Curated findings and actionable knowledge at the intersection of technology, innovation, and governance
Entrusting ADS to make or to influence such decisions raises a variety of ethical, political, legal, or technical issues, where great care must be taken to
This study reviews the opportunities and risks related to the use of ADS. It presents policy options to reduce the risks and explain their limitations. We sketch some options to overcome these limitations to be able to benefit from the tremendous possibilities of ADS while limiting the risks related to their use. Beyond providing an
In 2017, the U.S. Commission on Evidence-Based Policymaking unanimously recommended that further attention be given to the deployment of privacy-preserving data-sharing applications. If these types of applications can be tested and scaled in the near-term, they could vastly improve insights about important policy problems by using disparate datasets. At the same time, the approaches could promote substantial gains in privacy for the American public.
There are numerous ways to engage in privacy-preserving data sharing. This paper primarily focuses on secure computation, which allows information to be accessed securely, guarantees privacy, and permits analysis without making private information available. Three key issues motivated the launch of a domestic secure computation demonstration project using real government-collected data:
This project used real-world government data to illustrate the applicability of secure computation compared to the classic data infrastructure available to some local governments. The project took place in a domestic, non-intelligence setting to increase the salience of potential lessons for public agencies….(More)”.
Essay by Tim Rogan: “Machine learning – a kind of sub-field of artificial intelligence (AI) – is a means of training algorithms to discern empirical relationships within immense reams of data. Run a purpose-built algorithm by a pile of images of moles that might or might not be cancerous. Then show it images of diagnosed melanoma. Using analytical protocols
Signs of this impending change can still be hard to see. Productivity statistics, for instance, remain worryingly unaffected. This lag is consistent with earlier episodes of the advent of new ‘general purpose technologies’. In past cases, technological innovation took decades to prove transformative. But ideas often move ahead of social and political change. Some of the ways in which machine learning might upend the status quo are already becoming apparent in political economy debates.
The discipline of political economy was created to make sense of a world set spinning by steam-powered and then electric industrialisation. Its central question became how best to regulate economic activity. Centralised control by government or industry, or market freedoms – which optimised outcomes? By the end of the 20th century, the answer seemed, emphatically, to be market-based order. But the advent of machine learning is reopening the state vs market debate. Which between state, firm or market is the better means of coordinating supply and demand? Old answers to that question are coming under new scrutiny. In an eye-catching paper in 2017, the economists Binbin Wang and Xiaoyan Li at Sichuan University in China argued that big data and machine learning give centralised planning a new lease of life. The notion that market coordination of supply and demand encompassed more information than any single intelligence could handle would soon be proved false by 21st-century AI.
How seriously should we take such speculations? Might machine learning bring us full-circle in the history of economic thought, to where measures of economic centralisation and control – condemned long ago as dangerous utopian schemes – return, boasting new levels of efficiency, to constitute a new orthodoxy?
A great deal turns on the status of tacit knowledge….(More)”.
Blog Post by Juan Murillo Arias: “…But in order for data to truly become a lever that foments innovation in benefit of society as a whole, we must understand and address the following factors:
1. Disconnected, disperse sources. As users of digital services (transportation, finance, telecommunications, news or entertainment) we leave a different digital footprint for each service that we use. These footprints, which are different facets of the same polyhedron, can even be contradictory on occasion. For this reason, they must be seen as complementary. Analysts should be aware that they must cross data sources from different origins in order to create a reliable picture of our preferences, otherwise we will be basing decisions on partial or biased information. How many times do we receive advertising for items we have already purchased, or tourist destinations where we have already been? And this is just one example of digital marketing. When scoring financial solvency, or monitoring health, the more complete the digital picture is of the person, the more accurate the diagnosis will be.
Furthermore, from the user’s standpoint, proper management of their entire, disperse digital footprint is a challenge. Perhaps centralized consent would be very beneficial. In the financial world, the PSD2 regulations have already forced banks to open this information to other banks if customers so desire. Fostering competition and facilitating portability is the purpose, but this opening up has also enabled the development of new services of information aggregation that are very useful to financial services users. It would be ideal if this step of breaking down barriers and moving toward a more transparent market took place simultaneously in all sectors in order to avoid possible distortions to competition and by extension, consumer harm. Therefore, customer consent would open the door to building a more accurate picture of our preferences.
