Report

Combining crowds and machines

Experiments in collective intelligence design 1.0
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Data processing Artificial Intelligence (AI) Collective impact Data collection platforms Machine learning
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Combining crowds and machines 3.48 MB
Description

Tackling some of the most complex challenges of our time requires progress in how we think and act together. New technologies, including artificial intelligence (AI), allow us to mobilise human intelligence in new ways and at greater scale.

Nesta’s Centre for Collective Intelligence Design has been focusing on advancing the knowledge and practical applications of collective intelligence in fields with public benefit, such as health, international development or digital democracy. Yet, in spite of the many emerging opportunities to use novel combinations of human and machine intelligence, we still know relatively little about what works and how to do it well.

Through their Collective Intelligence Grants Programme, Nesta has supported 12 diverse organisations worldwide to conduct practical experiments that increase our understanding of how to make the most of the new technologies available to help with collective thinking and acting.

The experiments contribute new insights into how we can improve our decision-making, enable effective co-operation, make better use of citizen-generated data and increase the effectiveness of participation in collective intelligence initiatives.

Key insights:

  1. To make better decisions, delegate to AI – Passing responsibility to autonomous agents increased cooperation and coordination between groups.
  2. Want happier voters? Let them swarm! – Politically polarised British voters were happier with decisions made through ‘swarming’ and Borda count methods; majority voting consistently produced the least satisfactory decisions.
  3. Use AI to stop people following the herd – Mediating group decisions through an AI system reduced the tendency of people to go along with the group majority and led to more accurate outcomes.
  4. When fast action is needed, let the crowd self-organise – In time-critical scenarios, such as food rescue efforts, effective coordination among different actors is key. Decentralising coordination through a collective intelligence platform allowed volunteers to adapt to a changing situation and led to a significant increase in the amount of food that was rescued.
  5. To make digital democracy work better, use AI to help people find similar ideas – Natural language processing could improve the effectiveness of citizen participation on digital platforms, mainly by reducing the time it takes to find similar proposals.
  6. Offering better rewards or more varied tasks doesn’t get better results from crowdworkers – When analysing Twitter data for disaster recovery, higher pay seemed to have an adverse effect on labelling accuracy.
  7. AI recommendations can increase the engagement of citizen scientists by helping them discover less popular projects – Recommendation algorithms can better match users to projects they are interested in, increasing the activity of volunteers.

 

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CC BY-NC-SA