What does data-driven justice mean to us?

The rise of open data coupled with increasingly powerful methods in data science and artificial intelligence offers unparalleled opportunities to gain insight into, and make predictions of, human criminal behaviour. Simultaneously, we have a collective responsibility to ensure that data and methods are used in a responsible manner. Data-driven justice aims to harness the power of big data and cutting-edge data science technologies to find ways to maximize fairness, reduce criminality, and improve the delivery of justice.

What is our challenge about?

The challenge is to find and use open data to inform community groups and law enforcement within Europe. For instance, we could explore optimal allocation of policing resources to make high crime areas safer for residents. Alternatively, we might explore whether bias in policing practice leads to disproportionate incarceration of minority populations. Throughout the challenge, participants must make explicit effort to identify and potentially address social, legal and ethical issues related to the administration of justice.

What will we do in this challenge? 

We are going to collect and analyze data about crimes that would help us to:
1) Reveal patterns relevant to criminal justice;
2) Identify interesting uses of these data e.g. identify discriminatory practices or predict future crime events;
3) Investigate the relationship between perception of crime and actual crime
statistics within communities.

Why is this important?

Understanding the major features influencing crime and having the capability to accurately predict details about future crimes would be invaluable in the judicious allocation of law enforcement resources, which would ultimately save government and law enforcement agencies time and money. Understanding how citizens perceive crime can help governments make better decisions on how to engage with communities with regards to crime awareness, prevention and management. This will ultimately improve the quality of life of citizens. However, it is also important to remain aware of the potential drawbacks that these data-driven approaches may present. For instance, more policing in a particular area will disproportionately uncover more criminal activity; moreover, there could be bias against minorities and other forms of discrimination that need to be addressed and understood.

 

 

Guiding questions

Here are some guiding questions that can be used as a starting point for participants when building up their own project goals within the Data-Driven Justice Challenge

  1. Crime Patterns: When and where will the next crime take place in a specific region or community?
  2. Crime Classification:
    1. How does the socio-economic background of a person influence theirthe probability of committing a crime?
    2. How does the socio-economic background of the criminal relate to the type ofcrime committed?
  3. Perception of safety among citizens:
    1. What factors influence the perception of safety for European citizens?
    2. Can we explain regional differences, e.g. by looking at the demographics,prevalence of political affiliation or other interesting factors?

Outcome

What outcome do we expect from participants at the end of the hackathon and how can we evaluate this outcome?

  1. Crime Patterns: When and where will the next crime take place in a specific region or community?
  2. Crime Classification:
    1. How does the socio-economic background of a person influence theirthe probability of committing a crime?
    2. How does the socio-economic background of the criminal relate to the type ofcrime committed?
  3. Perception of safety among citizens:
    1. What factors influence the perception of safety for European citizens?
    2. Can we explain regional differences, e.g. by looking at the demographics,prevalence of political affiliation or other interesting factors?

Coordination

Nadine Rouleaux

Research Assistant, Institute of Data Science (Maastricht University)

Kody Moodley

Postdoctoral Researcher, Institute of Data Science (Maastricht University)

Pedro Hernandez

Data Scientist, Institute of Data Science (Maastricht University)

Chang Sun

PhD Researcher, Institute of Data Science (Maastricht University)

Michel Dumontier

Distinguished Professor of Data Science, Institute of Data Science (Maastricht University)

Arno Angerer

Master Student, School of Business and Economics (Maastricht University)

Claudia van Oppen

Programme Manager, Institute of Data Science (Maastricht University)

About the Institute of Data Science

Vast amounts of data being generated across all segments of society. If taken advantage of, these data offer an unprecedented opportunity to accelerate scientific discovery, to improve healthcare and wellbeing, and to strengthen our communities. Data Science is an interdisciplinary field concerning the scientific methods, systems and workflows to obtain insights from data. Data Science has the potential to affect all aspects of human activity. We embrace this development and are preparing a new generation of data scientists, scholars and entrepreneurs who work in a collaborative manner to tackle the world’s most pressing problems.

IDS aims to tackle impactful research problems through multidisciplinary teams involving students, researchers, partners, and stakeholders inside and out of the university. We aim to train the next generation of data scientists to be even more collaborative and interdisciplinary.

want to stay in touch?

Deadline
1 February 2018

GET TO KNOW US

technolawgy2017@gmail.com

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