Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies

Curated on Posted on by Stefaan Verhulst

Zeynep Engin and Philip Treleaven in the Computer Journal:  “The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the ‘smartification’ of public services and the national infrastructure will be much more significant in comparison to any other sector given government’s function and importance to every institution and individual.

Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major ‘client’ and also ‘public champion’ for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants….(More)”.

The Public Mapping Project: How Public Participation Can Revolutionize Redistricting

Curated on Posted on by Stefaan Verhulst

Book by Micah Altman and Michael P. McDonald: “… unveil the Public Mapping Project, which developed DistrictBuilder, an open-source software redistricting application designed to give the public transparent, accessible, and easy-to-use online mapping tools. As they show, the goal is for all citizens to have access to the same information that legislators use when drawing congressional maps—and use that data to create maps of their own….(More)”.

Statistics Canada promises more detailed portrait of Canadians with fewer surveys

Curated on Posted on by Stefaan Verhulst

Bill Curry at The Globe and Mail: “Canadians are increasingly shunning phone surveys, but they could still be providing Statistics Canada with valuable data each time they flush the toilet or flash their debit card.

The national statistics agency laid out an ambitious plan Thursday to overhaul the way it collects and reports on issues ranging from cannabis and opioid use to market-moving information on unemployment and economic growth.

According to four senior Statscan officials, the agency is in the midst of a major transformation as it adapts to a world of big data collected by other government agencies as well as private sector actors such as banks, cellphone companies and digital-based companies like Uber.

At its core, the shift means the agency will become less reliant on traditional phone surveys or having businesses fill out forms to report their sales data. Instead, Statscan is reaching agreements with other government departments and private companies in order to gain access to their raw data, such as point-of-sale information. According to agency officials, such arrangements reduce the reporting paperwork faced by businesses while creating the potential for Statscan to produce faster and more reliable information.

Key releases such as labour statistics or reporting on economic growth could come out sooner, reducing the lag time between the end of a quarter and reporting on results. Officials said economic data that is released quarterly could shift to monthly reporting. The greater access to raw data sources will also allow for more localized reporting at the neighbourhood level….(More)”.

The Role of Management in Open Data Initiatives in Local Governments: Opening the Organizational Black Box

Curated on Posted on by Stefaan Verhulst

Paper by Mila Gasco-Hernandez and  Jose Ramon Gil-Garcia: “Previous studies have infrequently addressed the dynamic interactions among social, technical, and organizational variables in open government data initiatives. In addition, organization level models have neglected to explain the role of management in decision-making processes about technology and data. This article contributes to addressing this gap in the literature by analyzing the complex relationships between open government data characteristics and the organizations and institutions in which they are embedded.

We systematically compare the open data inception and implementation processes, as well as their main results, in three Spanish local governments (Gava and Rubi in Catalonia and Gijon in Asturias) by using a model that combines the technology enactment framework with some specific constructs and relationships from the process model of computing change. Our resulting model is able to identify and explain the significant role of management in shaping and mediating different interactions, but also acknowledges the importance of organizational level variables and the context in which the open data initiative is taking place…(More)”.

Surveillance Studies: A Reader

Curated on Posted on by Stefaan Verhulst

Book edited by Torin Monahan and David Murakami Wood: “Surveillance is everywhere: in workplaces monitoring the performance of employees, social media sites tracking clicks and uploads, financial institutions logging transactions, advertisers amassing fine-grained data on customers, and security agencies siphoning up everyone’s telecommunications activities. Surveillance practices-although often hidden-have come to define the way modern institutions operate. Because of the growing awareness of the central role of surveillance in shaping power relations and knowledge across social and cultural contexts, scholars from many different academic disciplines have been drawn to “surveillance studies,” which in recent years has solidified as a major field of study.

Torin Monahan and David Murakami Wood’s Surveillance Studies is a broad-ranging reader that provides a comprehensive overview of the dynamic field. In fifteen sections, the book features selections from key historical and theoretical texts, samples of the best empirical research done on surveillance, introductions to debates about privacy and power, and cutting-edge treatments of art, film, and literature. While the disciplinary perspectives and foci of scholars in surveillance studies may be diverse, there is coherence and agreement about core concepts, ideas, and texts. This reader outlines these core dimensions and highlights various differences and tensions. In addition to a thorough introduction that maps the development of the field, the volume offers helpful editorial remarks for each section and brief prologues that frame the included excerpts. …(More)”.

Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making?

Curated on Posted on by Stefaan Verhulst

Paper by H.G. (Haiko)van der Voort et al: “Big data promises to transform public decision-making for the better by making it more responsive to actual needs and policy effects. However, much recent work on big data in public decision-making assumes a rational view of decision-making, which has been much criticized in the public administration debate.

In this paper, we apply this view, and a more political one, to the context of big data and offer a qualitative study. We question the impact of big data on decision-making, realizing that big data – including its new methods and functions – must inevitably encounter existing political and managerial institutions. By studying two illustrative cases of big data use processes, we explore how these two worlds meet. Specifically, we look at the interaction between data analysts and decision makers.

In this we distinguish between a rational view and a political view, and between an information logic and a decision logic. We find that big data provides ample opportunities for both analysts and decision makers to do a better job, but this doesn’t necessarily imply better decision-making, because big data also provides opportunities for actors to pursue their own interests. Big data enables both data analysts and decision makers to act as autonomous agents rather than as links in a functional chain. Therefore, big data’s impact cannot be interpreted only in terms of its functional promise; it must also be acknowledged as a phenomenon set to impact our policymaking institutions, including their legitimacy….(More)”.

