Let’s talk about who said what when, and priority dates, then

Rajeev Srinivasan

As a columnist in the Indian media for over twenty years, I have had several of my ideas copied without attribution by others, and I have always looked at this with mild amusement. If you put things out there in the public domain, there is always the chance that this will happen, and it may not even be such a bad thing. This is how ideas propagate, and all of us stand on the shoulders of others whose works we have read and unconsciously internalized.

Thus I was not particularly surprised by a column on wsj.com by Sadanand Dhume, titled “India’s Incredibly Shrunken Presidency” https://www.wsj.com/articles/indias-incredibly-shrunken-presidency-1500573655 . Several points made by Dhume I agreed with, and the structure of the piece appealed. It bemoaned the fact that non-politicians had very few chances to become President of India, and named a few professionals who would be, in a fairer world, serious candidates for the post. It then expressed regret that few Presidents nowadays were of the calibre of some of the stalwarts of the past, naming some of the worst examples. I read this piece and left it at that.

However, someone who was struck by some similarities with a piece I had written a month earlier, “E Sreedharan for President” http://www.rediff.com/news/column/e-sreedharan-for-president/20170616.htm on rediff.com put together a brief comparison chart that showed several similarities between my piece and Dhume’s piece. The BJP’s Amit Malviya tweeted about the similiarities, and here is his tweet: https://twitter.com/malviyamit/status/889723345641512962. The screenshot doesn’t capture the entire image.


That got me curious about these similarities, so I read both pieces carefully. There were differences: I wrote in general terms in June, requesting that the BJP nominate a non-politician. Dhume wrote in July, after the election, suggesting that a specific individual, Shri Ram Nath Kovind, the new President, was unworthy.

But overall, I was struck by the fact that the structures of the two pieces were almost identical: general concern about the role of the presidency, desire for non-politicians, etc. There were five or six clear similarities between the two. And I found a couple of others: both had mentioned the bathtub cartoon lampooning Fakhruddin Ali Ahmed, and while I dreaded some ‘dreary political apparatchik’ being chosen, Dhume called Kovind a ‘humdrum politician’. All this was interesting, but nothing of great substance.

However, this morning, I was directed to a piece by Dhume, “How the BJP’s Smear Machine Works: A Personal Story” https://medium.com/@Dhume01/how-the-bjps-smear-machine-works-e6aeb0fca78a . Apparently, Dhume feels there’s a conspiracy against him, and in passing, that I am guilty by association. I think he’s wrong on both counts. He wrote a point-by-point response which I feel compelled to respond to.

In addition to some general comments, Dhume suggests that there is a “a clear difference in our prose styles”. I am not quite sure about this, because looking at it casually, we both write passable prose, that’s about all.

Dhume makes a point of going through each of the six suggested similarities in the chart, and asserts that he could have arrived at them on his own, and he quotes his own earlier writings in July 2015 and in 2012.

But where my mild amusement turns to mild annoyance is when Dhume, choosing his words carefully, says the following:

“I’m not using these examples to claim that Srinivasan, or anyone else, lifted the idea of writing about the merits of India elevating a non-politician from my 2015 column”….

“Once again, I’m not accusing Srinivasan of plagiarism because he happened to make a similar observation to the one that I made in a column two years ago, or in a widely shared tweet six months ago…”

“Ironically, if I used exactly the same examples as in the graphic I could accuse Srinivasan of plagiarizing my earlier work”…

Graciously, he continues, “Of course, this is preposterous. I have no reason to believe that Srinivasan did not come to his views about the decline of the Indian presidency independently”.

Nice wording, reminds me of (in a small way) “I came to bury Ceasar, not to praise him”. Damning with faint praise, I believe they call it.

