Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study algorithms for simple linear regression that satisfy differential privacy, a constraint which guarantees that an algorithm's output reveals little about any individual input data record, even to an attacker with arbitrary side information about the dataset. We consider the design of differentially private algorithms for simple linear regression for small datasets, with tens to hundreds of datapoints, which is a particularly challenging regime for differential privacy. Focusing on a particular application to small-area analysis in economics research, we study the performance of a spectrum of algorithms we adapt to the setting. We identify key factors that affect their performance, showing through a range of experiments that algorithms based on robust estimators (in particular, the Theil-Sen estimator) perform well on the smallest datasets, but that other more standard algorithms do better as the dataset size increases.

Many of the world's most important fisheries are experiencing increases in illegal fishing, undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending illegal, unreported, and unregulated (IUU) fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time-consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict illegal fishing on the Patagonian Shelf, one of the world's most productive regions for fisheries. Specifically, we focus on Chinese fishing vessels, which have consistently fished illegally in this region. We combine vessel location data with oceanographic seascapes -- classes of oceanic areas based on oceanographic variables -- as well as other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a Chinese vessel is operating illegally with 69-96% confidence, depending on the year and predictor variables used. These results offer a promising step towards preempting illegal activities, rather than reacting to them forensically.

Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research. In this paper we explore a special type of dataset shift which we call class-dependent domain shift. It is characterized by the following features: the input data causally depends on the label, the shift in the data is fully explained by a known variable, the variable which controls the shift can depend on the label, there is no shift in the label distribution. We define a simple optimization problem with an information theoretic constraint and attempt to solve it with neural networks. Experiments on a toy dataset demonstrate the proposed method is able to learn robust classifiers which generalize well to unseen domains.

The FBI and Justice Department are assisting the Altoona Police Department's investigation after a noose was found last month at a work site on the Facebook Data Center property in Altoona, Iowa. Altoona police officials say they contacted the FBI on June 19, the day the noose was found. The date coincided with Juneteenth, the annual holiday celebrating the end of slavery. Interviews are still being conducted in the investigation, according to Altoona Police Department Public Information Officer Alyssa Wilson. While federal investigators were already involved with the incident, as of Thursday, all information in the case will be filtered through the FBI's Omaha office.

We've got the roads, the rail and the airport to keep growing this nation, keep getting those products out of the warehouses and into people's shops and into people's homes," he said. Amazon's new hub is a "boost for this community," said NSW Premier Gladys Berejiklian. "People won't need to travel those longer distances to get the best jobs available. They'll be able to live and work near their communities, which is exactly what we want," Berejiklian said. Other retailers in Australia are gearing up for an increase in automation in their own logistics.

The Pentagon's artificial intelligence hub is shifting its focus to enabling joint war-fighting operations, developing artificial intelligence tools that will be integrated into the Department of Defense's Joint All-Domain Command and Control efforts. "As we have matured, we are now devoting special focus on our joint war-fighting operation and its mission initiative, which is focused on the priorities of the National Defense Strategy and its goal of preserving America's military and technological advantages over our strategic competitors," Nand Mulchandani, acting director of the Joint Artificial Intelligence Center, told reporters July 8. "The AI capabilities JAIC is developing as part of the joint war-fighting operations mission initiative will use mature AI technology to create a decisive advantage for the American war fighter." Lt. Gen. Jack Shanahan said May 21 that the JAIC needs the authority to buy its own artificial intelligence technology in order to move fast. That marks a significant change from where JAIC stood more than a year ago, when the organization was still being stood up with a focus on using AI for efforts like predictive maintenance. That transformation appears to be driven by the DoD's focus on developing JADC2, a system of systems approach that will connect sensors to shooters in near-real time. "JADC2 is not a single product.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A racial slur that appeared in the CAPTCHA code on a Georgia Department of Public Health website has sparked an investigation by officials and Microsoft. The slur was flagged by Twitter user @DanieEve, who tweeted a photo of the CAPTCHA code, which is used to determine whether a human or a bot is entering information on the site. "A Georgia Department of Public Health (DPH) website for scheduling COVID-19 tests experienced an issue yesterday when it displayed an offensive computer-generated CAPTCHA code," a spokesperson for the Georgia Department of Public Health told Fox News, via email.