2. The public and private sectors’ asymmetric capacity to gather data.This is related to citizens using public services less frequently than private services in the new digital channels. However, governments could benefit from the information possessed by private companies. These anonymous, aggregated data can help to ensure a more dynamic public management. Even personal data could open the door to customized education or healthcare on an individual level. In order to analyze all of this, the European Commissionhas created a working group including 23 experts. The purpose is to come up with a series of recommendations regarding the best legal, technical and economic framework to encourage this information transfer across sectors.
3. The lack of incentives for companies and citizens to encourage the reuse of their data
4. Limited commitment to the diversification of services.Another barrier is the fact that information based product development is somewhat removed from the type of services that the main data generators (telecommunications, banks, commerce, electricity, transportation, etc.) traditionally provide. Therefore, these data based initiatives are not part of their main business and are more closely tied to companies’ innovation areas where exploratory proofs of concept are often not consolidated as a new line of business.
5. Bidirectionality. Data should also flow from the public sector to the rest of society. The first regulatory framework was created for this purpose. Although it is still very recent (the PSI Directive on the re-use of public sector data was passed in 2013), it is currently being revised, in attempt to foster the consolidation of an open data ecosystem that emanates from the public sector as well. On the one hand it would enable greater transparency, and on the other, the development of solutions to improve multiple fields in which public actors are key, such as the environment, transportation and mobility, health, education, justice and the planning and execution of public works. Special emphasis will be placed on high value data sets, such as statistical or geospatial data — data with tremendous potential to accelerate the emergence of a wide variety of information based data products and services that add value.The Commission will begin working with the Member States to identify these data sets.
In its report, Creating Data through Open Data, the European open data portal estimates that government agencies making their data accessible will inject an extra €65 billion in the EU economy this year.
6. The commitment to analytical training and financial incentives for innovation
Press Release: “The Council of Europe has issued a set of guidelines to its 47 member states urging them to ensure, in law and practice, that the processing of health-related data is done in full respect of human rights, notably the right to privacy and data protection.
With the development of new technological tools in the health sector the volume of health-related data processed has grown exponentially showing the need for guidance for health administrations and professionals.
In a Recommendation, applicable to both the public and private sectors, the Council of Europe´s Committee of Ministers, calls on governments to transmit these guidelines to health-care systems and to actors processing health-related data, in particular health-care professionals and data protection officers.
The recommendation contains a set of principles to protect health-related data incorporating the novelties introduced in the updated Council of Europe data protection convention, known as “Convention 108+”, opened for signature in October 2018.
The Committee of Ministers underlines that health-related data should be protected by appropriate security measures taking into account the latest technological developments, their sensitive nature and the assessment of potential risks. Protection measures should be incorporated by design to any information system which processes health-related data.
The recommendation contains guidance with regard to various issues including the legitimate basis for the data processing of health-care data – notably consent by the data subject -, data concerning unborn children, health-related genetic data, the sharing of health-related data by professionals and the storage of data.
The guidelines list a number of rights of data subjects, crucially the transparency of data processing. They also contain a number of principles that should be respected when data are processed for scientific research, when they are collected by mobile devices or when they are transferred across borders….(More)”.
Leo Gringut at the International Policy Digest: “The philosophy behind “Open Data for All” turns on the idea that easy access to government data offers everyday New Yorkers the chance to grow and innovate: “Data is more than just numbers – it’s information that can create new opportunities and level the playing field for New Yorkers. It’s the illumination that changes frameworks, the insight that turns impenetrable issues into solvable problems.” Fundamentally, the newfound accessibility of City data is revolutionizing NYC business. According to Albert Webber, Program Manager for Open Data, City of New York, a key part of his job is “to engage the civic technology community that we have, which is very strong, very powerful in New York City.”
Fundamentally, Open Data is a game-changer for hundreds of New York companies, from startups to corporate giants, all of whom rely on data for their operations. The effect is set to be particularly profound in New York City’s most important economic sector: real estate. Seeking to transform the real estate and construction market in the City, valued at a record-setting $1 trillion in 2016, companies have been racing to develop tools that will harness the power of Open Data to streamline bureaucracy and management processes.
One such technology is the Citiscape app. Developed by a passionate team of real estate experts with more than 15 years of experience in the field, the app assembles data from the Department of Building and the Environmental Control Board into one easy-to-navigate interface. According to Citiscape Chief Operational Officer Olga Khaykina, the secret is in the app’s simplicity, which puts every aspect of project management at the user’s fingertips. “We made DOB and ECB just one tap away,” said Khaykina. “You’re one tap away from instant and accurate updates and alerts from the DOB that will keep you informed about any changes to ongoing project. One tap away from organized and cloud-saved projects, including accessible and coordinated interaction with all team members through our in-app messenger. And one tap away from uncovering technical information about any building in NYC, just by entering its address.” Gone are the days of continuously refreshing the DOB website in hopes of an update on a minor complaint or a status change regarding your project; Citiscape does the busywork so you can focus on your project.
