Innovation for Social Good in Scotland Nesta is the UKs innovation - - PowerPoint PPT Presentation

Police Scotland - Data Driven Innovation for Social Good

in Scotland

Nesta is the UK’s innovation foundation

We were established in 1998 and now have over 300 staff in London, Edinburgh, Cardiff and Turin. We are an independent charity registered in Scotland

(SC042833) and have the status of an Independent

Research Organisation. We use methods such as:

• Social innovation labs
• Future-scoping
• Anticipatory regulation
• Innovation grant management
• Challenge prizes
• Citizen engagement
• Innovation mapping
• Data analytics

To bring bold ideas to life that change the world for good

We see We spark We shape We shift

Making sense of opportunities and challenges Generating new ideas Providing help so that promising ideas can grow, adapt and work in practice Changing whole systems

We are a global innovation foundation

• Scottish Leaders Forum and

Evidence and Data sub-group

• Scotland’s AI Strategy Steering

Group and AI Ethics and Regulation Working Group

• Scottish Government’s Digital

Ethics Expert Panel

• Enterprise and Skills Strategic

Board Analytical Unit (ESAU) Innovation Mapping and Evaluation Steering Group

• Scotland CAN DO Business

Innovation Forum

Strategic forums in Scotland

The innovation spiral ...hypothetically

Reality

The ways in which we innovate have evolved dramatically in recent years

The law of the instrument: if all you have is a hammer, then you see everything as a nail

Accelerator programmes Anticipatory regulation Challenge prizes Futures Experimentation Crowdfunding Impact investment Innovation mapping People Powered Results: the 100 day challenge Prototyping Public and social innovation labs Scaling grants for social innovations Standards of Evidence

In April 2019 we published our Compendium of Innovation Methods showcasing 13 proven methodologies to support social innovation.

Innovation Methods - Compendium

Innovation Methods - 20 Tools

In September 2019 we followed this with a new resource in support of innovation in government agencies and public services, showcasing methods like collective intelligence and impact partnerships for the first time.

Data analytics Collective intelligence Technology for democratic engagement Prototyping Experimentatio n 100 Day Challenges Testbeds Innovation labs Challenge prizes New ways of using money Behavioural insights What Works Centres of evidence People powered public services Impact partnerships Digital technologies to enhance services Anticipatory regulation Data governance How to develop and innovative mindset How to change

• perating

models How to use structures to promote innovation

Tech and Data Driven Innovation in Policing

Lots of data and digital driven innovation already going on

COMPAS (US): Applied across various jurisdictions in the United States. It uses an

algorithm to assess an offender’s potential recidivism risk. The variables which inform the tools’ analysis have been kept private by the tool designers. The tool produces a risk score, which is then used by judges to inform decisions around bail and sentencing. COMPAS is also used more broadly to inform decisions regarding resource allocation.

AlgoCare (Durham Police): proposed decision-making framework for the

deployment of algorithmic assessment tools in policing which has been developed in collaboration with Durham Constabulary, showing how ethical considerations, such as the public good and moral principles can be factored in.

PredPol (Kent and Essex Police): In Kent and Essex, the PredPol system was

until recently adopted to predict where crimes may occur. The system is trained using historic crime data and uses this to highlight areas where and when police officers may be needed. PredPol uses three data points; past type, place and time of crime, to create a unique algorithm based on criminal behaviour patterns.

Police Facial Recognition System (South Wales): face scanning

technology cross-references against a database of 500,000 custody images in real- time, helps the police know if there are past offenders at big public events, and has already led to a number of arrests.

Safeland (Sweden): An app first used in Sweden that takes the principles and

• bjectives of Neighbourhood Watch and delivers them through digital technology.

Residents log incidents on the app and give descriptions of suspects and other relevant information to help the police in their investigations.

Offices of Data Analytics (ODAs) in England

Premise: People do not conveniently live out their lives in one local authority area. Communities, areas of deprivation, crime, littering and school catchment areas cut across

• borders. However, public sector organisations’ data and

reach are often notably confined within the boundaries of their geographical area and jurisdiction

• ODAs create a pilot model for multiple organisations to

join up, analyse and act upon data sourced from multiple public sector bodies to improve services and make better decisions.

• ODAs always adopt a shared vision and objectives,

sometimes have shared capabilities and resource,

• ften have a range of collaborative working practises,

and definitely have a commitment to data analytics.

• Ultimately, an ODA creates multi-organisational,

actionable insight from otherwise siloed information.

Opportunities and Risks

Greater pool of insight Faster data processing Trend analysis and greater insight Improved cost analysis Greater community empowerment and engagement Requires cross sector culture change to share, try, fail and learn - this is difficult Raises critical questions of ethics, rights and systems bias Can be difficult to quantify / articulate initial value add It/they may fail

Making the case for data and AI in policing

• It’s a moment in time - an
• pportunity to embrace new

ways of working, with ethics at the core (NB: NPF and AI Strategy)

• Data driven policing is not about

eliminating human responsibility

• In a constant push for efficiencies

and greater impact, forces are being encouraged to make greater use of their data and embrace new tech - Scotland is well placed to lead on this

Supporting productive human-machine interactions

Three key principles which appear to play a significant role in shaping how humans interact effectively with predictive analytics tools:

Context: Introducing the tool with awareness and sensitivity to

the broader context in which practitioners are operating increases the chances that the tool will be embraced by practitioners.

Understanding: Building understanding of the tool means

practitioners are more likely to incorporate its advice into their decision-making.

Agency: Introducing the tool in a way that respects and

preserves practitioners’ agency encourages artificing. Full Report

Some final thoughts

• Enforcing the law and policing will always be a human task

and process - ambiguously written laws, the inconsistencies of judges, juries and police officer discretion are just some of the human elements involved

• But, there can be little doubt that these practices will

increasingly be enhanced by emerging technologies and data driven innovations. These bring with them new challenges of regulation and ethics. We must grapple with these proactively or get left behind.

• Nesta’s hunch is that the greatest value of these technologies

will come from using them to make policing more human, not less: ○ better at collecting information and insights from citizens ○ better at making policing practice visible ○ Better at combining the best of artificial and collective intelligence.

Thank you. Adam Lang Head of Nesta in Scotland