Algorithmic bias or fairness: the importance of the economic context – Catherine Tucker

MIT Sloan Distinguished Professor of Management and Professor of Marketing Catherine Tucker

From the Shorenstein Center

As a society, we have shifted from a world where policy fears are focused on the ubiquity of digital data, to one where those concerns now center on the potential harm caused by the automated processing of this data. Given this, I find it useful as an economist to investigate what leads algorithms to reach apparently biased results—and whether there are causes grounded in economics.

Excellent work from the discipline of computer science has already documented apparent bias in the algorithmic delivery of internet advertising [1]. Recent research of mine built on this finding by running a field test on Facebook (and replicated on Google and Twitter), which revealed that an ad promoting careers in science, technology, engineering, and math (STEM) was shown to between 20 and 40 percent more men than women across different age groups [2]. This test accounted for users from 190 different countries, with the ad displayed to at least 5,000 eyeballs in each country. In every case, the ad was specified as gender-neutral in terms of who it should be shown to.

When my team and I investigated why it was shown to far more men than women, we found that it is not because men use these internet sites more than women. Nor is it because women fail to show interest or click on these types of ads—thereby prompting the algorithm to respond to a perceived lack of interest. (In fact, our results showed that if women do see a STEM career ad, they are more likely than men to click on it.) Nor does it seem to echo any cultural bias against women in the workplace. The extent of female equality in each of the countries as measured by the World Bank was found to be empirically irrelevant for predicting this bias.

Instead, we discovered that the reason this variety of ad is shown to more men than women is because other types of advertisers actually seem to value the opportunity to get their ads in front of female (rather than male) eyeballs—and they’ll spend more to do it. Some advertisers’ willingness to pay more to show ads to women means that an ad which doesn’t specify a gender target is shown to fewer women than men. In essence, the algorithm in this case was designed to minimize costs and maximize exposure, so it shows the ad in question to fewer expensive women than what amounts to a greater number of relatively cheaper men.

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A judicial whodunnit: Shedding light on unsigned opinions — Andrew Lo

MIT Sloan Professor Andrew Lo

From WBUR Cognoscenti

Within legal circles, the mystery of “Whodunnit?” has increasingly become “Who wrote it?” as courts, including the U.S. Supreme Court, keep issuing opinions without divulging who actually authored them. Since 2005, for example, the Roberts Court has disposed of at least 65 cases through unsigned per curiam opinions. Many cases also came with unsigned concurring or dissenting opinions.

We place a high value on transparency in our democracy, and that should certainly apply to Supreme Court justices, who, after all, are already protected by lifetime tenure. Obscuring authorship removes the sense of judicial accountability, making it harder for experts and the public alike to understand how important issues were resolved and the reasoning that led to these decisions, especially in controversial cases. We’ve all heard the charge that judges are legislating from the bench — but assessing that claim requires, at the least, the ability to link opinions to individual decision makers.

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Facebook IPO and beyond: Catherine Tucker sees rich new revenue source in social advertising

MIT Sloan Assoc. Prof. Catherine Tucker

Much of the attention on Facebook’s initial public offering this week has been on whether the social networking giant is valued too highly. But whatever its current worth, Facebook has a potentially huge new source of revenue coming its way from “social advertising.” According to a new research paper I’ve just published, Facebook itself is only just beginning to realize the untapped potential of social advertising, in which marketers use online social relationships to improve ad targeting using data on Facebook users’ friend networks.

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How to improve products? Survey consumers with "active machine learning"

MIT Sloan Prof. John Hauser

When you buy a house, it would be irrational to search every possible house on the market. Instead, you narrow down your choices based on things like price, location, and number of bedrooms. The same thing happens when you buy a car. You might only look at sporty coupes or hybrid vehicles. Everyone has their own individual methods – or heuristic decision rules — for screening products, usually based on the item’s key features.

This presents a significant question for companies:  How do you determine what these decision rules are? Managers are increasingly interested in this topic as companies focus product development and marketing efforts to get consumers to consider their products or prevent them from rejecting the products without evaluation. If they better understood consumers’ heuristic decision rules, they could use this information in the design and marketing of new products.

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