The difference between partisans’ responses on retrospection items is highly variable, ranging from over 40% to nearly 0. For instance, in 1988 nearly 30% fewer Democrats than Republicans reported that the inflation rate between 1980 and 1988 had declined. (It had.) However, similar proportions of Republicans and Democrats got questions about changes in the size of the budget deficit and defense spending between 1980 and 1988 right. The median partisan gap across 20 items asked in the NES over 5 years (1988, 1992, 2000, 2004, and 2008) was about 15 points (the median was about 12 points), and the standard deviation was about 13 points. (See the tables.) This much variation suggests that observed bias in partisans’ perceptions depends on a variety of conditioning variables. For one, there is some evidence to suggest that during severe recessions, partisans do not differ much in their assessment of economic conditions (See here.) Even when there are partisan gaps, however, they may not be real (see paper (pdf)).
Freedom of Information Act is vital for holding the government to account. But little effort has gone into building tools that enable fulfilment of FOIA requests. As a consequence of this underinvestment, fulfilling FOIA requests often means onerous, costly work for government agencies, and long delays for the requesters. Here, I discuss a couple of alternatives. One tool that can prove particularly effective in lowering the cost of fulfilling FOIA requests is an anonymizer — a tool that detects proper nouns, addresses, telephone numbers, etc. and blurs them. This is easily achieved using modern machine learning methods. To ensure 100% accuracy, humans can quickly vet the suggestions by the algorithm along with ‘suspect words.’ Or a captcha like system that asks people in developing countries to label suspect words as nouns etc. can be created to further reduce costs. This covers one conventional solution.
Another way to solve the problem would be to create a sandbox architecture. Rather than give requesters stacks of redacted documents – often unnecessary – one can allow people the right to run certain queries on the data. Results of these queries can be vetted internally. A classic example would be to allow people to query government emails for total number of times porn domains are accessed via servers at government offices.
Consider a bank making decisions about loans. For the bank, making lending decisions optimally means reducing prediction errors*(cost of errors) minus the cost of making predictions (Keeping things simple here). The cost of any one particular error — especially, denial of loan when eligible– is typically small for the bank, but very consequential for the applicant. So the applicant may be willing to pay the bank money to increase the accuracy of their decisions. Say, willing to compensate the bank for the cost of getting a person to take a closer look at the file. If customers are willing to pay the cost, accuracy rates can increase without reducing profits. (Under some circumstances, a bank may well be able to increase profits.) Customer’s willingness to pay for increasing accuracy is typically not exploited by the lending institutions. It may be well worth exploring it.
For a class of problems, a combination of algorithms and human input makes for the most optimal solution. For instance, three years ago software to recreate shredded documents that won the DARPA award used “human[s] [to] verify what the computer was recommending.” The insight is used in character recognition tasks. I have used it to create software for matching dirty data — the software was used to merge shape files with electoral returns at precinct level.
The class of problems for which human input proves useful has one essential attribute — humans produce unbiased, if error-prone, estimates for these problems. So for instance, it would be unwise to use humans for making the ‘last mile’ of lending decisions (see also this NYT article). (And that is something you may want to verify with training data.)
“A decision is made about you, and you have no idea why it was done,” said Rajeev Date, an investor in data-science lenders and a former deputy director of Consumer Financial Protection Bureau
The assertion that there is no intuition behind decisions made by algorithms strikes me as silly. So does the related assertion that such intuition cannot be communicated effectively. We can back out the logic for most algorithms. Heuristic accounts of the logic — e.g. which variables were important — can be given yet more easily. For instance, for inference from seemingly complicated-to-interpret methods such as ensemble methods, intuition for what variables are important can be gotten in the same way as it is gotten for methods like bagging. However, even when specific points are hard to convey, the meta-logic of the system can be explained to the end user.
What is true, however, is that it isn’t being done. For instance, WSJ covering Orion routing system at UPS reports:
“For example, some drivers don’t understand why it makes sense to deliver a package in one neighborhood in the morning, and come back to the same area later in the day for another delivery. …One driver, who declined to speak for attribution, said he has been on Orion since mid-2014 and dislikes it, because it strikes him as illogical.”
Communication architecture is an essential part of all human focused systems. And what to communicate when are important questions that deserve careful thought. The default cannot be no communication.
The lack of systems that communicate intuition behind algorithms strikes me as a great opportunity. HCI people — make some money.
55000/(365*4) ~ 37.7. That seems a touch low for Sec. of state.
1. Clinton may have used more than one private server
2. Clinton may have sent emails from other servers to unofficial accounts of other state department employees
Lower bound for missing emails from Clinton:
- Take a small weighted random sample (weighting seniority more) of top state department employees.
- Go through their email accounts on the state dep. server and count # of emails from Clinton to their state dep. addresses.
- Compare it to # of emails to these employees from the Clinton cache.
To propose amendments, go to the Github gist
According to YouGov surveys in Switzerland, Netherlands and Canada, and the 2008 ANES in the US, Whites, on average, in each of the four countries feel fairly coldly — giving an average thermometer rating of less than 50 on a 0 to 100 scale — toward Muslims, and people from Muslim-majority regions (Feelings towards different ethnic, racial, and religious groups). However, in Europe, Whites’ feelings toward Romanians, Poles, and Serbs and Kosovars are scarcely any warmer, and sometimes cooler. Meanwhile, Whites feel relatively warmly towards East Asians.
Note that the x-axis covers a fair bit of time. So even if you were to imagine month or two long lag between the time the scandal breaks and public opinion changes, interpretation doesn’t change.
The median Democrat referred to in television news is to the left of the House Democratic Median, and the median Republican politician referred to is to the left of the House Republican Median.
Click here for the aggregate distribution.
And here’s a plot of top 50 politicians cited in news. The plot shows a strong right skewed distribution with a bias towards executives.
News data: UCLA Television News Archive, which includes closed-caption transcripts of all national, cable and local (Los Angeles) news from 2006 to early 2013. In all, there are 155,814 transcripts of news shows.
Politician data: Database on Ideology, Money in Politics, and Elections (see Bonica 2012).
Taking out data from local news channels or removing Obama does little to change the pattern in the aggregate distribution.