While the COVID-19 crisis got most folks thinking about face masks and toilet paper, Chris Calabrese was pondering artificial intelligence and its implications for public policy. His aha moment came when he realized Facebook had sent home most of its human overseers and put AI in charge of policing the social forum for inappropriate content. "The result has been systems that don't work as well. They are taking down groups dedicated to sewing masks, just because they are falsely flagged," said Calabrese, vice president of policy at the Center for Democracy and Technology. "That's automation being used by one of the most influential companies in the world, and it's still not up to snuff. That gives me a sense of how far we have to go." Facebook's stuttering steps into automation reflect broader ethical challenges faced by public tech leaders as AI, biometrics and surveillance technologies increasingly enter the mainstream.

![Figure][1] GRAPHIC: DAVIDE BONAZZI/SALZMANART Modern governments gather information across an extraordinary range of activities and use this information to direct policy. Whether a central bank monitoring inflation or a health agency monitoring disease, these entities typically publicly disclose the information gathered so that their actions can be reviewed and evaluated by others. But in many respects, the justice system is a glaring exception. In the United States, a range of technical and financial obstacles blocks large-scale access to public court records—all but foreclosing their use to direct policy. Yet a growing body of empirical legal research demonstrates that systematic analyses of court records could improve legal practice and the administration of justice. And although much of the legal community resists quantitative approaches to law, we believe that even the skeptics will be receptive to quantitative feedback—so long as it is straightforward, apolitical, and incontrovertible. We offer an example of this kind of feedback as well as a collaborative research agenda to dismantle access barriers to court records and enable the public to analyze them. Although court records in the United States sit in the public domain, federal courts charge $0.10 per printed page to view any record online ([ 1 ][2]). Accessing a single case might cost $10 or more. Accessing all cases from a given year would cost millions of dollars ([ 2 ][3]). To be sure, the federal judiciary releases inhouse studies that use federal court records, as well as a database of basic information about each case, such as the subject matter (e.g., tort, contract, civil rights) and disposition (e.g., settled, transferred, jury verdict) ([ 3 ][4]). The federal judiciary has steadfastly refused, however, to make the underlying public court records freely accessible. Selective access is not the approach taken by the rest of the U.S. federal government: Congressional records are freely available at [congress.gov][5]. Executive agencies' records are freely available at [regulations.gov][6]. It's hard to conceive of a compelling argument for selective access to judicial records that does not apply equally to selective access to congressional records or federal agencies. More to the point, it's hard to conceive of a reason why public records should not generally be accessible to the public. There are some alternative sources for court records, but barriers to systematic analysis remain. Commercial legal services have directly purchased many court records, but they impose their own fees, prohibit bulk downloads, and thus foreclose systematic analysis even for subscribers. Individual judges and commercial services occasionally grant ad hoc fee reductions for research purposes, but these grants are rare, cumbersome to acquire, limited to subsets of the data, and always come with the condition that the underlying records are not disclosed to the public ([ 4 ][7]). An open alternative, Free Law Project , maintains a crowdsourced repository of free court records, but coverage remains too low to support systematic research. The lack of access to court records seemingly undercuts any claim that the courts are truly “open” ([ 5 ][8], [ 6 ][9]). It surely conflicts with researchers' conception of openness. Scientific practice is grounded on a commitment to sharing data and enabling others to replicate findings. But the law's conception of openness is different, a commitment to carrying out public acts in a public space. A scientist might restrict access to a lab and still claim that the research she conducts there is “open.” Closed proceedings in a legal setting, on the other hand, are only tolerated in extraordinary circumstances. Also in contrast to scientific practice, much of the legal profession resists quantitative or evidence-based approaches to improving legal practice and instead prefers to rely on personal experience and professional judgment ([ 7 ][10]). In a recent Supreme Court case challenging the constitutionality of partisan gerrymandering, Chief Justice John Roberts summarily dismissed empirical approaches to gerrymandering as “sociological gobbledygook” that any “intelligent man on the street” would denigrate as “a bunch of baloney” ([ 8 ][11]). Such skepticism is by no means confined to the United States. France, for example, has recently prohibited the publication of any statistical analysis of a judge's or clerk's decisions “with the object or effect of evaluating, analyzing, comparing or predicting their actual