Weekly Top 5 Papers – February 25, 2019

1. Adam Smith’s 1759 Rebuke of the Slave Trade by Daniel B. Klein (George Mason University – Department of Economics)

Coming 28 years before the famed 1787 formation of the Society for Effecting the Abolition of the Slave Trade, Smith’s 1759 two-sentence rebuke of the slave trade was not lost on his contemporaries. In 1764, an anonymous anti-slavery pamphlet published in London quotes it in full—and twice.
 
That pamphlet is quoted by Thomas Clarkson in his classic two-volume account, The History of the Rise, Progress, & Accomplishment of the Abolition of the African Slave-trade, by the British Parliament (1808)—the act of abolition having been passed in 1807. Clarkson writes that Adam Smith, one who “promoted the cause of the injured Africans…[,] had, so early as the year 1759, held them up in an honorable, and their tyrants in a degrading light,” and then quotes in full the two sentences of Smith’s rebuke. 

Also quoted in Clarkson’s extensive honor roll are Francis Hutcheson and John Millar. Millar plainly echoes Smith.
 
Also noticed by Clarkson is Benjamin Rush, who in a 1773 pamphlet published in Philadelphia also quotes in full Smith’s two sentences.
 
It is quite clear, then, that Smith’s 1759 rebuke was an inspiration to the early movement against slavery and the slave trade. – Dan Klein

2. Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors by Klakow Akepanidtaworn (University of Chicago Booth School of Business) and Rick Di Mascio (Inalytics Limited ) and Alex Imas (Carnegie Mellon University – Department of Social and Decision Sciences) and Lawrence Schmidt (MIT Sloan School of Management)

3. Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing by Robert D. Arnott (Research Affiliates, LLC) and Campbell R. Harvey (Duke University – Fuqua School of Business) and Vitali Kalesnik (Research Affiliates LLC) and Juhani T. Linnainmaa (USC Marshall School of Business)

4. WACC and CAPM according to Utilities Regulators: Confusions, Errors and Inconsistencies by Pablo Fernandez (University of Navarra – IESE Business School)

5. Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice by Rashida Richardson (AI Now Institute) and Jason Schultz (New York University School of Law) and Kate Crawford (AI Now Institute)

We are at a critical moment in the debate over the use of predictive systems for policing. From the Deputy Attorney General to the Mayor of Baltimore, we’ve seen more public officials turn to predictive policing and other computational techniques both to improve the efficiency use of law enforcement resources – as well as counteract problems of human bias and discrimination. Yet even as these tools are being tested and procured, there continues to be a lack of transparency and public oversight concerning the risks they may pose. While some studies have documented concerns about bias, fairness, and accountability generally, none have looked at the risks of reinforcing bias in jurisdictions where documented corruption and unlawful police practices have potentially influenced the data that is used to “train” the underlying model of crime patterns.

In this paper, we provide the first study looking at these risks. Specifically, we document overlaps between 13 jurisdictions where legal adjudications have found so-called “dirty” police practices alongside public records indicating interest or efforts to develop predictive systems. We then identify various categories of risks that stem from implementing such systems, including the potential for illegal or unethical police practices to create “dirty data” – data that could bias the outputs of the predictive systems and create further bias via feedback loops, data sharing practices, etc. In teasing apart these risks, we identify cases where the link between dirty police practices and dirty data is most direct – for example, in person-based systems such as the Chicago “heat list” – to systems where the link is less certain – for example in place-based systems which focus on the location of potential criminal activity rather than specific people. However, even for place-based systems, findings of systemic corruption in law enforcement raise serious concerns over the use of any data for future predictions – concerns that governments and police technology vendors have done some work to redress but not nearly enough. As discussions on how to best use predictive technologies in public services move forward, we must closely examine the risks that problematic and illegal historical practices create for data systems. It is extremely difficult to weed out the ‘dirty data’ from the ‘clean data.’ Instead, stringent public review and transparent accountability mechanisms are needed to ensure these dark legacies are not perpetuated under the guise of technological progress. – Rashida Richardson, Jason Schultz, and Kate Crawford

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