1. Digital Finance: There's a regular theme I hit when it comes to digital finance--digital gives much more power to providers, government or private sector, than physical cash does. And that is something we should worry about. So my confirmation bias when into overdrive when this crossed my feed this week: China is detaining ethnic and religious minorities in Xinjiang Province and one of the criteria for detention is people who "did not use their mobile phone after registering it." Brett Scott objects to cashlessness for both its inherent nature as a tool of surveillance and for more pecuniary reasons: unlike cash, every digital transaction generates fees. Which in turn gives power to the organizations that have a seemingly insatiable appetite for categorizing and controlling people. Hey, ever wonder why Facebook is pushing hard into payments, even into fundraising for non-profits?
Scott uses Sweden's progress toward cashlessness as a foil. Want to guess which other country beyond China and Sweden has made the most progress toward digital-only payments? Somaliland. Huh. Elsewhere, the progress of digital finance seems to have slowed to a crawl: 76% of mobile money accounts are dormant, and the average active user only conducts 2.9 transactions a month. Perhaps that's because of a huge gap in usability that will require a similarly large push in education (according to Sanjay Sinha).
Given the near unrelenting negativity above, I feel like I have to say for the record: I don't oppose digitisation. I oppose not recognizing and planning for the negative consequences of digitisation.
2. Global Finance: Digital finance and mobile money is generally about very local transactions. But another important use is long-distance transactions, particularly remittances. But international transfers of funds require banks to have relationships that cross borders. The technical term is "correspondent banks." What correspondent banks do is vastly simplify and accelerate the flow of funds across borders. So it's a problem that correspondent banking relationships are shutting down as a result of "de-risking," which is banking jargon for "avoiding anything that may draw the attention of regulators who have the somewhat arbitrary ability to impose massive fines." The IFC reports that more than a quarter of banks responding to their survey reported losing correspondent bank relationships with compliance costs the most common reason; and 78% expected compliance costs to increase substantially for 2017.
And now for a bit of levity, if you can call it that. Matt Levine has the incredible story of how the Batista brothers, owners of a large Brazilian meat-packing company, made money shorting the Brazilian Real--they knew recordings of their conversations with President Michel Temer about bribes were going to be released. Is that insider trading?
3. US Poverty and Inequality: This week the US Census Bureau released its report on income and poverty in the United States in 2016. The new was good, at least on a relative basis: incomes are growing across the board and poverty is down. But...the majority of gains are still going to upper income groups, and inequality continues to rise as a result. The bottom half of the distribution is only now getting back to where it was in 1999 or earlier. Here's Sheldon Danziger's take on the data and the policy implications. The Economic Progress Institute has a good overview (with good charts) of the poverty data specifically, which focuses on how safety net programs reduce the number of people below poverty by "tens of millions."
The 8+ million who are above the Supplmental Poverty Measure threshold because of refundable tax credits (e.g. the EITC and the Child Tax Credit) particularly caught my eye because of this profile of a US Financial Diaries household that I just finished. Amy Cox, for the year we followed her, is one of those people. For the year, she is above the SPM because of tax credits. But she receives all of that in one lump sum in February. So for 11 months of the year, she's poor. In 9 months of the year, she's around 75% of the SPM threshold. But officially, she's not poor. Makes me think it's time for a Supplemental Supplemental Poverty Measure that takes into account how many weeks a year someone is below the line.
In other US Financial Diaries news, here's Jonathan Morduch speaking about Steady Jobs without Steady Pay at TEDxWilmington this week (skip ahead to 1:30:00).
Week of September 4, 2017
1. Evidence-Based Policy and Methods: One of the reasons I took a few weeks off was in late August I was part of a panel at Stockholm International Water Week sponsored by Water.org on the "evidence base for WASH microfinance." If you've been following the evaluations of microfinance or of WASH you know that evidence base is thin (in more ways than one). Preparing for the panel got me thinking about the strange state of evidence-based interventions. [Warning: I'm going to oversimplify for the next few paragraphs; if you want not oversimplified I recommend the detailed write-ups GiveWell has on both deworming and WASH] Arguably deworming is the sine qua non of evidence-based interventions right now, but the arguably mostly comes not from whether there is some other intervention with a better claim, but that there are large swathes of people who don't believe the evidence for deworming: epidemiologists. Why? Because there isn't a plausible biological mechanism to explain where the gains from deworming come from. There is no consistent detectable effect of deworming on weight or anemia for instance.
