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[Katariina Washuk]

The tax law enacted under former President Donald Trump, which lowered the statutory tax rate from 35 percent to 21 percent, has been in effect for four years, and Netflix has reported current federal corporate income tax rates of either 1 percent or nothing in each of those years. This outcome will be very unlikely for Netflix to replicate in the future if Congress enacts the minimum corporate tax provision included in the Build Back Better Act passed by the House of Representatives in November.

These diverse tax breaks have one thing in common: they are each perfectly legal. The most obvious legislative response to this would be to repeal corporate tax breaks that do not benefit society enough to justify their costs. The corporate minimum tax provision in the Build Back Better Act passed by the House in November takes an indirect approach but would still raise hundreds of billions of dollars by blocking significant tax avoidance. It would require the largest corporations to pay 15 percent of the profits they report to potential investors if that is more than they would pay under the normal tax rules.

Under current rules, corporations use various tax breaks to report little or no income to the IRS even as they report hefty profits in their financial statements that they make public to inform shareholders and potential investors. Corporations might try to avoid the minimum tax by manipulating their financial statements, but this would mean they are reporting lower profits to potential investors, something they have strong incentives to avoid.

If some form of this minimum tax had been in effect for 2021, Netflix would almost certainly have paid a higher tax rate than 1 percent. A recent report from the office of Senator Elizabeth Warren estimates that Netflix would likely see its federal income tax bill boosted substantially under the minimum tax.

The story of the Netflix Prize differs from traditional diversity narratives in which a single talented individual, given an opportunity, creates a breakthrough because of some idiosynchratic piece of information. Instead, teams of diverse, brilliant people competed to attain a goal. The contest attracted thousands of participants with a variety of technical backgrounds and work experiences. The teams applied an algorithmic zoo of conceptual, computational, and analytical approaches. Early in the contest, the top ten teams included a team of American undergraduate math majors, a team of Austrian computer programmers, a British psychologist and his calculus-wielding daughter, two Canadian electrical engineers, and a group of data scientists from AT&T research labs.

In the end, the participants discovered that their collective differences contributed as much as or more than their individual talents. By sharing perspectives, knowledge, information, and techniques, the contestants produced a sequence of quantifiable diversity bonuses.

Winning the Netflix Prize required the inference of patterns from an enormous data set. That data set covered a diverse population of people. Some liked horror films. Others preferred romantic comedies. Some liked documentaries. The modelers would attempt to account for this heterogeneity by creating categories of movies and of people.

To understand the nature of the task, imagine a giant spreadsheet with a row for each person and a column for each movie. If each user rated every movie, that spreadsheet would contain over 8.5 billion ratings. The data consisted of a mere 100 million ratings. Though an enormous amount of data, it fills in fewer than 1.2 percent of the cells. If you opened the spreadsheet in Excel, you would see mostly blanks. Computer scientists refer to this as sparse data.

The contestants had to predict the blanks, or, to be more precise, predict the values for the blanks that consumers would fill in next. Inferring patterns from existing data, what data scientists call collaborative filtering, requires the creation of similarity measures between people and between movies. Similar people should rank the same movie similarly. And each person should rank similar movies similarly.

One might think that including more features would lead to more accurate predictions. That need not hold. Models with too many variables can overfit the data. To guard against overfitting, computer scientists divide their data into two sets: a training set and a testing set. They fit their model to the first set, then check to see if it also works on the second set.[2] In the Netflix Prize competition, the size of the data set and the costs of computation limited the number of variables that could be included in any one model. The winner would therefore not be the person or team that could think up the most features. It would be the team capable of identifying the most informative and tractable set of features.

Given a feature set, each team also needed an algorithm to make predictions. Dinosaur Planet, a team of three mathematics undergraduates that briefly led the competition in 2007, tried multiple approaches, including clustering (partitioning movies into sets based on similar characteristics), neural networks (algorithms that take features as inputs and learn patterns), and nearest-neighbor methods (algorithms that assign numerical scores to each feature for each movie and compute a distance based on vectors of features).

At the end of the first year, a team from AT&T research labs, known as BellKor, led the competition. Their best single model relied on fifty variables per movie and improved on Cinematch by 6.58 percent. That was just one of their models. By combining their fifty models in an ensemble, they could improve on Cinematch by 8.43 percent.

A year and a half into the competition, BellKor knew they could outperform the other teams, but also that they could not reach the 10 percent threshold. Rather than give up, BellKor opted to call in reinforcements. In 2008, they merged with the Austrian computer scientists, Big Chaos, a team that had developed sophisticated algorithms for combining models. BellKor had the best predictive models. Big Chaos knew better ways to combine them. By combining these repertoires, they produced a diversity bonus. However, that bonus was not sufficient to push them above the 10 percent threshold.

Although, not yet. They had to wait. To safeguard against the possibility that 10 percent would prove too easy, the organizers wrote the rules so that the contest would end thirty days after a team passed the threshold. Had the threshold been 5 percent, a level that was bested a mere six days into the contest, this decision would have been prescient. As events unfolded, this delay seemed unnecessary.

It was not. The fun had only begun. As if drawn from the script of Jurassic Park, the dinosaurs came roaring back. And they brought reinforcements. More than thirty teams, including top performers Grand Prize Team, Opera Solutions, and Vandelay Industries, joined forces with the Dinosaur Planet team to form the Ensemble. Within a few weeks, the Ensemble blended forty-eight models using a sophisticated weighting scheme and took the slightest of leads.

Winning the contest required knowledge of the features of movies that matter most, awareness of available information on movies, methods for representing properties of movies in languages accessible to computers, good mental models of how people rank movies, the ability to develop algorithms to predict ratings, and expertise at combining diverse models into an ensemble. What had begun as a contest to determine the best data scientist became a demonstration of diversity bonuses.

1. Accuracy was measured by the squared distance between the predicted rating and the actual rating. If a person rated The Shawshank Redemption as three stars and a participant's model predicted five stars, the squared error would equal 4.

2. In the Netflix Prize contest, more than 98 percent of the one hundred million rankings went into a training set. The remaining data were divided into several testing sets to determine accuracy and the contest winner.

LISC is expanding its capacity to bridge racial gaps in health and wealth, thanks to a new $25 million investment from Netflix. The media giant is reallocating 2 percent of its cash holdings from global banks to support Black-owned lenders, businesses and institutions that can, in turn, expand access to capital and opportunity in underserved communities.

Earlier this week, Netflix, the online movie rental service, announced it will award $1 million to anyone who can come up with an algorithm that improves the accuracy of its movie recommendation service.

JB: We trained Cinematch on 100 million ratings and asked it to predict what the other 3 million would be. We compared ours with the actual answers. We do that every day. We get about 2 million ratings per day and we track the daily fluctuations of the system. We expect to measure submissions to the contest [the same way]. The actual prize dataset is 103 million ratings, but we only released 100 million of them.

JB: If you go to the website and rate 100 movies for us, the red stars shown under each movie are personalized for you. We use these ratings to adjust the prediction away from the average recommendation, according to your taste. A three-percent difference, for instance, might make a difference of one-quarter star. We have millions of people rating millions of DVDs, and that quarter-star difference helps us sort the list. The individual movie recommendation might not get so much better, but, overall, the set of recommended movies is very different. Move a battleship a little bit, and it makes a huge difference.

Rita Ferro, president of global advertising at Disney, told The Hollywood Reporter in an interview last month that 50 percent of new subscribers to Disney+ are choosing the ad plan. Disney+ also recently introduced programmatic ad inventory.

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