The
Nick Brown at Reuters: “The U.S. Census Bureau has asked tech giants Google, Facebook and Twitter to help it fend off “fake news” campaigns it fears could disrupt the upcoming 2020 count, according to Census officials and multiple sources briefed on the matter.
The push, the details of which have not been previously reported, follows warnings from data and cybersecurity experts dating back to 2016 that right-wing groups and foreign actors may borrow the “fake news” playbook from the last presidential election to dissuade immigrants from participating in the decennial count, the officials and sources told Reuters.
The sources, who asked not to be named, said evidence included increasing chatter on platforms like “4chan” by domestic and foreign networks keen to undermine the survey. The census, they said, is a powerful target because it shapes U.S. election districts and the allocation of more than $800 billion a year in federal spending.
Ron Jarmin, the Deputy Director of the Census Bureau, confirmed the bureau was anticipating disinformation
“We expect that (the census) will be a target for those sorts of efforts in 2020,” he said.
Census Bureau officials have held multiple meetings with tech companies since 2017 to discuss ways they could help, including as recently as last week, Jarmin said.
So far, the bureau has gotten initial commitments from Alphabet Inc’s Google, Twitter Inc and Facebook Inc to help quash disinformation campaigns online, according to documents summarizing some of those meetings reviewed by Reuters.
But neither Census nor the companies have said how advanced any of the efforts are
Colin Wood at StateScoop: “Civilian analysts and officers within the New York City Police Department are using a unique computational tool to spot patterns in crime data that is closing cases.
A collection of machine-learning models, which the department calls Patternizr, was first deployed in December 2016, but the department only revealed the system last month when its developers published a research paper in the Informs Journal on Applied Analytics. Drawing on 10 years of historical data about burglary, robbery and grand larceny, the tool is the first of its kind to be used by law enforcement, the developers wrote.
The NYPD hired 100 civilian analysts in 2017 to use Patternizr. It’s also available to all officers through the department’s Domain Awareness System, a citywide network of sensors, databases, devices, software and other technical infrastructure. Researchers told StateScoop the tool has generated leads on several cases that traditionally would have stretched officers’ memories and traditional evidence-gathering abilities.
Connecting similar crimes into patterns is a crucial part of gathering evidence and eventually closing in on an arrest, said Evan Levine, the NYPD’s assistant commissioner of data analytics and one of Patternizr’s developers. Taken independently, each crime in a string of crimes may not yield enough evidence to identify a perpetrator, but the work of finding patterns is slow and each officer only has a limited amount of working knowledge surrounding an incident, he said.
“The goal here is to alleviate all that kind of busywork you might have to do to find hits on a pattern,” said Alex Chohlas-Wood, a Patternizr researcher and deputy director of the Computational Policy Lab at Stanford University.
The knowledge of individual officers is limited in scope by dint of the NYPD’s organizational structure. The department divides New York into 77 precincts, and a person who commits crimes across precincts, which often have arbitrary boundaries, is often more difficult to catch because individual beat officers are typically focused on a single neighborhood.
There’s also a lot of data to sift through. In 2016 alone, about 13,000 burglaries, 15,000 robberies and 44,000 grand larcenies were reported across the five boroughs.
Levine said that last month, police used Patternizr to spot a pattern of three knife-point robberies around a Bronx subway station. It would have taken police much longer to connect those crimes manually, Levine said.
The software works by an analyst feeding it “seed” case, which is then compared against a database of hundreds of thousands of
Paper by Crystal G. Konny, Brendan K. Williams, and David M. Friedman: “The Bureau of Labor Statistics (BLS) has generally relied on its own sample surveys to collect the price and expenditure information necessary to produce the Consumer Price Index (CPI). The burgeoning availability of big data has created a proliferation of information that could lead to methodological improvements and cost savings in the CPI. The BLS has undertaken several pilot projects in an attempt to supplement and/or replace its traditional field collection of price data with alternative sources. In addition to cost reductions, these projects have demonstrated the potential to expand sample size, reduce respondent burden, obtain transaction prices more consistently, and improve price index estimation by incorporating real-time expenditure information—a foundational component of price index theory that has not been practical until now. In CPI, we use the term alternative data to refer to any data not collected through traditional field collection procedures by CPI staff, including
Blog Post by Annette Zimmermann and Bendert Zevenbergen: “… In what follows, we outline seven ‘AI ethics traps’. In doing so, we hope to provide a resource for readers who want to understand and navigate the public debate on the ethics of AI better, who want to contribute to ongoing discussions in an informed and nuanced way, and who want to think critically and constructively about ethical considerations in science and technology more broadly. Of course, not everybody who contributes to the current debate on AI Ethics is guilty of endorsing any or all of these traps: the traps articulate extreme versions of a range of possible misconceptions, formulated in a deliberately strong way to highlight the ways in which one might prematurely dismiss ethical reasoning about AI as futile.
