etendue

I'd be very wary of using complex SOUP like TensorFlow, even if brought under my quality system. I think a good answer here is that once one goes under design control the subset of functionality needed should be implemented in-house under the organization's SDLC.

The report makes reference to "Assessment of Safety Standards for Automotive Electronic Control Systems" by NHTSA, which itself reviews ISO 26262, MIL-STD-882E, DO-178C, the FMVSS, AUTOSAR, and MISRA C.

In this context, they mean verification and validation in the systems engineering sense. Software would be included in that it is a part of the whole system.

N.B., this policy is mainly concerned with Highly Automated Vehicles (HAVs), which are defined as SAE Level 3 ("capable of monitoring the driving environment") and above.

edit: as to SAE Level 2, it has this (and more) to say:

> Furthermore, manufacturers and other entities should place significant emphasis on assessing the risk of driver complacency and misuse of Level 2 systems, and develop effective countermeasures to assist drivers in properly using the system as the manufacturer expects. Complacency has been defined as, “... [when an operator] over-relies on and excessively trusts the automation, and subsequently fails to exercise his or her vigilance and/or supervisory duties” (Parasuraman, 1997).

also,

> Manufacturers and other entities should assume that the technical distinction between the levels of automation (e.g., between Level 2 and Level 3) may not be clear to all users or to the general public.

I think this point from the post deserves emphasis, for consideration in future discussions:

> If Tesla makes a "major" change to software, pretty much all bets are off as to whether the new software will be better or worse in practice unless a safety critical assurance methodology (e.g., ISO 26262) has been used. (In fact, one can argue that any change invalidates previous test data, but that is a fine point beyond what I want to cover here.) Tesla says they're making a dramatic switch to radar as a primary sensor with this version. That sounds like it could be a major change. It would be no surprise if this software version resets the clock to zero miles of experience in terms of software field reliability both for better and for worse.

I am admittedly a broken record on this point, but Tesla moves very fast and very nimble on a system that is under design controls. It would be very educational to learn about their development process and how it maps to ISO 26262.

Thinking of training data sets as an example: they contain a lot of personal information (faces, license plates, times, locations, speeds, etc.). Depends on jurisdiction, but there likely aren't legal issues in amassing and using this data when it is collected in plain view from public areas; I envision possible ethical issues though. Could you release training data for public use, without obscuring identifying information? There may not be legal issues in releasing raw data, but keeping in mind that the information is sensitive and recorded without consent, and that an engineer's first duty is to the public health, safety, and well-being, might there be a professional obligation to sanitize the data?

My example might be a bit contrived, but I think there are going to be many valid (and far better!) questions in this discipline that should be asked and considered, and I think your graduates need to be equipped to do so.

Will ethics (e.g., the collection and storage of personal information, or professional responsibilities in regulated industries) be given any treatment?

I believe that the comment you are responding to was speaking to the asymmetry that people with experience in computational biology have an easier time moving to general data science problems than do people with experience in general data science working on computational biology problems.

I agree that the asymmetry exists: there is a tremendous baseline of scientific knowledge and experience that is needed to make significant contributions to the field. I personally have worked with people with backgrounds in programming or CS on medical problems, and it has been frustrating because they lack what I would term "scientific common sense". I would personally prefer, and would be able to make more progress with, working with (for example) anyone who has completed a sequence of education sufficient for pre-med requirements and has some programming experience over a "full stack data engineer". Even if someone with a programming or CS background were inclined to pick up the textbooks and amass the baseline scientific knowledge (I'm sure they exist, although I haven't met them yet), they'd still lack the years of laboratory work and experience of applying this knowledge.

My original comment was apparently poorly worded because it was interpreted by the responders differently than I intended, but delightfully, it resulted in very thoughtful comments. I am very skeptical that one can make even small contributions to genetics without the experience of years of specialized work. There are ancillary problems that could be done by someone with a programming or CS background, e.g., a better LIMS system, or perhaps protocol management, but I don't see those tasks as leading to later making meaningful contributions to the field of genetics. The MD or PhD isn't required, but all the work done leading up to it is, and so as I see it those prepared to make the contributions are most likely going to have gotten the degree on the way.

How would one go about meaningfully contributing to solving problems in genetics without having done the work leading to a MD or PhD (or both)?

> Or is "that's disturbing" somehow considered to be an indication of dismissal or disagreement?

Actually, you said "It disturbs me that all the examples so far are science fiction." which (a bit out of context and ignoring the principle of charity) could be interpreted as a cursory dismissal along the lines of, "these examples are too ridiculous to consider further".

Even with the principle of charity, I find "they're getting it from sci-fi movies" to be an unfair summary of my point, but perhaps I'm doing a poor job making my case clearly.

Thinking on it, would it be fair to expect any example of an autopilot function on a car to be from a type other than science fiction or fantasy?