or supposed professional practices.” Violators face up to 5 years in prison ([ 9 ][12]). We believe that these differences help explain why the lack of large-scale access to data is not viewed as a priority—or even as a concern—by much of the legal community. The differences in priorities reflect not just commitments to different values but different conceptions of the same values. Yet, if court records are to be truly accessible and evaluable by the public, the legal and scientific communities must cooperate, and appreciate the values that the other holds dear. Access to justice is a fundamental right and the foundation of any fair and legitimate justice system. But how can one quantify and empirically evaluate this concept? Consider court fees. For a litigant without means, court fees are a substantial barrier to the civil justice system. Anyone who files a lawsuit in federal court must pay a $400 filing fee, along with other costs related to litigation such as formal service of the complaint. Litigants in need can file an application to waive court fees, but there is no uniform standard to review these requests ([ 10 ][13]). Application forms differ by district. Most ask the applicant to list sources of income, assets, and cash on hand—and then leave the decision to the judge's discretion. Individual judges thus have considerable power over whether to grant or deny access to the justice system. How do judges exercise this power? This is but one of the myriad questions that is difficult, and arguably impossible, to answer without easy access to structured court records. Even with free access to the data, the answer would be difficult to infer without being able to computationally analyze the text of the court records. In this case, the analysis is straightforward. When a party submits a fee waiver request, the case docket report adds a separate entry for that request, and the textual summary accompanying the entry typically includes some reference to whether the request was granted or denied. We analyzed these entries to compute the grant rate of each federal judge in 2016. Average grant rates naturally differ among federal districts because cases are not randomly assigned to districts. However, once a case is filed in, say, San Francisco, it is then randomly assigned to one of the judges sitting in the federal district that includes San Francisco. Thus, if all judges reviewed fee waiver applications under the same standard, then grant rates should not systematically differ within districts. We find, however, that they do (see the figure). At the 95% confidence level, nearly 40% of judges—instead of the expected 5%—approve fee waivers at a rate that statistically significantly differs from the average rate for all other judges in their same district. In one federal district, the waiver approval rate varies from less than 20% to more than 80%. These findings were recently presented to a group of federal judges who are responsible for amending the rules in their local district. On learning of the inconsistent treatment of fee waiver requests, these judges expressed interest in using our data to improve the decision-making process ([ 11 ][14]). We count this as an early and encouraging validation of our claim that judges will be especially receptive to quantitative feedback that is straightforward, apolitical, and incontrovertible. Going forward, we believe that the best way to provide the judiciary with quantitative feedback is to develop a forum where individuals can collaborate and build on each other's efforts. With this vision in mind, we propose a three-pronged collaborative research agenda to empower the public to access and analyze court records. ### Make court records free In theory, Congress could make federal records free by repealing the laws that authorize the judiciary to charge for access ([ 12 ][15]), or the Judicial Conference of the United States (the policy-making body of the federal judiciary) could stop charging fees. Both Congress and the courts have rejected calls to do so. A principal reason, it seems, is money. About 2% of the federal judiciary's budget comes from online record access fees ($145 million in fiscal year 2019). The judiciary is naturally unwilling to forgo this revenue without a commensurate increase from Congress, and Congress, for its part, is unwilling to increase funding. The stalemate persists because not enough judges, members of Congress, and people realize that this is an issue of legitimacy, not just an issue of money. To break this impasse, we believe that organizations outside government should directly purchase and publicize court records. The most impactful first step is to make docket reports accessible. A docket report is essentially a lawsuit's table of contents. It lists the case title, presiding judge, subject matter of the suit, and information on the plaintiffs, defendants, and their attorneys. A docket report also gives the date that a document was filed, along with a summary of the document that can be analyzed to extract important features of a case. The data for the figure, for example, were constructed by parsing docket reports, not the underlying court records. Though docket reports represent only a fraction of all court records, acquiring them will be expensive. The docket reports used in the figure, which cover all cases filed in 2016, cost more than $100,000. ![Figure][1] Inconsistency in judicial fee waiver decisions Litigants filed 