So it appears to be a claim about primogeniture, so to speak. Unfortunately for Dhume, I can point to another of my columns from ten years ago, which I had referred to at the start of “E Sreedharan for President”: from July 2007, http://www.rediff.com/news/column/rajeev/20070723.htm “A Whiff of a Manchurian Candidate”. Almost every one of the points Dhume elaborates on was elucidated there. So Dhume stands little chance of accusing me of plagiarizing from him because this was written ten years ago, much before his own work that he quotes.

Here are the points Dhume made in response to the chart:

  1. Mostly mediocre politicians. I said in 2007: “The ceremonial leader of the country, which the President is, should really not be a politician… What India needs are leaders, intellectuals and others who can inspire the citizenry to dream and to aspire to greatness.”
  2. Jagdish Bhagwati and other potential candidates. Here are my suggestions from 2007, apart from O V Vijayan and E Sreedharan: “N R Narayana Murthy or Ratan Tata or Lakshmi Mittal or Azim Premji… K P S Gill,… Jagdish Bhagwati,… C K Prahalad,… Arundhati Ghose,… Fathima Beevi,… Vandana Shiva”. Yes, there are/were many deserving candidates. Ratan Tata is also one of Dhume’s suggestions, along with Rahul Dravid (I would never suggest a cricket player). By the way, I think Jagdish Bhagwati may be a US citizen, in which case he’s not eligible for the post and both of us would be wrong.
  3. The presidential palace. No, I didn’t say anything about this in 2007, and yes, 300 acres or 340 rooms just suggests the place is huge, a place of unimaginable privilege
  4. Excellence of past Presidents. I said in 2007: “So far as I can tell, none of the politicians who held the position particularly distinguished himself.” The point is obvious, and whatever phrases we used, both Dhume and I basically said that.
  5. Kalam and Radhakrishnan as good Presidents. I said in 2007: “Kalam, on the other hand, certainly stood out. This is quite possibly because he was a working engineer, not a politician… Perhaps the scholar Sarvepalli Radhakrishnan was a good President, for he was a towering intellect”.
  6. Dynasty loyalists. I said in 2007: “The problem is that ….the Congress – which cannot think beyond the interests of the Nehru Dynasty… are not particularly thrilled at the prospect of an activist President. They would much rather have someone who will do what they are told. This may well be a reason for choosing Prathibha Patil…”

It’s rather clear that the prior art argument doesn’t work very well, because almost all the points made were covered by me either in 2007 or 2017 before Dhume’s 2017 piece. And the laws about copyright and ‘fair use’ are such that it is acceptable for someone to use another’s ideas for limited research and educational purposes.

Therefore let me grant that I have no reason to believe that Dhume is not capable of arriving at all these ideas by himself, which seems to be crux of his argument. Hey, I can do “damning with faint praise” with the best of ‘em.

But there is also the dictum that “plagiarism is stealing from one person; research is stealing from many people”. In these days of efficient Google searches, and crowdsourcing on Twitter, it is astonishing how much one can dig up through due diligence, and one may unconsciously internalize what one read somewhere.

As a non-professional journalist, I have had the luxury of having consistent opinions over time, and I have suffered for it. For the longest time, I was a complete outlier. Then I was kicked off one newspaper not for what I wrote, but because the opinion editor didn’t like my political perspective. I stopped writing for another because the sub editor, who disagreed with my perspective, made it clear through unreasonable demands that he didn’t want me there. So I’m not about to change now. But there has long been a Leftie stranglehold on opinion, which basically prevents any dissent; and that extends to their online acolytes on Twitter and Facebook. They have been masters of ‘manufacturing consent’.