In the meantime, there's no question that if you remove bacteria and viruses from water, people won't get sick and will have all sorts of positive short-term health gains. But the most rigorous evaluations of WASH interventions don't find detectable effects on incidence of diarrhea or other health or economic indicators. The most-likely story is that there are so many vectors for infection that people end up consuming contaminated water despite the WASH interventions (and given that doctors in US hospitals still won't wash their hands regularly, that's very plausible). In that way, WASH has a lot in common with microfinance--single point interventions in complex and broken systems are unlikely to produce large long-term effects.
So the state of play is that the intervention with a clear biological mechanism has no effect and the one with no clear biological mechanism has large effects. I hope I'm not the only one who finds that a bit discomfiting.
So what to make of all of this? The point I made at the conference is that building an evidence base isn't just about methodology but about what is being measured. In the WASH + microfinance space, I think the right metrics are about whether well-functioning markets are being created (see my rant about low-quality equilibria, or my "vaccine or antibiotic" theory of change for microfinance piece) where poor households have more actual ability to choose, including the option to not have to think about whether their water is clean.
A second important point is that there is a long way to go figuring out how to measure things we care about. To that point specifically, Rachel Glennerster and Claire Walsh have a post about the difficulty of measuring women's empowerment via surveys and the limitations of how empowerment is currently being measured. They have some useful specific suggestions for improving the current methods. Perhaps there will be some real traction here, as Glennerster was named the new Chief Economist of DfID this week.
Bonus Overflow Links: David McKenzie has a post about re-interviewing participants in unrelated evaluations. Kieran Healy is writing a book about Data Visualization for Social Science and posting most of the content as far as I can tell.
Week of August 7, 2017
This week's faiV is a fun change of pace of just visualizations & graphics - click through to see.
1. The Global Middle Class: By now, Branko Milanovic's elephant chart should be quite familiar. Nancy Birdsall of CGD has a new post about the state of the global middle class that delves into the elephant chart and other data looking at the state of the middle class globally.
2. Global Inequality: Another chart that may be somewhat familiar but certainly should be top of mind these days. Our World in Data looks at inequality, from a lot of perspectives, here before and after taxes and benefits in developed countries.
3. US Inequality (and Debt): Speaking of inequality before and after redistribution, Catherine Rampell at the Washington Post has a couple of interesting recent posts on policy to help (or not) lower-income workers. The first chart here made lots of waves this week in a post by David Leonhardt, and provides the visceral oomph behind the need to reassess policy in the US. Although this data and similar charts have been circulating for quite awhile, it still thankfully grabs attention.
Whether or not the top chart is related to the bottom chart is one of the questions that Aspen's EPIC is taking on this year. Regardless of the direct connection between income inequality and rising debt, the fact that we are back to record levels of credit card debt seems concerning since it's likely not the .001 percent taking on this debt. That being said, rising debt could also be a sign that finally consumer confidence is returning and people feel that their incomes may start rising again.
4. Statistics GIFS: You can't say I don't know my audience--you guys go crazy for things like this, at least that's what the click data says. The two images at the top are from Rafael Irizarry at Simply Stats, in a post about teaching statistics and how to think about data. Helpfully, the post includes the code to recreate each of the images (and he's got a lot more where these came from).
This week there was also a revival of the Autodesk post about how visualizations can mislead that I featured a while back. It's here again because Jeff Mosenskis of IPA made an underappreciated awesome joke about also being wary of violin plots.
5. Low Quality Equilibria: I couldn't pass this one up when I saw it this week, given my recent rants. Who knew that removing frictions from sharing market information would make it impossible to ever tell if any product was good or not?
Week of August 1, 2017
1. More Ranting (Low-Quality Equilibria and Digital Currency): Following up on my rant last week about the prevalence of low-quality or sub-optimal equilibria because people have such a hard time figuring out what matters, here's another paper that caught my attention because it so thoroughly confirms my priors. The basics: a field experiment provided repair technicians with varying amounts and frequency of feedback. Performance suffered when feedback was weekly versus monthly because the technicians overreacted to each report. In other words, they had a hard time figuring out which details mattered to their own performance. The study could inspire another about "isomorphic mimicry" and the technology of management but I'll save that for another time.