Declaration on Ethics and Data Protection in Artifical Intelligence

Curated on Posted on by Stefaan Verhulst

Declaration: “…The 40th International Conference of Data Protection and Privacy Commissioners considers that any creation, development and use of artificial intelligence systems shall fully respect human rights, particularly the rights to the protection of personal data and to privacy, as well as human dignity, non-discrimination and fundamental values, and shall provide solutions to allow individuals to maintain control and understanding of artificial intelligence systems.

The Conference therefore endorses the following guiding principles, as its core values to preserve human rights in the development of artificial intelligence:

  1. Artificial intelligence and machine learning technologies should be designed, developed and used in respect of fundamental human rights and in accordance with the fairness principle, in particular by:
  2. Considering individuals’ reasonable expectations by ensuring that the use of artificial intelligence systems remains consistent with their original purposes, and that the data are used in a way that is not incompatible with the original purpose of their collection,
  3. taking into consideration not only the impact that the use of artificial intelligence may have on the individual, but also the collective impact on groups and on society at large,
  4. ensuring that artificial intelligence systems are developed in a way that facilitates human development and does not obstruct or endanger it, thus recognizing the need for delineation and boundaries on certain uses,…(More)

When AI Misjudgment Is Not an Accident

Curated on Posted on by Stefaan Verhulst

Douglas Yeung at Scientific American: “The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society—those that wrongly predict black defendants will commit future crimes, for example, or facial-recognition technologies developed mainly by using photos of white men that do a poor job of identifying women and people with darker skin.

But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into them, smuggled inside the data that helps those systems learn. This could introduce a worrisome new dimension to cyberattacks, disinformation campaigns or the proliferation of fake news.

According to a U.S. government study on big data and privacy, biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices. Algorithms could be designed to take advantage of seemingly innocuous factors that can be discriminatory. Employing existing techniques, but with biased data or algorithms, could make it easier to hide nefarious intent. Commercial data brokers collect and hold onto all kinds of information, such as online browsing or shopping habits, that could be used in this way.

Biased data could also serve as bait. Corporations could release biased data with the hope competitors would use it to train artificial intelligence algorithms, causing competitors to diminish the quality of their own products and consumer confidence in them.

Algorithmic bias attacks could also be used to more easily advance ideological agendas. If hate groups or political advocacy organizations want to target or exclude people on the basis of race, gender, religion or other characteristics, biased algorithms could give them either the justification or more advanced means to directly do so. Biased data also could come into play in redistricting efforts that entrench racial segregation (“redlining”) or restrict voting rights.

Finally, national security threats from foreign actors could use deliberate bias attacks to destabilize societies by undermining government legitimacy or sharpening public polarization. This would fit naturally with tactics that reportedly seek to exploit ideological divides by creating social media posts and buying online ads designed to inflame racial tensions….(More)”.

The Lack of Decentralization of Data: Barriers, Exclusivity, and Monopoly in Open Data

Curated on Posted on by Stefaan Verhulst

Paper by Carla Hamida and Amanda Landi: “Recently, Facebook creator Mark Zuckerberg was on trial for the misuse of personal data. In 2013, the National Security Agency was exposed by Edward Snowden for invading the privacy of inhabitants of the United States by examining personal data. We see in the news examples, like the two just described, of government agencies and private companies being less than truthful about their use of our data. A related issue is that these same government agencies and private companies do not share their own data, and this creates the openness of data problem.

Government, academics, and citizens can play a role in making data more open. In the present, there are non-profit organizations that research data openness, such as OpenData Charter, Global Open Data Index, and Open Data Barometer. These organizations have different methods on measuring openness of data, so this leads us to question what does open data mean, how does one measure how open data is and who decides how open should data be, and to what extent society is affected by the availability, or lack of availability, of data. In this paper, we explore these questions with an examination of two of the non-profit organizations that study the open data problem extensively….(More)”.

This is how computers “predict the future”

Curated on Posted on by Stefaan Verhulst

Dan Kopf at Quartz: “The poetically named “random forest” is one of data science’s most-loved prediction algorithms. Developed primarily by statistician Leo Breiman in the 1990s, the random forest is cherished for its simplicity. Though it is not always the most accurate prediction method for a given problem, it holds a special place in machine learning because even those new to data science can implement and understand this powerful algorithm.

This was the algorithm used in an exciting 2017 study on suicide predictions, conducted by biomedical-informatics specialist Colin Walsh of Vanderbilt University and psychologists Jessica Ribeiro and Joseph Franklin of Florida State University. Their goal was to take what they knew about a set of 5,000 patients with a history of self-injury, and see if they could use those data to predict the likelihood that those patients would commit suicide. The study was done retrospectively. Sadly, almost 2,000 of these patients had killed themselves by the time the research was underway.

Altogether, the researchers had over 1,300 different characteristics they could use to make their predictions, including age, gender, and various aspects of the individuals’ medical histories. If the predictions from the algorithm proved to be accurate, the algorithm could theoretically be used in the future to identify people at high risk of suicide, and deliver targeted programs to them. That would be a very good thing.

Predictive algorithms are everywhere. In an age when data are plentiful and computing power is mighty and cheap, data scientists increasingly take information on people, companies, and markets—whether given willingly or harvested surreptitiously—and use it to guess the future. Algorithms predict what movie we might want to watch next, which stocks will increase in value, and which advertisement we’re most likely to respond to on social media. Artificial-intelligence tools, like those used for self-driving cars, often rely on predictive algorithms for decision making….(More)”.