Therefore if Dhume feels that there is a Hindutva Troll Army [sic] after him, I sympathize. Personally, I don’t know anything about this, and they are all big boys and can take care of themselves.Screenshot 2017-07-26 at 1.12.27 PM

didn’t occur to me when i wrote this article, but senkumar situation is a bit like l’affaire dreyfus in france: he’s a possible victim of religious discrimination. those of the communist religion do whatever they can to hurt hindus. this article was published on swarajay.com on 16 jul 2017 at https://swarajyamag.com/politics/is-the-hounding-of-former-kerala-dgp-t-p-senkumar-a-communist-witch-hunt

i had written in 1998 about l’affaire dreyfus and its relevance to us today. there is nobody to stand up for a wronged man. dreyfus was attacked because he was a jew. i think senkumar is being attacked partly because he’s an ezhava. and we have no emile zolas to say j’accuse. http://www.rediff.com/news/1998/jul/23rajeev.htm

this book review was published in Swarajya magazine, March 2017. it is not online, so here’s my actual copy.

lately, i’ve been seeing a lot of people swear by ‘data’. this is another shibboleth with terrible implications. the west has the vanity that by reducing everything to data they can arrive at the truth. that is not true. data becomes information only when contextual information is supplied.

besides, there are problems with ‘fat tailed distributions’. we assume, implicitly, that the phenomena we study are under the gaussian bell curve on normal distribution. if they aren’t, and are fat tailed, then what we think are unlikely events will happen far more frequently than we thought: thus black swan events.

the other huge problem is the unconscious assumption that ‘correlation = causation’.

we mess up on all these fronts.

what i call ‘junk data’ is data with incorrect assumptions that has been fed into computers, which will  produce circular reasoning that ‘proves’ the assumptions correct. there are also heavily biased data sources that ignore inconvenient data points.

briefly, excessive dependence on the infallibility of computers is a bad idea.

i wrote a companion piece to this in swarajya, on the ethical problems of bias in data selection for AI. https://swarajyamag.com/magazine/fatal-flaw-in-ai-the-robots-will-probably-be-as-biased-as-their-masters

The Danger from our Over-Reliance on Computers and Big Data

Rajeev Srinivasan (Book Review)

Can we trust computers? The evidence is mixed. Those in the business have seen enough ‘kludges’ and bugs that they would, if they were honest, be suspicious of a lot of things spewed out by computers. But do the infernal machines work, more or less, most of the time? They in fact do, and they do useful things, too. It is now possible to mathematically prove at least the core (or kernel) of operating systems, but on average we have to take things on faith.

The average person on the street, however, is often misled into thinking that anything that comes out of a computer must be true, because after it all, it has the weight of all those white-coated types chanting mysterious incantations or whatever that you see in the movies. So if the computer tells you your credit score is a tad low at 500, you take it in all humility and internalize the idea that you are a bit of a deadbeat who can’t get loans.

Not to be a neo-Luddite, but our excessive dependence on computers is a bit worrisome. Our faculties as a species may be getting eroded. For instance, all of us used to do arithmetic in our head, until calculators came around. We used to navigate ourselves, until Google Maps appeared. There was a controversy over a 2008 article in the US monthly the Atlantic “Google Is Making Us Stupid” because now we don’t need to know anything, as we can look it up.

Unfortunately, this is coming precisely as computers are getting smarter. All those chess, Go, and poker players who have been bested by computers can tell you that. In fact, we now have to worry about the ethics of artificial intelligence and self-driving cars, as I mention in a companion piece in this issue. At least we think that’s in the future, but this book, Weapons of Math Destruction (Allen Lane/Penguin Random House, 2016) by Cathy O’Neill, a PhD mathematician-turned-data-scientist, suggests we are already feeling some of the deadly effects of Big Data.

A part of the problem comes from the confusion of statistical correlation with causation. The computer models make assumptions – for example that a broken family is associated with increased tendency towards violent crime – which are in the bowels of the algorithm, and are opaque and cannot be questioned by their victims. These proxies may not have a causal relationship with the outcome, but their use is widespread. A recent study by Daniel Hamermesch et al suggests there is a correlation in the US workforce between race and laziness, but undoubtedly, it will be taken to mean causation that blacks and Hispanics are inherently lazy precisely because they are blacks and Hispanics.