Instead, I'll move on to a different rant about digital finance. In my world, there's only a tenuous connection between the digital finance groups and the cryptocurrency (e.g. BitCoin) groups, but the former certainly should be paying attention to the latter. As Matt Levine put it this week (again, he says this a lot): "The job of the cryptocurrency revolutionaries is to re-learn all of the old lessons of modern finance, one at a time, in public, in embarrassing ways." Right now those old lessons being re-learned seem particularly focused on how hard it is to manage and secure a money supply. I really hope that the digital finance advocates are paying attention to how often various "unhackable" and "secure" cryptocurrencies are being hacked. The spirit of Willie Sutton lives on, and as more "money" is stored in digital form, there will inevitably be more theft. And there's very little reason to believe that average users will employ security practices better than the supposed sophisticated users currently adopting cryptocurrencies. I fear though that the fate of much of digital finance is to "re-learn all of the old lessons of financial services, one at a time, in public, in especially embarrassing ways because they ignored the cryptocurrency movement's repeated mistakes."
2. Global Development (rants): On to more traditional faiV-ing. Kevin Starr has a new rant on the many outside groups making hay over government-funded private schools in Liberia (We need a hashtag to go along with #lantrant, I'm proposing #starrant). Someone once told me there were a lot less education experiments in the US than in other countries because more people were paying close attention and fighting any policy experiments where the outcomes were not already known. That may have been true, but it's certainly not true anymore in Liberia at least. Kevin's plea is to let the Education Minister do his job.
And here's a rant (with a link to another) against the "getting better" narrative that points out how much the world has improved, to the point where it is certainly the best time in history to be alive. I find the argument here pretty annoying, but not annoying enough to rant about myself. Pointing out that fewer children are dying of malnutrition and more people can read (for instance) in no way implies "this is fine."
In fact it's far more common for the "getting better" crowd to argue for more and for taking risks to make more progress, rather than settling for the status quo as Kottke says they are. In that vein, philosopher Peter Singer is probably the best known advocate for doing more, particularly associated with the "drowning child" thought experiment. Except it's not always an experiment. Last week, French philosopher Anne Dufourmantelle died while trying to rescue some actual drowning children. She was particularly known for her work on taking risks.
Week of July 24, 2017
1. Low Quality Equilibria: There's an important "new" (e.g. it's been circulating in working paper form for a while, but is now published) paper in QJE about why hobby woodworkers waste so much money...just kidding, it's about why people keep buying cheap Chinese knock-off tech products and IKEA furniture...actually it's about the persistent use of predatory financial products and poor financial decision making...OK, it's really about the bind that the evidence-based policy movement finds itself in. Well, truthfully it's actually about agricultural markets in Uganda and why adoption rates of fertilizer and improved seed are low, but not zero. Really, that's what the paper is about.
But it is also about all of those other things. Here's the basic story:
Fertilizer and improved seeds boost agricultural productivity substantially. But it's hard for farmers to tell whether the fertilizer or seeds they are buying are fake. So there are lots of people willing to sell low quality stuff claiming it's high quality--in Uganda, the fertilizer is regularly diluted (30% of nutrients are missing) and the "improved seed" is fake 50% of the time. Classical economics tells us that markets will drive out the low quality products as people learn who is a reliable seller; or that the market will collapse and no one will be willing to buy the fertilizer or seeds at all. But farming, like almost every other human endeavor depends on lots of factors, not just these inputs. And so it's not only hard for farmers to tell whether they were sold a "lemon" even even after using it. Did their crops underperform because the were sold fake inputs or because the weather was bad, or they used it wrong, or their land was too degraded, or their were too many of a certain kind of pest, or because they were sick during the planting season, etc.? After all some people buying the fertilizer and seeds did get good stuff and have high yields, so it's even harder to tell where the problem lies. So the market doesn't collapse, and low-quality sellers/products don't get driven out of the market but farmers also--for good reason!--don't invest in the inputs as much as would make sense based on the theoretical productivity boost.
