high. Unfortunately, however, such verification software is not designed to verify first that the sensor is operating properly.

Moreover, software developers must learn to be more analytical about the specifications their software is being designed to obey. Making software conform to “some specification,” as Andrew Appel of Princeton University said to Shein, is foolish unless the specification is the right specification. In a large-system context, “right,” as Tatlock said, cannot be assured in advance. Developers must first create the code, then confirm it is consistent with all the other code with which it will eventually inter-operate. Here, “confirm” means “ consistent with the rules of logic, arithmetic, and semantics,” not just some code developer’s specification for only a piece of the ultimate system.

Jack Ring, Gilbert, AZ

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data scientists and researchers who critique data science is a strawman. For example, Cathy O’Neil, whose book Weapons of Math Destruction Barocas and boyd turned to for examples of unethical algorithms, is not just a researcher who happens to focus on data ethics, as they described her, but is herself a data scientist. Researchers like O’Neil would thus seem to be part of the solution to the problem Barocas and boyd identified.

Barocas and boyd also did not mention capitalism—the economic force behind much of today’s data science—with its behavioral prediction and manipulative advertising. What if the answer to unethical algorithms is to not create them in the first place? If an algorithm yields biased results based on, say, ethnicity, as Barocas and boyd mentioned, then surely the answer would be to not develop or use it to begin with; that is, do no harm. But such an approach also means the algorithm writer cannot sell the algorithm, an option Barocas and boyd ignored. They said data scientists try “to make machines learn something useful, valuable…” yet did not identify who might find it useful or what kind of value might be derived. Selling an algorithm that helps “wrongly incarcerate” people has financial value for the data scientist who wrote it but negative social value for, and does major financial harm to, those it might help put in jail. Data scientists must do what “maximizes the…models’ performance” but for whom and to what end? Often the answer is simply to earn a profit.

If one can learn data science, one can should be able to use it ethically. More than once Barocas and boyd mentioned how data scientists “ struggle” with their work but expressed no empathy for those who have been hurt by algorithms. They did say data scientists choose “an acceptable error rate,” though for some scenarios there is no acceptable error rate.

If data scientists do not use data science ethically, as Barocas and boyd wrote and as O’Neil has shown, they are indeed doing it improperly. What Barocas and boyd failed to suggest is such data scientists should not be doing data science at all.

Nathaniel Poor, Cambridge, MA

Why Whole Foods for Amazon

Michael A. Cusumano’s Viewpoint “Amazon and Whole Foods: Follow the Strategy (and the Money)” (Oct. 2017) looked to identify a financial strategy for Amazon.com’s June 2017 acquisition of Whole Foods, which Amazon must have seen as strategically advantageous because it paid far more than Whole Food’s market valuation at the time. However, the column ignored the crucial potential for cross-subsidization to harm competition in the grocery industry. The trade press has since reported sales of high-margin Amazon products, including Echo devices, at Whole Foods, along with deals expected to come later (such as Whole Foods discounts for customers who also purchase Amazon Prime). Using revenue from such products and services to lower the price for commodity, low-margin products like groceries is a classic anti-competitive strategy with dubious legal or ethical basis.

Andrew Oram, Arlington, MA

Address the Slacker Before the Hacker

Esther Shein’s news story “ Hacker-Proof Coding” (Aug. 2017) deserves a clarification and a warning about assumptions. Software developers should recognize that the techniques Shein explored will locate code faults but need not be solely manual, expensive, or tedious and thus error-prone. Contrary to the sources Shein quoted, much of the process of looking for faults can be automated. Automated diagnosis of errors can be done for approximately 20% of what it now costs in terms of programmer time and financial expenditure, yielding immediate and significant ROI.

Regarding assumptions, software developers should also be aware that the threat to the world’s software systems is not just from hackers but also from slackers often within their own midst. Too many software developers simply do not put enough effort into understanding the context in which their code will operate. For example, consider what Zachary Tatlock of the University of Washington said to Shein, “… as software that verifies the beam power has not become too high … ” because software verifies only that some sensor output value is not too

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