- 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, , 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

Making sense of opportunities Generating new ideas and challenges

We shape We shift

Providing help so that promising Changing whole systems ideas can grow, adapt and work in practice We are a global innovation foundation

● Scottish Leaders Forum and Strategic Evidence and Data sub-group ● Scotland’s AI Strategy Steering Group and AI Ethics and forums in Regulation Working Group

● ’s Digital Scotland Ethics Expert Panel

● Enterprise and Skills Strategic Board Analytical Unit (ESAU) Innovation Mapping and Evaluation Steering Group

● Scotland CAN DO Business Innovation Forum 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 Innovation Methods - Compendium In April 2019 we published our Compendium of Innovation Accelerator Anticipatory Challenge Methods showcasing 13 programmes regulation prizes proven methodologies to support social innovation. Futures Experimentation Crowdfunding

Impact Innovation People investment mapping Powered Results: the 100 day challenge

Prototyping Public and Scaling grants social for social innovation labs innovations

Standards of Evidence 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 Collective Technology Prototyping Experimentatio analytics intelligence for n democratic engagement

100 Day Testbeds Innovation Challenge New ways of Challenges labs prizes using money

Behavioural What Works People Impact Digital insights Centres of powered partnerships technologies to evidence public services enhance services

Anticipatory Data How to How to How to use regulation governance develop and change structures to innovative operating promote mindset models innovation Tech and Data Driven Innovation in Policing COMPAS (US): Applied across various jurisdictions in the United States. It uses an Lots of data 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 and digital 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. driven AlgoCare (Durham Police): proposed decision-making framework for the deployment of algorithmic assessment tools in policing which has been developed in innovation collaboration with , showing how ethical considerations, such as already the public good and moral principles can be factored in. PredPol (Kent and ): In Kent and Essex, the PredPol system was going on 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 objectives 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, often 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 Requires cross sector culture change to share, try, fail and Faster data processing learn - this is difficult

Trend analysis and greater Raises critical questions of insight ethics, rights and systems bias

Improved cost analysis Can be difficult to quantify / articulate initial value add Greater community empowerment and It/they may fail engagement ● It’s a moment in time - an Making the case for opportunity to embrace new ways of working, with ethics at data and AI in the core (NB: NPF and AI policing 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 ● Enforcing the law and policing will always be a human task Some and process - ambiguously written laws, the inconsistencies of judges, juries and police officer discretion are just some of the final human elements involved thoughts ● 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