Here's where the rant, and the weird introduction to the item, comes in. This situation is incredibly common: in most of life it's hard to tell whether some input--be it technology, or practice, or advice, or an employee--is high quality before you use it, but also after you use it because of the complex nature of most of life. This basic fact seems to be ignored frequently as researchers, policymakers, and advocates try to explain behavior. In almost all our endeavors we are in a Dunning-Krueger low quality equilibrium. We don't know enough to tell high quality from low quality ex ante, or ex post (yes, I'm a Calvinist). Determining causality is hard--even the most highly trained economists and social scientists get it wrong all the time! What hope does the average human have of looking at a complex system and determining which of the hundreds of factors involved was responsible for what portion of the outcomes? Behavioral economics explanations for sub-optimal choices are tempting because they tend to skirt this core issue. True, cognitive biases and limited attention exacerbate these problems and nudges can yield improvement on the margin, but figuring out what matters is hard (an opportunity to link, yet again, to one of my favorite papers, [Not] Learning by Noticing [the wrong things].
This is why Amazon or any crowdsourced product reviews are worthless. And it's why most people, regardless of their financial literacy, can't consistently tell which financial products are good for them and their situation. And it's why evidence-based policy is such a hard sell--when a policy with strong evidence behind it fails to live up to expectations is that because the advice was bad, the implementation was bad or circumstances changed?
Low quality equilibria are everywhere, defeating them is hard, and that's the sobering challenge we face.
Week of July 17, 2017
1. Weaponized Data and American Inequality (Part 3): We learned a lot in reading the faiV’s summary and corresponding links detailing the minimum wage debate consuming economists across the country. While we haven’t reached our own conclusion about whether a $13 minimum wage in Seattle is or isn’t too high, we are following how some state legislatures across the country are actively rolling back minimum wages established by municipal governments. Example? St. Louis was dealt a big blow and the city has received a lot of press this summer.
(ICYMI the debate, here and here are the two papers that offer opposing outcomes of Seattle’s minimum wage increase. If you don’t have time to read the papers, here’s a fun breakdown from Vice.)
2. Living for the City: CityLab profiled recent research on the intersection of urban development and economic inequality, making us think back to Stevie Wonder’s “Living for the City.” Still relevant. And beautiful. A new study out of the University of Idaho looks at 639 urban counties in the US and the factors that determined when they felt the effects of the 2006-2010 recession. Rarely do we see the Gini coefficient being used in the context of domestic inequality – but we should use this metric more often. Consequently, we were really excited to see this interactive map of the Gini coefficients of counties across the US.
For more on cities, another CityLab piece looks at how housing policies worldwide will only exacerbate urban inequality and housing crises. And this story on how inefficient tax codes, high cost of living, and migration, by both companies and residents, are sending the state of Connecticut spiraling, makes us rethink how we view the fiscal policies of traditionally blue, wealthy states.
3. Income Volatility, Short-Term Savings, Retirement (Oh My): Over the last 18+ months, our team has conducted a deep dive on both the impact income volatility – large fluctuations in week-to-week and month-to-month income – has on US households and potential solutions for mitigating the problem. Our latest briefs look at the role wage insurance could play in helping families cope with job loss or reduced wages and how shortfall savings can serve as a buffer during financial emergencies.
Because we care about both short-term financial stability and long-term security, we also spend our days thinking about comprehensive policy solutions to help expand access to retirement savings opportunities. In our process learning about more about income volatility, we’ve realized it’s particularly hard to save for the long-term when short-term savings are lacking. This new paper looks at the effect income shocks have on retirement savings (the stats aren’t pretty: “96 percent of Americans experience four or more income shocks by the time they reach 70”), and *mark your calendars* later this fall, we’ll be publishing two papers on how volatility affects retirement savings.
Week of June 26, 2017
1. Weaponized Data and American Inequality: Last week I linked to a paper finding minimal effects from minimum wage increases, unaware that a huge explosion of debate on this issue was about to occur. If you follow these things at all, you know that last Friday a paper on Seattle's minimum wage increase was released finding no job losses or cuts in hours. Monday, a different paper finding large losses for households with minimum wage jobs was released. There's a whole lot out there now on the two papers so I'm not going to rehash those arguments (if you need to catch up, try this or this or this or just scroll through Twitter). I want to focus on the backstory of why there were two papers released so close to each other because it's important for the future of research and policy-making. As detailed here, what appears to have happened is researchers at UW shared an early draft of their paper (using tax data that is rarely available in minimum wage studies) with the Seattle mayor's office. The mayor's office didn't like the conclusions so asked a different set of researchers to write their own paper--and release it just before the planned date for release of the UW paper. While I have no special insight into the exact details of what happened, the prospect that the report is accurate disturbs me a great deal. It's a blatant step toward what the author of the Seattle Weekly piece calls "weaponized data." Be afraid for evidence-based policy. Very afraid.
