Overcoming common design challenges
Overcoming common design challenges
Below provides an overview of three overarching issues that are critical for making the most of collective intelligence climate initiatives: participation, data utility and shifting towards action. For each of these issues, we describe common challenges faced by collective intelligence and suggest design tactics to overcome them. It is not a comprehensive analysis of optimizing the design of collective intelligence. Instead we focus on evidence derived from the case studies in this report.
We have prioritized challenges that can be at least partially addressed through better design rather than focusing on systemic barriers such as absence of political will, organizational culture and lack of financial support. We hope that the practical focus on design will be useful for frontline innovators in the climate space to help them deploy collective intelligence solutions more effectively.
Common challenges for climate collective intelligence initiatives and how to overcome them
Overarching issues | Challenges | Design tactics |
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Participation – mobilizing communities for climate action | Lack of engagement or failure to sustain engagement over time This is a common challenge for all collective intelligence initiatives and may be caused by practical considerations such as contributions or tasks that are too difficult for volunteers, or inappropriate incentives for people to take part in fully. |
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Low motivation to contribute due to other, more pressing, issues and concerns or participation fatigue Some issues associated with climate change may seem irrelevant or disconnected from the main sources of stress in the daily lives of target communities. People become frustrated after being asked to contribute multiple times, without evidence of concrete benefits from their engagement in the past. |
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Hopelessness and perceived lack of efficacy of climate action Only a few studies focus on the psychological impacts of climate change on people in the Global South, particularly where climate change is already causing disruption to homes or livelihoods. Early evidence suggests that the negative emotional impact of this experience may lead to mental health challenges that decrease motivation to engage. |
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Lower participation from certain groups e.g. women, older people, people with disabilities In some parts of the Global South such as sub-Saharan Africa, uptake of technology such as mobile phones and use of the internet is higher among working-age men than women and older people. This means that collective intelligence initiatives may struggle to engage these groups. These disparities can be difficult to quantify because collective intelligence initiatives often don’t report participation data disaggregated by gender, age, disability, etc. |
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Data utility – improving usability and usefulness of collective intelligence data | Small datasets or “patchy” data Some collective intelligence projects collect datasets that are limited to hundreds of datapoints making them unsuited for larger scale data analysis. And even when data is collected at scale it can be “patchy” meaning that there are gaps or inconsistencies in time or missing locations. This is particularly common for biodiversity data or monitoring of environmental variables like air pollution and water quality. |
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Messy and inconsistent data protocols Collective intelligence initiatives often reinvent the wheel when designing their data collection process. This makes it difficult to integrate datasets between different projects working on the same topic, which limits their impact. Collection of qualitative insights or data about preferences and attitudes towards climate issues is often more ad-hoc. Data tends to be collected as free text, which becomes increasingly difficult to make sense of when participation numbers are high. |
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Concerns about data quality Studies comparing citizen generated data with data collected by experts in citizen science have shown comparable quality in several citizen science environmental monitoring projects. But concerns persist among decision makers and institutions, particularly when there is no official guidance about how and when to use non-traditional data sources. |
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Action – shifting from “data for knowledge” towards “data for action.” | Decision makers (public and private) fail to act on citizen generated data. A lack of technical skills amongst decision makers can make it difficult for them to interpret data and models – especially when issues are complex and intersecting, for example health policy makers that need to interpret how environmental variables affect the risk of infectious disease. The data collected by citizens and outputs of models does not always match the specific interests of policy makers or comes at the wrong time in the policy cycle. |
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Data from collective intelligence isn’t shared directly with the people contributing. |
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Contexts where there is limited internet and low digital literacy.