1. The reductionism trap:
“Doing the morally right thing is essentially the same as acting in a fair way. (or: transparent, or egalitarian, or <substitute any other value>). So ethics is the same as fairness (or transparency, or equality, etc.). If we’re being fair, then we’re being ethical.”
Even though the problem of algorithmic bias and its unfair impact on decision outcomes is an urgent problem, it does not exhaust the ethical problem space. As important as algorithmic fairness is, it is crucial to avoid reducing ethics to a fairness problem alone. Instead, it is important to pay attention to how the ethically valuable goal of optimizing for a specific value like fairness interacts with other important ethical goals. Such goals could include—amongst many others—the goal of creating transparent and explainable systems which are open to democratic oversight and contestation, the goal of improving the predictive accuracy of machine learning systems, the goal of avoiding paternalistic infringements of autonomy rights, or the goal of protecting the privacy interests of data subjects. Sometimes, these different values may conflict: we cannot always optimize for everything at once. This makes it all the more important to adopt a sufficiently rich, pluralistic view of the full range of relevant ethical values at stake—only then can one reflect critically on what kinds of ethical trade-offs one may have to confront.
2. The simplicity trap:
“In order to make ethics practical and action-guiding, we need to distill our moral framework into a user-friendly compliance checklist. After we’ve decided on a particular path of action, we’ll go through that checklist to make sure that we’re being ethical.”
Given the high visibility and urgency of ethical dilemmas arising in the context of AI, it is not surprising that there are more and more calls to develop actionable AI ethics checklists. For instance, a 2018 draft report by the European Commission’s High-Level Expert Group on Artificial Intelligence specifies a preliminary ‘assessment list’ for ‘trustworthy AI’. While the report plausibly acknowledges that such an assessment list must be context-sensitive and that it is not exhaustive, it nevertheless identifies a list of ten fixed ethical goals, including privacy and transparency. But can and should ethical values be articulated in a checklist in the first place? It is worth examining this underlying assumption critically. After all, a checklist implies a one-off review process: on that view, developers or policy-makers could determine whether a particular system is ethically defensible at a specific moment in time, and then move on without confronting any further ethical concerns once the checklist criteria have been satisfied once. But ethical reasoning cannot be a static one-off assessment: it required an ongoing process of reflection, deliberation, and contestation. Simplicity is good—but the willingness to reconsider simple frameworks, when required, is better. Setting a fixed ethical agenda ahead of time risks obscuring new ethical problems that may arise at a later point in time, or ongoing ethical problems that become apparent to human decision-makers only later.
3. The relativism trap:
“We all disagree about what is morally valuable, so it’s pointless to imagine that there is a universalbaseline against which we can use in order to evaluate moral choices. Nothing is objectively morally good: things can only be morally good relative to each person’s individual value framework.”
Public discourse on the ethics of AI frequently produces little more than an exchange of personal opinions or institutional positions. In light of pervasive moral disagreement, it is easy to conclude that ethical reasoning can never stand on firm ground: it always seems to be relative to a person’s views and context. But this does not mean that ethical reasoning about AI and its social and political implications is futile: some ethical arguments about AI may ultimately be more persuasive than others. While it may not always be possible to determine ‘the one right answer’, it is often possible to identify at least some paths of action are clearly wrong, and some paths of action that are comparatively better (if not optimal all things considered). If that is the case, comparing the respective merits of ethical arguments can be action-guiding for developers and policy-makers, despite the presence of moral disagreement. Thus, it is possible and indeed constructive for AI ethics to welcome value pluralism, without collapsing into extreme value relativism.
4. The value alignment trap:
“If relativism is wrong (see #3), there must be one morally right answer. We need to find that right answer, and ensure that everyone in our
organisation acts in alignment with that answer. If our ethical reasoning leads to moral disagreement, that means that we have failed.”…(More)”.