Achieving the SDGs will require supportive national policies, strategies and planning. To design effective policies, policy makers around the world will need to utilize scientific evidence. Developing countries, such as Argentina, have recognized the need to use scientific evidence in policy-making and are interested in pursuing cost effective options for sourcing easy-to-understand scientific information that is relevant to their region. Our solution is a web-based, on-demand, AI-powered service for converting complex critically important scientific data into simple, easy-to-digest summaries.
Achieving SDG 7, Affordable and Clean Energy, by 2030 will require supportive national policies, strategies and planning. In fact, many other SDGs require supportive national policies and measures. In designing effective policies, policy makers around the world will need scientific evidence. In developed countries, these resources and processes to incorporate evidence into policy are available. For example, the UK government has the Parliamentary Office of Science and Technology. This office produces POSTnotes, which are four-page summaries of science relevant for public policy issues. These are based on reviews of the scientific literature and interviews with stakeholders from across academia, industry, government and the social sector. They are also peer reviewed by external experts and impose a yearly cost of more than 1M GBP.
Governments in developing countries would also benefit from this kind of information, but they often don’t have the resources to produce it. It is, of course, possible for government officials to read relevant scientific papers and reports. However, these people are often short on time and usually are not experts in the area they are researching, so they struggle to understand the content. An added issue is that the content may not be tailored to their region and the local issues. The result is policy that is made with irrelevant, inaccurate or incomplete evidence. And a poorly desired policy can have unexpected or undesirable outcomes.
Developing countries, such as Argentina, have recognized this issue and are interested in pursuing cost effective options for sourcing easy-to-understand scientific information that is relevant to their region.
Our solution for governments in developing nations, such as Argentina, is a web-based, on-demand, AI-powered service for converting complex critically important scientific data into simple, easy-to-digest summaries. The process for producing these summaries is broken down into four main steps
1. Creating a Scientific Summary – the AI data engine searches highly credible and relevant scientific documents and produces a single scientific summary
2. Local Scientist Review – three local scientists review the scientific summary for quality and regional relevance
3. Creating a Plain Language Summary – the Tomorrow Matters team converts the scientific summary into a simple, easy-to-digest summary
4. Creating a Local Language Summary – the same three local scientists will translate the simple, easy-to-digest summary into their local language
Our solution is unique, targeting a well-known need in governments in developing countries – that is currently unmet. We are leveraging AI models, which use natural language programming and machine learning, in a sector that currently makes little to use of this kind of technology. We have also created a new model for incorporating science into policy.
Empowering policy makers with high-quality understandable data could have a huge and disruptive effect in the decision-making and implementation of energy and environmental policies. We intend to start in energy policy, but if successful this solution would be useful across many policy areas, in many countries.
We could potentially impact a number of different SDG areas: SDG 6 (6.4, 6.5), SDG 7 (7.1, 7.2, 7.3), SDG 8 (8.4, 8.9), SDG 9 (9.4, 9.5, 9.b), SDG 11 (11.6), SDG 12 (12.2), SDG 13 (13.2), SDG14 (14.b 14.c), SDG 15 (15.6), SDG16 (16.10) and SDG (17.13,17.19)
Between now and launching the platform we will, build the technology and AI, form partnerships with relevant parties, build a local network of scientists, and finally pilot the system.
AI powered architecture/expert, Access to scientific journals and reports, Connections with governments interested in using scientific summaries and evidence in policy making but lack the resources the do this in-house.