must accurately reflect the density of the proposal in the target distribution. When MCMC is applied to Bayesian inference, this means that the values calculated must be posterior likelihoods, or at least be proportional to the posterior likelihood (i.e., the ratio of the likelihoods calculated relative to one another must be correct).”

which leaves me uncertain as to what the authors do mean by the alternative situation, i.e., by the proposed value not reflecting the proposal density. Again, the reluctance in using (more) formulae hurts the intended pedagogical explanations.

computing Bayes 2.0

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on December 11, 2020 by xi'an

Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)

Rao-Blackwellisation, a review in the making

Posted in Statistics with tags , , , , , , , , , , on March 17, 2020 by xi'an

Recently, I have been contacted by a mainstream statistics journal to write a review of Rao-Blackwellisation techniques in computational statistics, in connection with an issue celebrating C.R. Rao’s 100th birthday. As many many techniques can be interpreted as weak forms of Rao-Blackwellisation, as e.g. all auxiliary variable approaches, I am clearly facing an abundance of riches and would thus welcome suggestions from Og’s readers on the major advances in Monte Carlo methods that can be connected with the Rao-Blackwell-Kolmogorov theorem. (On the personal and anecdotal side, I only met C.R. Rao once, in 1988, when he came for a seminar at Purdue University where I was spending the year.)

open reviews

Posted in Statistics with tags , , , , , , on September 13, 2019 by xi'an

When looking at a question on X validated, on the expected Metropolis-Hastings ratio being one (not all the time!), I was somewhat bemused at the OP linking to an anonymised paper under review for ICLR, as I thought this was breaching standard confidentiality rules for reviews. Digging a wee bit deeper, I realised this was a paper from the previous ICLR conference, already published both on arXiv and in the 2018 conference proceedings, and that ICLR was actually resorting to an open review policy where both papers and reviews were available and even better where anyone could comment on the paper while it was under review. And after. Which I think is a great idea, the worst possible situation being a poor paper remaining un-discussed. While I am not a big fan of the brutalist approach of many machine-learning conferences, where the restrictive format of both submissions and reviews is essentially preventing in-depth reviews, this feature should be added to statistics journal webpages (until PCIs become the norm).