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Can University Faculty Hold Platforms To Account?

Heidi Tworek has a good piece with the Centre for International Governance Innovation, where she questions whether there will be a sufficient number of faculty in Canada (and elsewhere) to make use of information that digital-first companies might be compelled to make available to researchers. The general argument goes that if companies must make information available to academics then these academics can study the information and, subsequently, hold companies to account and guide evidence-based policymaking.

Tworek’s argument focuses on two key things.

  1. First, there has been a decline in the tenured professoriate in Canada, with the effect that the adjunct faculty who are ‘filling in’ are busy teaching and really don’t have a chance to lead research.
  2. While a vanishingly small number of PhD holders obtain a tenure track role, a reasonable number may be going into the very digital-first companies that researchers needs data from to hold them accountable.

On this latter point, she writes:

If the companies have far more researchers than universities have, transparency regulations may not do as much to address the imbalance of knowledge as many expect.

I don’t think that hiring people with PhDs necessarily means that companies are addressing knowledge imbalances. Whatever is learned by these researchers tends to be sheltered within corporate walls and protected by NDAs. So those researchers going into companies may learn what’s going on but be unable (or unmotivated) to leverage what they know in order to inform policy discussions meant to hold companies to account.

To be clear, I really do agree with a lot in this article. However, I think it does have a few areas for further consideration.

First, more needs to be said about what, specifically, ’transparency’ encompasses and its relationships with data type, availability, etc. Transparency is a deeply contested concept and there are a lot of ways that the revelation of data basically creates a funhouse of mirrors effect, insofar as what researchers ‘see’ can be very distorted from the reality of what truly is.

Second, making data available isn’t just about whether universities have the professors to do the work but, really, whether the government and its regulators have the staff time as well. Professors are doing a lot of things whereas regulators can assign staff to just work the data, day in and day out. Focus matters.

Third, and related, I have to admit that I have pretty severe doubts about the ability of professors to seriously take up and make use of information from platforms, at scale and with policy impact, because it’s never going to be their full time jobs to do so. Professors are also going to be required to publish in books or journals, which means their outputs will be delayed and inaccessible to companies, government bureaucrats and regulators, and NGO staff. I’m sure academics will have lovely and insightful discussions…but they won’t happen fast enough, or in accessible places or in plain language, to generally affect policy debates.

So, what might need to be added to start fleshing out how universities are organised to make use of data released by companies and have policy impacts in research outputs?

First, universities in Canada would need to get truly serious about creating a ’researcher class’ to analyse corporate reporting. This would involve prioritising the hiring of research associates and senior research associates who have few or no teaching responsibilities.1

Second, universities would need to work to create centres such as the Citizen Lab, or related groups.2 These don’t need to be organisations which try and cover the waterfront of all digital issues. They could, instead, be more focused to reduce the number of staff or fellows that are needed to fulfil the organisation’s mandate. Any and all centres of this type would see a small handful of people with PhDs (who largely lack teaching responsibilities) guide multidisciplinary teams of staff. Those same staff members would not typically need a a PhD. They would need to be nimble enough to move quickly while using a peer-review lite process to validate findings, but not see journal or book outputs as their primacy currency for promotion or hiring.

Third, the centres would need a core group of long-term staffers. This core body of long-term researchers is needed to develop policy expertise that graduate students just don’t possess or develop in their short tenure in the university. Moreover, these same long-term researchers can then train graduate student fellows of the centres in question, with the effect of slowly building a cadre of researchers who are equipped to critically assess digital-first companies.

Fourth, the staff at research centres needs to be paid well and properly. They cannot be regarded as ‘graduate student plus’ employees but as specialists who will be of interest to government and corporations. This means that the university will need to pay competitive wages in order to secure the staff needed to fulfil centre mandates.

Basically if universities are to be successful in holding big data companies to account they’ll need to incubate quasi-NGOs and let them loose under the university’s auspice. It is, however, worth asking whether this should be the goal of the university in the first place: should society be outsourcing a large amount of the ‘transparency research’ that is designed to have policy impact or guide evidence-based policy making to academics, or should we instead bolster the capacities of government departments and regulatory agencies to undertake these activities

Put differently, and in context with Tworek’s argument: I think that assuming that PhDs holders working as faculty in universities are the solution to analysing data released by corporations can only hold if you happen to (a) hold or aspire to hold a PhD; (b) possesses or aspire to possess a research-focused tenure track job.

I don’t think that either (a) or (b) should guide the majority of the way forward in developing policy proposals as they pertain to holding corporations to account.

