research paper / thesis / dissertation

The Inevitability of AI Law & Policy: Preparing Government for the Era of Autonomous Machines

Curated on November 1, 2018May 29, 2019 by Stefaan Verhulst

Public Knowledge: “Today, we’re happy to announce our newest white paper, “The Inevitability of AI Law & Policy: Preparing Government for the Era of Autonomous Machines,” by Public Knowledge General Counsel Ryan Clough. The paper argues that the rapid and pervasive rise of artificial intelligence risks exploiting the most marginalized and vulnerable in our society. To mitigate these harms, Clough advocates for a new federal authority to help the U.S. government implement fair and equitable AI. Such an authority should provide the rest of the government with the expertise and experience needed to achieve five goals crucial to building ethical AI systems:

Boosting sector-specific regulators and confronting overarching policy challenges raised by AI;
Protecting public values in government procurement and implementation of AI;
Attracting AI practitioners to civil service, and building durable and centralized AI expertise within government;
Identifying major gaps in the laws and regulatory frameworks that govern AI; and
Coordinating strategies and priorities for international AI governance.

“Any individual can be misjudged and mistreated by artificial intelligence,” Clough explains, “but the record to date indicates that it is significantly more likely to happen to the less powerful, who also have less recourse to do anything about it.” The paper argues that a new federal authority is the best way to meet the profound and novel challenges AI poses for us all….(More)”.

Positive deviance, big data, and development: A systematic literature review

Curated on October 31, 2018 by Stefaan Verhulst

Paper by Basma Albanna and Richard Heeks: “Positive deviance is a growing approach in international development that identifies those within a population who are outperforming their peers in some way, eg, children in low‐income families who are well nourished when those around them are not. Analysing and then disseminating the behaviours and other factors underpinning positive deviance are demonstrably effective in delivering development results.

However, positive deviance faces a number of challenges that are restricting its diffusion. In this paper, using a systematic literature review, we analyse the current state of positive deviance and the potential for big data to address the challenges facing positive deviance. From this, we evaluate the promise of “big data‐based positive deviance”: This would analyse typical sources of big data in developing countries—mobile phone records, social media, remote sensing data, etc—to identify both positive deviants and the factors underpinning their superior performance.

While big data cannot solve all the challenges facing positive deviance as a development tool, they could reduce time, cost, and effort; identify positive deviants in new or better ways; and enable positive deviance to break out of its current preoccupation with public health into domains such as agriculture, education, and urban planning. In turn, positive deviance could provide a new and systematic basis for extracting real‐world development impacts from big data…(More)”.

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

Curated on October 29, 2018 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 Role of Management in Open Data Initiatives in Local Governments: Opening the Organizational Black Box

Curated on October 28, 2018 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)”.

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

Curated on October 27, 2018 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)”.

The causal effect of trust

Curated on October 27, 2018 by Stefaan Verhulst

Paper by Björn Bartling, Ernst Fehr, David Huffman and Nick Netzer: “Trust affects almost all human relationships – in families, organizations, markets and politics. However, identifying the conditions under which trust, defined as people’s beliefs in the trustworthiness of others, has a causal effect on the efficiency of human interactions has proven to be difficult. We show experimentally and theoretically that trust indeed has a causal effect. The duration of the effect depends, however, on whether initial trust variations are supported by multiple equilibria.

We study a repeated principal-agent game with multiple equilibria and document empirically that an efficient equilibrium is selected if principals believe that agents are trustworthy, while players coordinate on an inefficient equilibrium if principals believe that agents are untrustworthy. Yet, if we change the institutional environment such that there is a unique equilibrium, initial variations in trust have short-run effects only. Moreover, if we weaken contract enforcement in the latter environment, exogenous variations in trust do not even have a short-run effect. The institutional environment thus appears to be key for whether trust has causal effects and whether the effects are transient or persistent…(More)”.

Crowdsourcing reliable local data

Curated on October 25, 2018 by Stefaan Verhulst

Paper by Jane Lawrence Sumner, Emily M. Farris, and Mirya R. Holman: “The adage “All politics is local” in the United States is largely true. Of the United States’ 90,106 governments, 99.9% are local governments. Despite variations in institutional features, descriptive representation, and policy making power, political scientists have been slow to take advantage of these variations. One obstacle is that comprehensive data on local politics is often extremely difficult to obtain; as a result, data is unavailable or costly, hard to replicate, and rarely updated.

We provide an alternative: crowdsourcing this data. We demonstrate and validate crowdsourcing data on local politics, using two different data collection projects. We evaluate different measures of consensus across coders and validate the crowd’s work against elite and professional datasets. In doing so, we show that crowd-sourced data is both highly accurate and easy to use. In doing so, we demonstrate that non-experts can be used to collect, validate, or update local data….All data from the project available at https://dataverse.harvard.edu/dataverse/2chainz …(More)”.

Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security

Curated on October 24, 2018 by Stefaan Verhulst

Paper by Robert Chesney and Danielle Keats Citron: “Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating the technology’s sophistication, making deep fakes ever more realistic and increasingly resistant to detection.

Deep-fake technology has characteristics that enable rapid and widespread diffusion, putting it into the hands of both sophisticated and unsophisticated actors. While deep-fake technology will bring with it certain benefits, it also will introduce many harms. The marketplace of ideas already suffers from truth decay as our networked information environment interacts in toxic ways with our cognitive biases. Deep fakes will exacerbate this problem significantly. Individuals and businesses will face novel forms of exploitation, intimidation, and personal sabotage. The risks to our democracy and to national security are profound as well.

Our aim is to provide the first in-depth assessment of the causes and consequences of this disruptive technological change, and to explore the existing and potential tools for responding to it. We survey a broad array of responses, including: the role of technological solutions; criminal penalties, civil liability, and regulatory action; military and covert-action responses; economic sanctions; and market developments. We cover the waterfront from immunities to immutable authentication trails, offering recommendations to improve law and policy and anticipating the pitfalls embedded in various solutions….(More)”.

Privacy and Synthetic Datasets

Curated on October 23, 2018 by Stefaan Verhulst

Paper by Steven M. Bellovin, Preetam K. Dutta and Nathan Reitinger: “Sharing is a virtue, instilled in us from childhood. Unfortunately, when it comes to big data — i.e., databases possessing the potential to usher in a whole new world of scientific progress — the legal landscape prefers a hoggish motif. The historic approach to the resulting database–privacy problem has been anonymization, a subtractive technique incurring not only poor privacy results, but also lackluster utility. In anonymization’s stead, differential privacy arose; it provides better, near-perfect privacy, but is nonetheless subtractive in terms of utility.

Today, another solution is leaning into the fore, synthetic data. Using the magic of machine learning, synthetic data offers a generative, additive approach — the creation of almost-but-not-quite replica data. In fact, as we recommend, synthetic data may be combined with differential privacy to achieve a best-of-both-worlds scenario. After unpacking the technical nuances of synthetic data, we analyze its legal implications, finding both over and under inclusive applications. Privacy statutes either overweigh or downplay the potential for synthetic data to leak secrets, inviting ambiguity. We conclude by finding that synthetic data is a valid, privacy-conscious alternative to raw data, but is not a cure-all for every situation. In the end, computer science progress must be met with proper policy in order to move the area of useful data dissemination forward….(More)”.