Data Science & Ethics in Government

Data Science & Ethics in Government

DATA SCIENCE IN GOVERNMENT: PROMISES AND PITFALLS

FWD50 Workshop

Dr. Tracey P. LauriaultCritical Media and Big DataSchool of Journalism and CommunicationCarleton University, Ottawa, ON, [email protected]@TraceyLauriaultORCID: orcid.org/0000-0003-1847-2738

mailto:[email protected]

Table of Contents

1. Data Science Regional, national, state and corporate reports

2. Gaps

3. EU Policy

Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University

Data Science Reports


Big Data Reports

Shortage of Talent


“a shortage of talent, particularly of people with deep

expertise in statistics and machine learning, and the

managers and analysts who know how to operate

companies by using insights from big data.”

Some of the data science skills

• A/B testing

• Association rule learning

• Cluster analysis

• Data fusion & data integration

• Data base management

• Data mining

• Ensemble learning

• Genetic algorithms

• Machine Learning

• Natural language processing

• Neural networks.

• Network Analysis

• Pattern recognition

• Predictive modelling

• Regression analysis

• Sentiment analysis

• Signal processing

• Simulation

• Visualization

• Etc.

Public Sector Benefits


Graduates


National Plans - Ireland

National Program - Ireland

1. Priority Area B in the Research Prioritization Steering Group (Mar. 2012)

2. Action Plan for Jobs 2013 (Feb. 2013) that repositions big data as Disruptive Reform 1 which is to “Build on our existing enterprise strengths to make Ireland a leading country in Europe in ‘Big Data’”.

3. SAS UK and Ireland commissions the Centre for Economics and Business Research (Cebr) Data equity –Ireland Unlocking the value of big data (June 2013) coining the term ‘data equity’ from the idea of brand equity. Companies will be forming data equity with the power of analytics.

4. The Joint Industry/Government task force on Big Data was formed in June 2013 to “drive the development of this high-growth sector in Ireland”.

5. Global Technology and Service Trends Influencing Irish ICT Skills Demand Addressing Future Demand for High-Level ICT Skills.(Nov. 2013).

6. Request for Tender for The Provision of Research for the Study Assessing the Demand for Big Data / Data Analytics Skills in Ireland 2013-2020 awarded to E & Y and Oxford Economics.

7. Big data is a “disruptive growth and innovation phase. This includes the adoption of cloud computing, the penetration of mobile devices and technologies and the Internet of things, the emergence of Big Data analytics, IT security, micro- and nanoelectronics and the adoption of social technologies in both the personal and business environment” in the EGFSN ICT Skills Action Plan (Mar. 2014) also an outcome of the Action Plan for Jobs 2013.

8. In April of 2014, the Assessing the Demand for Big Data and Analytics Skills, 2013 – 2020(Big Data Skills Report) is released.

http://www.forfas.ie/publication/search.jsp?ft=/publications/2012/Title,9545,en.php

http://www.djei.ie/enterprise/apj.htm

http://www.sas.com/offices/europe/uk/press_office/press_releases/cebr-unlock-big-data.html

http://www.djei.ie/press/2013/20130624.htm

http://www.forfas.ie/publication/search.jsp?ft=/publications/2013/Title,11289,en.php

http://www.skillsireland.ie/publications/2014/title,11945,en.php

http://www.skillsireland.ie/publications/2014/title,12194,en.php

Baseline estimates and projections

• McKinsey Global Institute (MGI) (2011) Big data: The next frontier for innovation, competition, and productivity (focusing in the main on the US).

• Accenture Institute for High Performance (2013) Crunch Time: How to overcome the looming global analytics talent mismatch - focusing on key sectors in the US, UK, Singapore, Japan, Brazil, India, China

• e-skills UK (SAS) (2013) Big Data Analytics – Adoption and Employment Trends, 2012-2017.

• Council for Economics and Business Research (Cebr) (2013) Data equity – Ireland. Unlocking the Value of big data (applying international findings to Ireland).

http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

http://www.accenture.com/us-en/blogs/analytics/archive/tags/analytics talent/default.aspx

http://www.sas.com/en_gb/offers/big-data-skills/register.html

http://www.sas.com/reg/gen/uk/cebr-ireland

Baseline employment demand was determined

1. E & Y and Oxford Economics consistently estimated that existing employment in this sector consisted of 1.5% to 2% of total employment, although categorizations differed.

2. Eurostat data were used to compare shares of employment in Ireland and the UK by sector and CSO Data from the Quarterly National Household Survey (QNHS) were used (See figure 3.2 p. 42).

3. Based on estimates and proportions for the US and UK, the Deep Analytical Talent employment demand for Ireland was estimated.

4. Also, the proportion of those in established roles was considered to be more than half of that demand while the remainder are emerging analytical roles.

4. The Big Data Savvy demand was determined by applying the proportion of total employment in this category in the US to the Irish employment data.

5. The Supporting Technology demand was derived from structured interviews with the 45 enterprises and organizations in Ireland. A ratio of 1:4 was determined of Deep Analytical Talent to Supporting Technology professionals.