34,001 applications to waive court fees in U.S. federal courts in 2016. For visual simplification, we show only the 294 judges (out of 1742 total) who ruled on at least 35 applications. We would expect 5% of judges to differ from their within-district peers at 95% confidence. Instead, we find that nearly 40% of judges differ. GRAPHIC: X. LIU/ SCIENCE ### Link data in a knowledge network Because court records are mostly unstructured text, researchers will need to dedicate extensive time and resources to organizing the data. Documents must be analyzed using natural language processing; entities must be disambiguated; and events, such as the filing of a fee waiver, must be classified using machine learning. The docket reports should also be linked to external metadata such as information on judges, litigants, and lawyers. By linking court records to outside data sources, individual users can conduct more powerful searches, such as for litigation against big tech firms or for suits currently pending against the federal government. Although we already have solutions to many of the problems associated with organizing and classifying the data, for many more we will need additional research. For example, it is straightforward to link the presiding judge of each case to outside data on the judge's characteristics such as age, gender, and appointing president. By contrast, to assemble information about litigants and lawyers, researchers will need to make considerable progress on named-entity recognition techniques while protecting litigants' and third parties' privacy. We believe that an open and collaborative platform is the best way to make substantial and rapid progress on these challenges. ### Empower the public The ultimate goal must be to enable the public to directly evaluate and engage with the work of the courts. To this end, we should create applications that not only support scholars and researchers who may want to analyze the data but also enable members of the judiciary, entrepreneurs, journalists, potential litigants, and concerned citizens to learn more about the functioning of the courts. To support inquiries made by the public, we should develop applications that can process natural language queries such as “What are the most recent data privacy cases?” or “How often do police officers invoke qualified immunity?” Funding the efforts we propose will be challenging because the cause does not slot nicely into standard philanthropic categories. To carry out our proposals, the academic community should partner with other stakeholders such as nongovernmental organizations, law firms, legal clinics, and other advocacy groups. Indeed, we believe that one of the main reasons why past calls for change failed is because they were not coordinated. Opening up court records could lead to some flawed or misleading analyses, yet such problems apply to any setting with open data. No one can control what people do with congressional records, federal agency records, census data, etc. Nevertheless, these data are—and should remain—available to everyone. As in any discipline, standards and best practices eventually emerge, and there is already a thriving literature of empirical legal studies. Many scholars have engaged with these data, albeit on a smaller scale. Thus, for the most part, standards and best practices already exist ([ 13 ][16]). We believe that the judiciary should be shielded from outside pressures so that it can decide cases according to the law, not the latest poll. But the judiciary also acts on behalf of the public. Its independence must therefore be balanced with commensurate transparency. Ultimately, the judiciary's principal asset is not its annual appropriation from Congress or the revenue generated by access fees, but the public trust. And the most effective way to cultivate this trust—to promote transparency, dismantle barriers to access ([ 14 ][17], [ 15 ][18]), and build an open knowledge network—is to do it together. 1. [↵][19]Public Access to Court Electronic Records (PACER), “PACER user manual for CM/ECF courts” (United States Courts, 2019). 2. [↵][20]United States Courts, Federal judicial caseload statistics 2018 (2018); [www.uscourts.gov/statistics-reports/federal-judicial-caseload-statistics-2018][21]. 3. [↵][22]1. W. Hubbard , J. Empir. Leg. Stud. 14, 474 (2017). [OpenUrl][23] 4. [↵][24]1. J. B. Gelbach , Yale Law J. 121, 2270 (2011). [OpenUrl][25] 5. [↵][26]1. A. Bronstad , “PACER fees harm judiciary's credibility, Posner says in class action brief,” 25 January 2019; [www.law.com/2019/01/25/pacer-fees-harm-judiciarys-credibility-posner-says-in-class-action-brief/][27]. 6. [↵][28]1. L. Doggett, 2. M. J. Mucchetti , Tex. Law Rev. 69, 643 (1990). [OpenUrl][29] 7. [↵][30]1. H. F. Lynch et al ., Science 367, 1078 (2020). [OpenUrl][31][Abstract/FREE Full Text][32] 8. [↵][33]Gill v. Whitford, Transcript of oral argument at 38 and 40, no. 16-1161, 138 S. Ct. 1916 (2018). 9. [↵][34]1. J. Tashea , “France bans publishing of judicial analytics and prompts criminal penalty,” ABA Journal, 7 June 2019; [www.abajournal.com/news/article/france-bans-and-creates-criminal-penalty-for-judicial-analytics][35]. 10. [↵][36]1. A. Hammond , Yale Law J. 128, 1478 (2018). [OpenUrl][37] 11. [↵][38]Owing to the preliminary nature of discussions, the identities of courts and judges are not reported, but Science has confirmed this claim. 12. [↵][39]28 U.S. Codes §§ 1913, 1914, 1926, 1930, 1932. 