O’Neill’s villains are the big algorithms that increasingly run our lives. In a dystopian vision, she produces example after example of big pieces of software that have become a sort of Deep State, one whose workings are incomprehensible except to the code-jockey boffins who run them; and said boffins often have no idea of the devastation they can wreak on individuals and society.

We are generally familiar with the trading software that has caused ‘flash crashes’ and the algorithms that led to the sub-prime lending debacle in the US. But O’Neill (who had a ring-side view of the market meltdown as a quant jock at hedge fund D E Shaw) points out that that there are several others that have equally sinister outcomes, often because there is a self-fulfilling prophesy – people who are deemed undesirable by algorithms in fact become undesirable as a result.

O’Neill starts with a rating scheme for convicts called LSI (Level of Service Inventory); unfortunately, says she, the parameters used to rate them, and the questions in the questionnaire, are biased against the poor and especially against blacks. Thus unemployment and criminal convictions among friends and family seem reasonable enough questions, but they end up giving them longer terms and likely greater difficulty in finding a job upon release. This creates a vicious cycle.

Then O’Neill goes on to several other case studies, all of which may seem innocuous enough to begin with. But we begin to succumb to total dependence on the ratings spewed out by algorithms, the connection with reality begins to recede. The software guys doing the coding may have no idea of whether the assumptions they are making are appropriate. And the field guys who do know that stuff will soon be defeated by the complexity of the algorithms.

One result is the Black Swan effect that Nassim Nicholas Taleb wrote about so evocatively. Events with very low, but non-zero, probability will soon be excluded from the calculations, with the result that when such events occur (as they did in the 2008 meltdown) the entire edifice on which the algorithms rest will crumble catastrophically.

O’Neill talks of “haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias”. In addition, she wonders if “we’ve eliminated human bias or merely camouflaged it with technology”. She talks of “pernicious feedback loops” that lead to “toxic cycles”, and she concludes that these are the mathematical analogs of Weapons of Mass Destruction, hence the title of the book.

As examples, O’Neill offers up several algorithms. One is used to rate schoolteachers, which seems to grossly distort the incentives for teachers to focus largely, or entirely, on test scores, thus devaluing various other things a good teacher can offer: such as inspiring students, or taking time with a slow starter.

Another is the pernicious role played by a US News and World Report ranking of colleges, which has outlived the magazine itself. Apparently an objective measurement of the ‘quality’ of the college, this metric has now become so widely adopted that colleges focus exclusively on the fifteen parameters it considers. So much so that they went on a spending spree, building stadiums, grand campuses, attracting star football players, and so on. But college fees were not part of the metric; and these soared, as well.

Today, toxic student loans are a huge overhang on the US economy, as big as the bad mortgage problem. In addition, entrepreneurs created rip-off private for-profit colleges

which deliberately targeted poor and military-veteran students, as well as non-whites. Again, clever advertising techniques using Big Data allowed these colleges to sell essentially useless but expensive, loan-led ‘education’ to these people.

O’Neill suggests that greed is a major factor. The folks over on Wall Street who have been making up ever cleverer mathematical models to make money often don’t realize that money comes from screwing over real people, as was the case with sub-prime mortgages and the related credit-default swaps and Collateralized Debt Obligations. Or even if they do, they don’t care. She points out that despite major convulsions, in large part the big Wall Street firms and banks and hedge funds did all right, often at taxpayer expense.

O’Neill goes on to give a litany of other examples of malevolent data exploitation, for instance in hiring, loan processing, worker evaluation, voter targeting and even health monitoring. It’s chilling to think of how these will play out when applied to the relatively trusting and naïve populations of rural India. These algorithms, which are “opaque, unregulated and incontestable”, can be truly weapons of mass destruction. They take your privacy and individualism away from you, and whatever they decide about you, you have no appeal. Truly a frightening Big Brother scenario.

1250 words, 10 February 2017