Collaborative Policymaking Using Human-centered AI

Collaborative Policymaking

There is increasing demand for greater citizen and stakeholder involvement in the development of a wide range of policies and interventions. Government, non-government and the private sector all recognize the need for more effective ways to carry out meaningful multi-actor engagement and to transform complex data into evidence-informed policy and products that support decision-making.

PolicySpark's mission is to use cutting-edge concepts and technologies to provide decision-makers with the strategic intelligence they need to make the most effective and efficient decisions in the face of increasing complexity. We tame this complexity to find out what matters and what works, and we deliver this valuable strategic intelligence to our clients in ways that are clear, visual, intuitive and practical to use.

Why Policies Fail

Policies, programs and other interventions fail to achieve results for two reasons:

Understanding Any Policy System

Our Software

Allows every voice to be heard

Analyzes the evidence and data to identify which policy levers produce the desired outcomes

Identifies where people agree

Advantages for Decision-makers

Why Use Evidence?

Policies and Programs Informed by Evidence:

Are more likely to achieve desired outcomes

Build trust, buy-in and communication with stakeholders and benefactors

Increase institutional credibility

Satisfy the increasing demand for evidence-informed decision-making

Three Steps to Better Results

STEP 1: Gather Evidence

Principal Sources of Data

Stakeholder Knowledge

Experiential evidence derived from deliberative stakeholder narrative

Research / Science

Evidence derived from existing research literature

Expert Knoweldge

Evidence derived from subject matter experts

Big Data / Open Data

Evidence derived from available big and/or open data sets

STEP 2: Build Models and Compute Key Outcome Pathways

Sources of evidence are transformed into models that reflect the many causal factors that play a part in driving outcomes. Data are combined and analyzed using PolicySpark’s algorithmic platform resulting in a model that reveals the factors that exert the highest levels of influence on desired outcomes. With these “leverage points” in hand, it becomes possible to describe detailed results pathways, providing the basis for measurable and testable theories of change.

STEP 3: Use Results to Manage Strategically

By systematically gathering and processing the available evidence, decision-makers and managers are in a better position to make more informed choices. Our products can be used to:

Executive team

FOUNDER, CEO

Chris Callaghan

A native Montréaler, Chris studied math, chemistry and physics at Dawson College in the mid-80’s, and went on to earn earned a B.Sc. from McGill in natural history and wildlife science, and an M.Sc. from the University of Ottawa, with a focus on how human activities affect natural ecosystems. Chris then entered the Canadian federal public service, where he took on various positions related to environmental toxicology, ecosystem management, sustainable development and environmental auditing... More Info

It was during his time with the Auditor General of Canada that Chris became interested in the policies and frameworks associated with measuring organizational performance and accountability. In 2006, Chris opened his own consulting firm specializing in program evaluation. As he gained insight into some of the challenges associated with program evaluation theory and practice, he observed what he sees as serious gaps in how interventions are designed and measured. Chris is now concerned with developing better means to understand how interventions work, how to improve design processes, and how to support evidence-informed decision-making. His vision is a world in which decision-making is systematically supported with evidence, including research, expert knowledge, stakeholder narrative, and open/big data. To realize this vision, he is centering his focus on the use of cutting-edge concepts and technologies, including those associated with data science and machine learning, to gather, analyze and test the evidence needed to bring policy-making into the modern, data-driven era.

PRINCIPAL SCIENTIST

Matthew Spencer

Matt is PolicySpark’s Principal Scientist. Matt is an applied scientist with expertise in enterprise-scale machine learning, software development, data science, and computational biology. He is the engine behind PolicySpark’s innovative technological solutions. He obtained a BASc. in computer engineering from the University of Ottawa and went on to earn a Ph.D. in cybernetics from the University of Reading in the United Kingdom. The focus of his thesis was complex network models of brain connectivity... More Info

Matt is an effective international and inter-disciplinary collaborator. He has over 10 years of experience working with teams around the world and communicating complex ideas to a wide range of audiences. He has 5 years of experience leading machine learning projects at Amazon. His technical expertise includes machine learning research and development employing a wide variety of technologies, including TensorFlow, Sklearn, Spark, Python and AWS. Other strengths include natural language processing, biosignal/time-series analysis, complex network analysis (Graph Theory), predictive stochastic models, reinforcement learning, programming (Python, Java, C#, C/C++, MATLAB), and enterprise software architecture.

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