13. [↵][40]1. W. Baude et al ., Univ. Chic. Law Rev. 84, 37 (2017). [OpenUrl][41] 14. [↵][42]1. A. Madison , “Team tapped to review PACER amid fee dispute (corrected),” Bloomberg Law, 9 January 2020; . 15. [↵][43]1. A. Kragie , “Court transparency bill calls for live audio, free PACER,” 2 March 2020; [www.law360.com/articles/1249148][44]. Acknowledgments: We thank K. Sanga for valuable feedback. This research was supported by a gift from John and Leslie McQuown and by the National Science Foundation Convergence Accelerator Program under grant no. 1937123. The data and code used for this article, along with full replication instructions and additional discussion of the analyses, are available at and at Zenodo (10.5281/zenodo.3905128). 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I am a scientist. I am an American. And I am the product of special expert visas and chain migration—among the many types of legal immigration into the United States. On 22 June, President Trump issued a proclamation that temporarily restricts many types of legal immigration into the country, including that of scientists and students. This will make America neither greater nor safer—rather, it could make America less so. The administration claims that these restrictions are necessitated by the coronavirus disease 2019 (COVID-19) outbreak to prevent threats to American workers. This reasoning is flawed for science and engineering, where immigrants are critical to achieving advances and harnessing the resulting economic opportunity for all Americans. For decades, the United States has inspired both immigrants and nonimmigrants to make substantial contributions to science and technology that benefit everyone. Preventing highly skilled scientists and postdocs from entering the United States directly threatens this enterprise. My uncle, a geologist, came to the United States in the 1960s to work at NASA. He then taught at Appalachian State University in North Carolina and later served as lead geochemist for the state of California. He sponsored my father to come to America in 1968. Leaving Mumbai, a city of millions, and arriving in Hickory, a town of thousands in North Carolina, my father came home to a place he had never been before. My parents worked in furniture factories and textile mills to put us though college and ensure we had opportunities. Today, my sister works at the U.S. Centers for Disease Control and Prevention, and I have the privilege of leading the American Association for the Advancement of Science (AAAS, the publisher of Science ). We exist because of the Immigration and Nationality Act of 1965 and our parents' belief in the vision of the United States as a shining city on a hill. My family's story is repeated by thousands of American scientists. These stories include uncertainty when an immigrant's status in America is in question. This uncertainty causes stress and the possibility that immigrants will leave and take their skills, talents, and humanity elsewhere. For the successful, these stories culminate with relief, celebration, and the pride of becoming a naturalized citizen. As President Reagan said, the United States is the one place in the world where “anybody from any corner of the world can come…to live and become an American.” Naturalized citizens love the United States deeply because they chose to be American. They and other immigrants make huge contributions to science and engineering. According to the National Science Foundation, more than 50% of postdocs and 28% of science and engineering faculty in the United States are immigrants. Of the Nobel Prizes in chemistry, medicine, and physics awarded to Americans since 2000, 38% were awarded to immigrants to the United States. I don't know the number of prizes given to second-generation Americans but Steven Chu—current chair of the AAAS Board of Directors—is among them. The incredible achievements of the American scientific enterprise speak volumes about the vision and forethought of the American people who have worked to create a more perfect union. Suspending legal immigration is self-defeating and breaks a model that is so successful that other nations are copying it. As Thomas Donohue, chief executive officer of the U.S. Chamber of Commerce, said regarding the administration's proclamation, “Putting up a ‘not welcome’ sign for engineers, executives, IT experts, doctors, nurses, and other workers won't help our country, it will hold us back. Restrictive changes to our nation's immigration system will push investment and economic activity abroad, slow growth, and reduce job creation.” To develop treatments and vaccines for COVID-19, cure cancers, go to Mars, understand the fundamental laws of the universe and human behavior, develop artificial intelligence, and build a better future, we need the brain power of the descendants of Native Americans, Pilgrims, Founding Mothers and Fathers, Enslaved People, Ellis Island arrivals, and immigrants from everywhere. The United States has thrived as a crossroads where people are joined together by ideas and contribute by choice to the freedom and opportunity provided by this wonderful, inspiring, and flawed country that is always striving to live up to its aspirations. Scientists, look around your labs and offices. Think about your collaborations and friendships. We must ensure that this “temporary” restriction on legal immigration does not become permanent. Now is the time to speak up for your immigrant colleagues and for America.