In other American inequality news on topics that yield strong confirmation bias reactions, Justin Fox reports on new work suggesting that occupational licensing actually crowded-in historically disadvantaged workers--seemingly the transparent rules of licensing reduced formal and informal discrimination that kept these groups underemployed. That's a very plausible story to me, though I generally also buy the anti-licensure arguments.
There's also new work on school vouchers, from Indiana, finding short-term declines in test scores, but later (over four years) gains. It's worth noting how claims for vouchers have down-shifted to "no harm and some students gain." But keeping on the weaponized data theme, the paper is not publicly available and was only obtained by ChalkBeat through public records requests. Apparently the study authors don't think it should be public until it's peer-reviewed, which illustrates the difference in norms in sociology and economics.
2. Our Algorithmic Overlords: Also a few weeks ago I linked to a story about how to tell if borrowers on online lending platforms were going to default, and to the book, Everybody Lies, from which it came. I said I was going to read the book and I started this week--and was immediately dismayed. The opening of the book discusses what search data--particularly searches on pornography websites--can tell us about Americans' hidden desires. You can see a summary in this deeply disappointing Vox piece (isn't Vox supposed to be better at thinking critically about this stuff?). There is no discussion of how such data might be biased or inaccurate, how a site's interface may interact with what people search for, or why we should believe that search data closely corresponds to "real life." In other words, it's an object lesson in the dangers of using data and algorithms without understanding the data or the people, social structures and institutions that generate it. So of course it's a best seller. Suffice it to say that I have radically revised down my faith in any of the book's conclusions.
In other data-generating processes of uncertain usefulness news, Google will stop showing ads inside Gmail based on scans of email content (illustrating the sucker's game that is attention, I had no idea they were still doing this; I hadn't noticed an ad in years). The nominal reason is combating hesitance from corporates to adopt Gmail and Google's suite of web apps. As someone in my Twitter feed noted, the real reason is that Google already gets better information to drive ads to you than your email.
Week of June 19, 2017
1. Indebtedness: A few weeks ago I mentioned the wave of agricultural loan waivers in a variety of Indian states, a pattern that has been repeated over decades (and not just in India; and perhaps I should say repeated over millennia) with all sorts of moral hazard implications for lenders and borrowers (here's Xavi Gine explaining the impact of the 2008 agricultural debt relief program). Shamika Ravi looks at data from the current round of farmer distress examining how poverty, indebtedness and political power interact since straightforward explanations don't hold up to scrutiny.
2. Our Algorithmic Overlords (and some Data Viz): Sometimes it's helpful to take a step back and see where artificial intelligence is still struggling. Reassuringly while AI can negotiate it still produces aphorisms like: Death when it comes will have no sheep. But maybe that's a negotiating tactic? Meanwhile, apparently machine learning still struggles to tell the difference between labradoodles and fried chicken (I suppose that would be more frightening than funny to chickens and labradoodles).
And while not about algorithms, here's another one of those cool illustrations of how data visualization influences how we interpret data that are so popular.
3. American Inequality: One of the clear themes of recent research on poverty and inequality in the United States is the rise of month-to-month and year-to-year volatility of incomes, while real wages have stagnated. The safety net in the US, such as it is, is especially unable to deal with income volatility. Here's the story of a family in Texas with volatile income who has adopted a number of medically fragile children: because of the way the state administers Medicaid the family has to re-certify eligibility almost every month. While this is somewhat unusual, the language of the Senate Republicans healthcare/Medicaid legislation would enable states to require all recipients to re-certify eligibility monthly.
Meanwhile here's Cengiz, Dube, Lindner and Zipperer with a new look at the perpetual question of what raising minimum wages does to jobs, finding little evidence for job losses or labor substitution. And here's a piece from HBR on the household effects of unstable work.
Week of June 12, 2017
1. St. Monday, American Inequality and Class Struggle: One of my favorite things about writing the faiV is when I get the chance to point readers to something they would likely never come across otherwise. So how about a blog post from a woodworking tool vendor about 19th century labor practices, craft unions and the gig economy? Once you read that, you'll want to remind yourself about this piece from Sendhil Mullainathan about employment as a commitment device (paper here), and this paper from Dupas, Robinson and Saavedra on Kenyan bike taxi drivers' version of St. Monday.
