Here is an argument for allowing companies to maintain a lot of secrecy about how their data mining (AI) models work. The claime is that revealing information will put companies at a competitive disadvantage. Sorry, that is not enough of a reason. And it’s not actually true, as far as I can tell.
The first consideration when discussing transparency in AI should be data, the fuel that powers the algorithms. Because data is the foundation for all AI, it is valid to want to know where the data…
Source: The problem with ‘explainable AI’ | TechCrunch
Here is my response.
Your questions are good ones. But you seem to think that explainability cannot be achieved except by giving away all the work that led to the AI system. That is a straw man. Take deep systems, for example. The IP includes:
1) The training set of data
2) The core architecture of the network (number of layers etc)
3) The training procedures over time, including all the testing and tuning that went on.
4) The resulting system (weights, filters, transforms, etc).
5) HIgher-level “explanations,” whatever those may be. (For me, these might be a reduced-form model that is approximately linear, and can be interpreted.)
Revealing even #4 would be somewhat useful to competitors, but not decisive. The original developers will be able to update and refine their model, while people with only #4 will not. The same for any of the other elements.
I suspect the main fear about revealing this, at least among for-profit companies, is that it opens them up to second-guessing . For example, what do you want to bet that the systems now being used to suggest recidivism have bugs? Someone with enough expertise and $ might be able to make intelligent guesses about bugs, although I don’t see how they could prove them.
Sure, such criticism would make companies more cautious, and cost them money. And big companies might be better able to hide behind layers of lawyers and obfuscation. But those hypothetical problems are quite a distance in the future. Society deserves to, and should, do more to figure out where these systems have problems. Let’s allow some experiments, and even some different laws in different jurisdictions, to go forward for a few years. To prevent this is just trusting the self-appointed experts to do what is in everyone else’s best interests. We know that works poorly!
Academia has a problem: the value, necessity, and practices of collaboration are increasing, but the system of giving credit is inadequate. In most fields, there are only 4 levels of credit:
- None at all
- “Our thanks to Jill for sharing her data.” (a note of thanks)
- First Authorship (This is ambiguous: it may be alphabetical.)
- Listed as another author
In contrast to this paucity, modern empirical paper writing has many roles. Here are a dozen roles. Not all of them are important on a single paper, but each of them is important in some papers.
- Intellectual leadership.
- Source of the original idea
- Doing the writing
- Writing various parts, e.g. literature review
- Doing the grunt work on the stat analysis. (Writing and running the R code)
- Doing the grunt work of finalizing for publication. (Much easier than it used to be!)
- Dealing with revisions, exchanges with editors, etc.
- Source of the data.
- Raised the funding;
- Runs the lab where the authors are employed
- Source of the money: usually an agency or foundation, but sometimes the contracting author is listed as a coauthor.
SC overturns Lexmark’s patent win on used printer cartridges. Since the 17th century, restricting resale has been “against Trade and Traffique.”
Source: Supreme Court overturns Lexmark’s patent win on used printer cartridges | Ars Technica
Summary: once a product is sold, the original patent holder can’t control how it is subsequently used.
Not the only seller.
Today’s ruling is a win for many tech companies, with companies like Vizio, Dell, Intel, LG Electronics, HTC, and Western Digital all taking the side of Impression Products. [the winner] …The companies on Lexmark’s side, no surprise, were heavy licensers of patents, including tech giants like Qualcomm, IBM, Nokia, and Dolby. Biotechnology and pharmaceutical groups also supported Lexmark. Those lineups largely mirror industry divisions over Congressional debates around reforming patent laws, with the pro-Impression companies favoring user-friendly changes to patent laws, and the pro-Lexmark companies wanting more changes that favor patent owners.
I often gripe about the Supreme Court’s seeming “go with the big $” jurisprudence. But in this case, there was plenty of corporate power on both sides. And the 7-1 verdict means it was not a close call.
I just taught the Theranos case in my course on “Innovation and Industry Development,” co-taught with Prof. Elizabeth Lyons. The first half is about positioning a startup: powerful new technology, established incumbents, how should we enter to disrupt the industry and make the world a better place? Any moderate set of numbers makes Theranos’ reputed $9,000,000,000 valuation look reasonable.
The “case” presently consists of four articles. I put together a set of overhead slides to generate and lead the discussion. The first half ends with some general lessons about disruptive innovation and whether to follow an open or closed IP strategy. The second half starts in December 2015 and discusses the crash. I also compare Theranos with the Google contact lens (another technically impossible pseudo-invention).
“That’s a type of Silicon Valley arrogance,” he said. “That isn’t how science works.” (re Google, not Theranos)
Every 10 years or so, a conspicuous bubble bursts, and in doing so it resets the expectations of the next generation of young adults.
- 2008 financial collapse
- Now Theranos
Reading this article, I’m astonished at how little substance the adulation of Elizabeth Holmes was based on. And how much secrecy her investors allowed her. Given that she was claiming that her system would be ~100x better than established technologies, why didn’t they demand evidence? Why was it left to a reporter to figure out that the emperor had no clothes? And, was she nothing more than a successful con-artist with no genuine scientific expertise?
“In a searing investigation into the once lauded biotech start-up Theranos, Nick Bilton discovers that its precocious founder defied medical experts—even her own chief scientist—about the veracity of its now discredited blood-testing technology.”
Source: Exclusive: How Elizabeth Holmes’s House of Cards Came Tumbling Down | Vanity Fair
Here’s a column by a Forbes blogger about Zika saying that “we should not wait so long to develop vaccines against tropical diseases.” He concludes:
Many pharmaceutical companies don’t focus on a disease until it becomes common enough to be highly profitable. The trouble is the vaccine world has become a bit like the plot line for “She’s All That” or “Cinderella.” Attention towards a person or thing does not occur until a cool person notices he or she or it. But when it comes to disease and stock market opportunities, as the saying goes, once your grandmother knows about it, it is usually too late.
Source: Zika Vaccine: Another Example Of Waiting Until It’s Too Late? – Forbes
This is not news. And it’s a classic situation where market forces are not enough to give socially desirable behavior. Developing a vaccine for a disease that is not in rich countries has low expected profitability. Even if the disease goes epidemic, pharma company will have to sell at a price near marginal cost.
The only solution is to use a different way to fund development. Contests, grants (Gates foundation), purchase guarantees (used by US DoD) all work. But waiting for the traditional patent system + pharma profit motive won’t lead to timely development of medication for poor-country diseases.
I guess a Forbes columnist is not allowed to point this out.
I’ve probably purchased 300 books in the last year for research purposes, not to mention all the fiction my wife gets (and so do I, if it costs $3 or less). For the newer ones , buying them as eBooks is generally an option. But the state of software, DRM, and copy protection for Kindle books is a mess. Kindle’s software (like iBooks) is deliberately crippled – no copying into another document, no printing, and especially no way to copy diagrams. I’m running Kindle’s software on my Mac and on an iPad, rather than using a Kindle tablet, but that barely helps.