Do faculty have a role in holding companies such as Google, Facebook, Amazon, Apple, or Netflix to account? You bet. But if the university, and university researchers, are going to seriously get involved in using data released by companies to hold them to account and have policy impact, then I think we need dedicated and focused researchers. Faculty who are torn between teaching, writing and publishing in inaccessible locations using baroque theoretical lenses, pursuing funding opportunities and undertaking large amounts of department service and performing graduate student supervision are just not going to be sufficient to address the task at hand.


  1. In the interests of disclosure, I currently hold one of these roles. ↩︎
  2. Again in the interests of disclosure, this is the kind of place I currently work at. ↩︎

The Roundup for December 30 – January 5, 2017 Edition

Climb
Climb by Christopher Parsons

I’ve long planned a lot in my personal and professional life. I keep financial roundups so that I can see how I’m faring through and across years, periodic emotional evaluations, and live by my weekly and quarterly professional schedules.1 But what I’m doing is only kinda-working. So I’ve been casting about for a new process to not just hold myself to account but to hold myself to better set goals and accomplish my tasks at hand.

I’m considering adopting shortened planning periods (e.g. 10 week planning cycles, with a 2 week ‘buffer’ for recollection, learning, evaluation, and next-cycle planning) and will likely experiment with this approach to professional goal setting and project completion. But I also want to get better at reflecting on my annual themes and goals. To that end, I was interested in what Michael Karnjanaprakorn (of Skillshare) wrote about planning his ‘ideal’ year.

Specifically I was interested in how he reviews his monthly and weekly goals. In writing about monthly goals, at the end of each month he evaluates:

  1. From 0–10, how do you feel you are doing?
  2. What were the highlights and lowlights?
  3. What were the biggest lessons learned?
  4. Review your goals and assess your progress. Did you spend your time on the right things? If not how will you improve next month?
  5. Write down goals for the upcoming month.

I’ve been really bad at reviewing my monthly (and quarterly) goals but that’s a result of why I’ve historically set and logged professional goals: I’m just really bad at remembering all that I’ve done in any given year, and so fall into deep funks if I can’t periodically go through the past year and realized ‘oh, hey! I’m actually doing a hella lot of work, and am advancing both my own projects and those of colleagues and partners!’ After years of doing this kind of goal-tracking I want to get better at longer-term tracking that is less done for just mental health reasons and more for organizational accountability reasons.

So, to try and get better at reviewing longer-term goals I want to try something like what Michael has outlined. But, at the same time, I want to figure out a way of nicely presenting this information a glanceable digital format; all of my weekly tracking is on paper and so it’s not particularly conducive to understanding longer-term trends that exceed a month or two.

With regards to weekly updates, Michael evaluates progress on monthly and weekly goals. Specifically:

  • Review annual & monthly goals
  • Review last week’s progress
  • Review habits
  • Plan weekly priorities (3 personal & 3 work)

I’ve been good at reviewing my last week’s progress and thinking about weekly priorities but less good at either thinking about habits or how activities really advance my longer-term goals. So I want to adopt some of these kinds of reviews as well.

But the area that I most need to focus on surrounds setting longer-term personal life goals. I’m pretty good at professional goal setting: I’ve been setting and hitting the big ticket items over the past decade or so. But I don’t have really good visions for what I want to happen in my personal life.2

To this end, I’ve adopted a series of personal goals this year that aren’t just about reforming habits but are more focused towards longer-term aspirations. I’m going to be curious as to how those really work out but, to be honest, I just want to try and envision what my non-technical personal goals might be.3 If I can spend a year thinking through what I want to do with my personal life over the next 5, 10, and 20 years, and have some discrete strong ideas, then I’ll really be happy regardless of how well I accomplish the more technical personal goals I’ve set for myself this year.


Companies are doing everything they can to ensure that you own a speaker and/or microphone device that is hooked into their virtual assistant. Microsoft is trying to do it with Cortana. Google with, well, Google. Amazon with Alexa. And Apple with Siri.

For a long time it’s seemed like the assistant that comes with your chosen smartphone would act as the pathway into any given virtual assistant. While some might have multiple assistants on the same device — by way of installing the assistant in a separate application — it was unlikely that the secondary assistants would ‘take over’ your daily operations. And given the failure of Amazon’s Fire Phone, Amazon was likely out of the running for establishing the most dominant assistant in the United States.

