The Data Firehose and AI in Government: Why Data Management is a Key to Value and Ethics

Technical and organizational innovations such as Open Data, Internet of Things and Big Data have fueled renewed interest in policy analytics in the public sector. This revamped version of policy analysis continues the long-standing tradition of applying statistical modeling to better understand policy effects and decision making, but also incorporates other computational approaches such as artificial intelligence (AI) and computer simulation. Although much attention has been given to the development of capabilities for data analysis, there is much less attention to understanding the role of data management in a context of AI in government. In this paper, we argue that data management capabilities are foundational to data analysis of any kind, but even more important in the present AI context. This is so because without proper data management, simply acquiring data or systems will not produce desired outcomes. We also argue that realizing the potential of AI for social good relies on investments specifically focused on this social outcome, investments in the processes of building trust in government data, and ensuring the data are ready and suitable for use, for both immediate and future uses.

[1]  Jan vom Brocke,et al.  Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data , 2016, MIS Q. Executive.

[2]  Boris Otto,et al.  Management of the master data lifecycle: a framework for analysis , 2013, J. Enterp. Inf. Manag..

[3]  John Ladley Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program , 2012 .

[4]  Alie Doorten,et al.  GETTING A HANDLE , 1994 .

[5]  Virginia E. Eubanks Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018 .

[6]  Fernando Iafrate,et al.  Artificial Intelligence and Big Data: The Birth of a New Intelligence , 2018 .

[7]  Teresa M. Harrison,et al.  The Salience and Urgency of Enterprise Data Management In the Public Sector , 2018, HICSS.

[8]  Simon C. Mathews,et al.  Top-Funded Digital Health Companies And Their Impact On High-Burden, High-Cost Conditions. , 2019, Health affairs.

[9]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[10]  Muhammad Anshari,et al.  E-Government with Big Data Enabled through Smartphone for Public Services: Possibilities and Challenges , 2017 .

[11]  Daniel E. O'Leary,et al.  Embedding AI and Crowdsourcing in the Big Data Lake , 2014, IEEE Intelligent Systems.

[12]  R. Kitchin,et al.  The ethics of smart cities and urban science , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[13]  Karl M. Manheim,et al.  Artificial Intelligence: Risks to Privacy and Democracy , 2018 .

[14]  Paul Smaglik Getting a handle on data , 2001, Nature.

[15]  Francesco Corea,et al.  AI Knowledge Map: How to Classify AI Technologies , 2018, Studies in Big Data.

[16]  Keith Kirkpatrick,et al.  It's not the algorithm, it's the data , 2017, Commun. ACM.

[17]  Hannah Lebovits Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018, Public Integrity.