Back to modern America, here's Matt Bruenig on class struggle and wealth inequality through the lens of American Airlines, Thomas Picketty and Suresh Naidu. I feel a particular affinity for this item this week having watched American Airlines employees for a solid 12 hours try to do their jobs while simultaneously giving up the pretense that they have any idea what is going on.
2. Our Algorithmic Overlords: Facebook is investing a lot in machine learning and artificial intelligence. Sometimes that work isn't about getting you to spend more time on Facebook...or is it? With researchers at Georgia Tech, Facebook has been working on teaching machines to negotiate by "watching" human negotiations. One of the first things the machines learned was to "deceive." I use quotes here because while it's the word the researchers use, I'm not sure you can use the word deceive in this context. And that's not the only part of the description that seems overly anthropomorphic.
Meanwhile, Lant Pritchett has a new post at CGD that ties together Silicon Valley, robots, labor unions, migration and development. And probably some other things as well. If I read Lant correctly, he would approve of Facebook's negotiating 'bots since negotiation is a scarce and expensive resource (though outsourcing negotiation is filled with principal-agent problems). I guess that means a world where robots are negotiating labor contracts for low- and mid-skill workers would be a better one than the one we're currently in?
3. Statistics, Research Quality and External Validity: Here's another piece from Lant on external validity and multi-dimensional considerations when trying to systematize education evidence. A simpler way to put it: He's got some intriguing 3-dimensional charts that allow for thinking a bit more carefully about likely outcomes of interventions, given multiple factors influence how much a child learns in school. It closely parallels some early conversations I've had for my next book with Susan Athey and Guido Imbens, so I'm paying close attention. And if you can't get enough Lant, you could always check out my current book. Yes, both of those sentences are shameless plugs.
Week of June 5, 2017
1. Social Enterprise: A few weeks ago I noted that Etsy was under pressure from an activist investor for behaving like a B Corp (which it is (was?)). I missed the notice that the investor won: Etsy layed off 80 employees and fired the CEO/Chairman. Here's a piece reflecting on the Etsy saga that is emblematic of much of what I think is wrong in social enterprise rhetoric. The argument that social enterprises have to be ruthless competitors may sound good (to some) but it ignores the exact issue that is at the heart of social enterprises: how do you manage the trade-offs. It's worthless--less than worthless, I should probably say "actively harmful"--to pretend there are no trade-offs or to imply that there is value in advice like "be ruthlessly competitive except for in these parts of your business model." It's why efforts like B Corporations that don't have any governance teeth are a distraction, and why even efforts life For Benefit Corporations that do have governance teeth are fraught.
In other social enterprise-ish news, I can't resist a story about a star rapper, off-grid solar power in Senegal and Chinese investors. You can't either can you? On a more practical level here's Devanshi Vaid on the lack of information flow on social enterprise in India.
And here's Felix Salmon with some remarkably clear reframing of an important wing of social investment: if a foundation endowment can't get high investment returns in the near term, don't cut back on grantmaking, accelerate it!
2. Our Algorithmic Overlords: The Atlantic has a long piece on how cryptocurrencies like Bitcoin, purportedly designed to limit centralized authority, actually can become tools of authoritarianism. You don't have to go all the way to cryptocurrencies though, as I try to frequently point out. Digital currency of any sort can easily become weaponized by authority, even authority that isn't fully authoritarian.
I wasn't sure whether to include this in "Social Enterprise" or "Our Algorithmic Overlords" because it's a bit of both, through an extraordinary lens: Venezuela's bonds. As Matt Levine relates, Goldman Sachs (sort-of) bought some bonds from Venezuela (sort-of) that (sort-of) prop up an authoritarian government apparently bent on starving people. But no one is really responsible for this decision because of the way governance of the investment funds is set-up and which all point back to an index by which fund manager performance is measured. (I know, this is confusing and complicated, but it's worth it). In this case everyone is pointing to some arbitrary set of decisions as responsible for their behavior and denying any responsibility for moral judgment. If we struggle with these issues already, how much worse are they going to get with the arbitrary set of decisions are made by an algorithm that we don't really understand?
But people are more worried about algorithms driving their cars, than about algorithms ruling their moral decisions.