But then along came Amazon’s smart speakers and the landscape of smart speakers and Alexa in the continental United States has changed dramatically. As noted by M.G. Siegler:

Amazon is winning this battle because they’re putting Alexa everywhere. Some of this is thanks to third-parties, but a larger part is the strategy to sell devices such as the Echo Dot for $29. At such prices, it’s not only a no-brainer to get one to at least try out — it’s a no-brainer to get a few of them to place all around your house. If this is the winning strategy — which I believe it to be — Apple cannot compete with this because it’s not in Apple’s DNA to run this type of playbook.

I think that one the one hand Siegler is very correct: Amazon is fast becoming a dominant player in the United States. But there are a few limitations to his (admittedly brief) analysis:

  1. Amazon’s Alexa, by being as cheap as it is, lacks the prestige of Apple’s brand and, by extension, Siri’s exclusivity;
  2. Apple’s ‘moat’ which is created around their infrastructure by only letting Siri be the default virtual assistant means that a lot of non-price conscious users will keep waiting and using Apple products;
  3. Alexa is a very United States-focused product; the speakers are cheap by not essential to conducting daily life or business. Contrast with smartphones which are requirements for daily life in many areas of the world; this means that even as Alexa floods the U.S. market the emerging economic regions of the world will continue to adopt Android (i.e. Google) and, to a far lesser extent, Cortana and Siri.

While the ‘threat’ to Apple of Alexa’s spread-by-speaker is linked to people buying them in droves I think that Amazon’s smart speakers are fundamentally poised to intrude into Google’s market and less Apple’s. Moreover, while people tend to only buy speakers once in a few years4 that tends to be the case because they’re expensive. So if people are only spending $100 or so on speakers…will that mean they’re disincentivized to buy ones that sound significantly better to play music? For consumers that purchase the HomePod they’re unlikely to replace the one or two they buy every few years, whereas if someone dropped $60 on Amazon speakers they might be tempted to just shift over to Google’s own (equivalently priced) offering or even to Apple’s or Sonos’ more expensive, and better sounding, premium offerings.

I think that the real threat to Apple or to Google will come as consumers purchase the more expensive and, by extension, better sounding, speakers. Those kinds of devices are unlikely to be replaced and will function as another kind of ‘moat’ that will contain consumers in a given virtual assistant ecosystem. Though it would be pretty amazing to see a world where people, when selling their phones second-hand, also end up selling their speaker sets alongside them to truly switch ecosystems…


Great Photography Shots

I’m absolutely loving some of the 100 best iPhone photos of 2017 which have been collated by iPhone Photography School. A few examples:

Music I’m Digging

Neat Podcast Episodes

Good Reads for the Week

Cool Things

  1. Ok, so I sometimes blow the quarterly schedules but I hold myself to account for why they get blown.
  2. To some extent my ‘success’ in planning long-term professional goals has been tightly linked to a historical failure to balance my work and life: my work entirely dominated everything I did and who I was.
  3. Technical goals being things like reduce student loan debt by X or learn Y new recipes.
  4. I’ve been using the same 2.1 speakers attached to my TV for over a decade at this point and not really tempted to replace a perfectly good set of speakers for something else that would be equally perfectly good. Except for maybe a pair of Apple HomePods…
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The Subtle Ways Your Digital Assistant Might Manipulate You

From Wired:

Amazon’s Echo and Alphabet’s Home cost less than $200 today, and that price will likely drop. So who will pay our butler’s salary, especially as it offers additional services? Advertisers, most likely. Our butler may recommend services and products that further the super-platform’s financial interests, rather than our own interests. By serving its true masters—the platforms—it may distort our view of the market and lead us to services and products that its masters wish to promote.

But the potential harm transcends the search bias issue, which Google is currently defending in Europe. The increase in the super-platform’s economic power can translate into political power. As we increasingly rely on one or two head butlers, the super-platform will learn about our political beliefs and have the power to affect our views and the public debate.

The discussions about algorithmic bias often have an almost science fiction feel to them. But as personal assistant platforms are monetized by platforms by inking deals with advertisers and designing secretive business practices designed to extract value from users, the threat of attitude shaping will become even more important. Why did your assistant recommend a particular route? (Answer: because it took you past businesses the platform owner believes you are predisposed to spend money at.) Why did your assistant present a particular piece of news? (Answer: because the piece in question conformed with your existing views and thus increased time you spent on the site, during which you were exposed to the platform’s associated advertising partners’ content.)

We are shifting to a world where algorithms are functionally what we call magic. A type of magic that can be used to exploit us while we think that algorithmically-designed digital assistants are markedly changing our lives for the better.