# ↳ Philosophy

## Ideology in AP Economics

When the media talks about ideological indoctrination in education, it is usually assumed to refer to liberal arts professors pushing their liberal agenda. Less discussed is the very different strain of ideology found in economics. The normative import is harder to spot here, as economics presents itself as a science: it provides an empirical study of the economy, just as mechanical engineering provides an empirical study of certain physical structures. When economists offer advice on matters of policy, it’s taken to be normatively neutral expert testimony, on a par with the advice of engineers on bridge construction. However, tools from the philosophy of explanation, in particular the work of Alan Garfinkel, show how explanations that appear purely empirical can in fact carry significant normative assumptions.1 With this, we will uncover the ideology embedded in economics.

More specifically, we’ll look at the ideology embedded in the foundations of traditional economics—as found in a typical introductory micro-economics class. Economics as a whole is diverse and sprawling, such that no single ideology could possibly be attributed to the entire discipline, and many specialized fields avoid many of the criticisms I make here. Despite this, if there are ideological assumptions in standard introductory course, this is of great significance.

## Why Rational People Polarize

U.S. politics is beset by increasing polarization. Ideological clustering is common; partisan antipathy is increasing; extremity is becoming the norm (Dimock et al. 2014). This poses a serious collective problem. Why is it happening? There are two common strands of explanation.

The first is psychological: people exhibit a number of “reasoning biases” that predictably lead them to strengthen their initial opinions on a given subject matter (Kahneman et al. 1982; Fine 2005). They tend to interpret conflicting evidence as supporting their opinions (Lord et al. 1979); to seek out arguments that confirm their prior beliefs (Nickerson 1998); to become more confident of the opinions shared by their subgroups (Myers and Lamm 1976); and so on.

The second strand of explanation is sociological: the modern information age has made it easier for people to fall into informational traps. They are now able to use social media to curate their interlocutors and wind up in “echo chambers” (Sunstein 2017; Nguyen 2018); to customize their web browsers to construct a “Daily Me” (Sunstein 2009, 2017); to uncritically consume exciting (but often fake) news that supports their views (Vosoughi et al. 2018; Lazer et al. 2018; Robson 2018); and so on.

So we have two strands of explanation for the rise of American polarization. We need both. The psychological strand on its own is not enough: in its reliance on fully general reasoning tendencies, it cannot explain what has changed, leading to the recent rise of polarization. But neither is the sociological strand enough: informational traps are only dangerous for those susceptible to them. Imagine a group of people who were completely impartial in searching for new information, in weighing conflicting studies, in assessing the opinions of their peers, etc. The modern internet wouldn’t force them to end up in echo chambers or filter bubbles—in fact, with its unlimited access to information, it would free them to form opinions based on ever more diverse and impartial bodies of evidence. We should not expect impartial reasoners to polarize, even when placed in the modern information age.

## Machine Ethics, Part One: An Introduction and a Case Study

The past few years have made abundantly clear that the artificially intelligent systems that organizations increasingly rely on to make important decisions can exhibit morally problematic behavior if not properly designed. Facebook, for instance, uses artificial intelligence to screen targeted advertisements for violations of applicable laws or its community standards. While offloading the sales process to automated systems allows Facebook to cut costs dramatically, design flaws in these systems have facilitated the spread of political misinformation, malware, hate speech, and discriminatory housing and employment ads. How can the designers of artificially intelligent systems ensure that they behave in ways that are morally acceptable--ways that show appropriate respect for the rights and interests of the humans they interact with?

The nascent field of machine ethics seeks to answer this question by conducting interdisciplinary research at the intersection of ethics and artificial intelligence. This series of posts will provide a gentle introduction to this new field, beginning with an illustrative case study taken from research I conducted last year at the Center for Artificial Intelligence in Society (CAIS). CAIS is a joint effort between the Suzanne Dworak-Peck School of Social Work and the Viterbi School of Engineering at the University of Southern California, and is devoted to “conducting research in Artificial Intelligence to help solve the most difficult social problems facing our world.” This makes the center’s efforts part of a broader movement in applied artificial intelligence commonly known as “AI for Social Good,” the goal of which is to address pressing and hitherto intractable social problems through the application of cutting-edge techniques from the field of artificial intelligence.

## Can you bias a coin?

Challenge: Take a coin out of your pocket. Unless you own some exotic currency, your coin is fair: it's equally likely to land heads as tails when flipped. Your challenge is to modify the coin somehow—by sticking putty on one side, say, or bending it—so that the coin becomes biased, one way or the other. Try it!

How should you check whether you managed to bias your coin? Well, it will surely involve flipping it repeatedly and observing the outcome, a sequence of h's and t's. That much is obvious. But what's not obvious is where to go from there. For one thing, any outcome whatsoever is consistent both with the coin's being fair and with its being biased. (After all, it's possible, even if not probable, for a fair coin to land heads every time you flip it, or a biased coin to land heads just as often as tails.) So no outcome is decisive. Worse than that, on the assumption that the coin is fair any two sequences of h's and t's (of the same length) are equally likely. So how could one sequence tell against the coin's being fair and another not?

We face problems like these whenever we need to evaluate a probabilistic hypothesis. Since probabilistic hypotheses come up everywhere—from polling to genetics, from climate change to drug testing, from sports analytics to statistical mechanics—the problems are pressing.

Enter significance testing, an extremely popular method of evaluating probabilistic hypotheses. Scientific journals are littered with reports of significance tests; almost any introductory statistics course will teach the method. It's so popular that the jargon of significance testing—null hypothesis, $p$-value, statistical significance—has entered common parlance.