6. To understand the sectoral distribution, the analysis in the 2011 MGI report was used. In the MGI analysis low, medium and high data intensity based on data storage capital stock per firm groups were devised. CSO data were used here to “understand the data intensity of industries as the capital stock in computer software per employee” (p.44).

Consultation

• A Structured Interview Survey about the employment levels across each of the three categories was conducted by telephone with 45 Forfás selected Irish based enterprises and organizations that employ big data & data analytics talent.

• This included:• 35 companies (foreign owned and indigenous),

• 7 government bodies,

• 2 data analytic research centres and

• 10 key informants.

• Some workshops were also held.

Coding in high school

Increase math learning

Greater female participation

Cooperation with Industry

Coop & Work Placement

Note – U. Waterloo

Universities should pool

resources and offer specialized

courses

Up Skill, transition workforce, retraining,

Short courses

Business associations hold workshops

Promotion & awareness

Prediction

State of Massachusetts








Fraud detection

Threat analysis

Crime

Weather

Cyber security

European Union

• Low-quality data, biases in the data or errors in the analysis can all lead to incorrect or misguided conclusions.

• Technical concerns:• potentially limited scope of data,

• problems of accessing the information, and poor interoperability.

• online transactions may not be a good model for offline 'real world' behaviour,

• rapid changes in the digital environment may mean that predictions based on

• past data are unreliable guides to future behaviour

• analytics will provide a sustainable competitive advantage to companies only if the data is inimitable, rare, valuable, exploitable and non-substitutable


EU Briefing - Issues

• Privacy and personal data protection• General Data Protection Regulation (GDPR)• Anonymization is easy to reverse• Surveillance

• Data ownership• More than one owner• Complex set of rights and privileges

• Autonomous car & data ownership

• DRM

• Abuse of dominant actors & consumer lock-in

• Trade the sale of anonymized health data for reduced cost of medicine and right to access the information?

• Data sovereignty• Localization• Personal and non-personal data• Corporate R&D• The rights of data subjects

• Skills gap

• Data Divide• Access rights• Data poverty & underrepresented voices

EU Briefing - Policy

• Policy framework is required• Data driven economy• Digital single market• Investment in enabling infrastructure

• Data Protection• Tension between innovation and protecting rights

• Data flows• Data resale• Restrictions vs incentives

• Law• Interoperability• Data access• Data ownership• Data usability

Big Data Consortium Canada

• Canada’s Big Data Talent Gap is estimated between10,500 and 19,000 professionals with deep data and analytical skills, such as those required for roles like• Chief Data Officer,

• Data Scientist, and

• Data SolutionsArchitect.

• The gap for professionals with solid data and analytical literacy to make better decisions is estimated at a further 150,000, such as those required for roles• Business Manager and

• Business Analyst

• Need for clearer definitions

Gaps


Data Brokers & Credit Scoring?


Absence of key issues

• Analytics for the public good & in public interest

• Application areas –• IoT, Smart City, Precision

Agriculture, autonomous car

• Bots & fake new – social media & news

• Blockchain

• Error correction

• Literacy – general public

• Machine learning bias

• Not-SO-Sharing Economy

• Platform concentration & capitalism

• Predictive policing

• Robotics & displacement of labour

• Social impact

• Social & Environmental issues

• Surveillance

• Etc.


Absence of the following talents

• Critical thinking

• Data quality

• Ethics

• Governance

• Law

• Policy esp. Public Policy

• Standards

• Subject matter specialists


Absence of the following domains

• Communications

• Digital humanities

• GI Science / Geomatics

• Journalism

• Public Administration

• Social Science

• Surveillance


Absence of critical scholarship

Kitchin’s Data Assemblage, 2015

Material Platform

(infrastructure – hardware)

Code Platform

(operating system)

Code/algorithms

(software)

Data(base)

Interface

Reception/Operation

(user/usage)

Systems of thought

Forms of knowledge

Finance

Political economies

Governmentalities & legalities

Organisations and institutions

Subjectivities and communities

Marketplace

System/process

performs a task

Context

frames the system/task

Digital socio-technical assemblage

Algorithm Studies

Critical code studies

Software studies

Critical data studies

New media studies

game studies

Critical Social Science

Science Technology Studies

Platform studies Places

Practices

Flowline/Lifecycle

Surveillance studies

HCI, remediation studies

Absence of public interest


EU Policy


General Data Protection Regulation

• Data Subjects

• Data Sovereignty

• Data Portability

• Right to Access

• Right to Explanation – Algorithms

• Right to Repair????


Conclusion


Conclusion

• Need for skills & talent beyond• Deep analytical talent• Big data savvy• Supporting technology

• Need to recognize, value, support & grow the following skills• Critical thinking• Data quality• Ethics• Governance• Law & Regulation• Policy esp. Public Policy• Standards• Subject matter specialists

• Data science programs need to include• Communications• Digital humanities• GI Science / Geomatics• Journalism• Public Administration• Social Science• Surveillance

• Look to other jurisdictions• Right to Repair• Right to Explanation - Algorithms• Data Subjects• Data Sovereignty• Right to Access• Data Portability


Date post:	28-Jan-2018
Category:	Technology
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