Frequently Asked Questions

1. Realising Scale FAQs

What is the GSMA’s role in AI for Impact projects?

The primary role of the GSMA in AI for Impact projects is to identify the fundamental aspects of scaling up big data solutions sustainably to a wide range of humanitarian and environmental issues. The GSMA is doing this by bringing together stakeholders from the private sector, humanitarian agencies and governments, and by establishing a common framework for implementing mobile big data projects. To highlight best practices, the GSMA is also capturing the learnings from projects in published case studies and is sharing knowledge globally through this external proposition toolkit.

How did the GSMA support operators during Covid-19?

In response to the pandemic, mobile operators worked with governments and international agencies in at least 40 countries to better understand and respond to the virus. With the support of the UK Foreign, Commonwealth and Development Office, the GSMA responded to requests from 14 low and middle income countries (LMICs), and supported development of mobile big data products in the Democratic Republic of Congo, Benin, Rwanda and Burkina Faso.

2. The Ecosystem FAQs

Who do I involve when setting up an AI for Impact project?

A successful AI for Impact project depends on identifying the relevant stakeholders from the outset, both on the demand-side and the supply-side, and involving them throughout. Each use case will likely have a specific set of internal and external stakeholders; you can find baseline descriptions of the main stakeholders and their functions in the ‘Ecosystem’ section of the toolkit.

I want to set up an AI for Impact project, what are the key stages for developing a solution?

The GSMA has developed a six-phase engagement process as a guide for anyone wishing to set up a project of this nature. This process is designed to drive collaboration amongst the local stakeholders and to successfully deliver scalable, sustainable and replicable MBD products and services. It is illustrated in the diagram below.

GSMA AI4I Implementation Process

For detailed information about this engagement process and practical experience gained from applying it during the Covid-19 pandemic read the report Utilising mobile big data and AI to benefit society.

Another useful resource is the baseline description for an AI4I implementation, which can be found here. This provides a checklist of activities to include within an implementation project.

3. Technical Considerations FAQs

There are several different mobile operators in my country. Do I need all of their data in order to make the mobile data representative?

In most countries, mobile penetration is very high and many operators hold a market share that covers a significant proportion of the population. This market share generally translates into a larger and richer sample than you would obtain from using traditional means of data gathering. When developing your solution, it is important to work with the mobile operator to understand their customer base, which will ensure that the most accurate insights are delivered.

How do I know that the data is not biased?

Bias may be present to some degree when analysing data, for several reasons, including the fact that mobile network coverage and customers may not be uniformly distributed across a country. Bias may also be present in non-mobile data sets, for a variety of reasons, such as the sampling method, or the difficulty in capturing data for certain demographics and geographies. It is important in the design and planning stages to quantify the potential biases, understand their impacts, and correct for them, where possible. The best way to understand any biases in mobile data is to ensure that you work closely with the relevant mobile operator, to benefit from their knowledge of their customer base and how this will influence the subsequent insights that the mobile data delivers.

Do mobile phone companies have the expertise to understand how their data can be used for development purposes?

A key element of building a sustainable big data solution is ensuring that all parties in the ecosystem are involved at the right time, in the right capacity. Mobile operators can process and configure mobile data using their expertise in network operations and their knowledge of their network and customer base. These crucial steps enable mobile big data to generate actionable insights that will enable you to make well-informed decisions. Operators may also have knowledge of previous, successful applications of mobile data in other spaces. Working collaboratively will lead to the effective employment of the available expertise on both the supply-side and the demand-side to find the development applications with the most potential. This mode of working collaboratively is found throughout the cases in the Covid-19 Case Studies .

Can mobile big data replace other forms of data?

The value of mobile big data is in its ability to give us a richer understanding of the world around us. Rather than replacing other forms of data, mobile big data complements existing data sources and can bring to light otherwise invisible phenomena, enabling you to make more robust, evidence-based decisions.

Will the demand side agency (DSA) of a big data project need a data specialist?

The DSA will not necessarily require a data scientist with implementation expertise, as this can be managed by the mobile operator. However, a foundational understanding of how data works will enable you to make the most of a big data for social good implementation. Of course, statistical knowledge of your domain and relevant data that your own organisation holds is critical. It is also important to work with the operator to make sure any specific considerations are taken into account.

4. Policy and Regulation Section FAQs

Can my agency/organisation’s statistics office have access to the raw data?

No. In order to protect the privacy of individuals, access to raw data should be limited and controlled. Operators are under legal and regulatory obligations to protect their data and should ensure that data used for the MBD projects are non-identifiable. Limiting access also reduces the risk of data breaches. Furthermore, operators possess the expertise and understanding of their own systems to ensure that the results are accurate, while safeguarding the rights of their users.

Are ethical issues considered in the context of AI for Impact?

The GSMA’s Big Data Privacy paper highlights the importance of considering the overall fairness and ethical dimension of what big data analytics services are designed to do, and the intentions of the third parties accessing the resulting insights. This, coupled with the AI Ethics principles, ensures that projects are designed with privacy and ethics at the core to minimise harm to users.

Do I need multiple agreements when I include third parties?

Contractual agreements provide governance mechanisms for the data processing and handling in MBD projects. When collaborating with several organisations you must ensure that any privacy obligations are set out clearly, and each party knows what they will be responsible for and what they expect of each other. You may be accountable for making sure that privacy obligations also apply to third parties.

Do I need to involve mobile phone regulators?

Laws and regulations will vary by country and mobile phone operators may be subject to licensing conditions and sector specific regulations. All stakeholders in the ecosystem should have a good understanding of the legal and regulatory landscape of the country where they are implementing their mobile big data solution. Once these are identified, it is important to assess whether regulators or other government officials in specific departments need to be notified on a case-by-case basis.

Has the AI4I initiative worked on Covid-19 contact tracing apps?

No. The AI4I initiative supported a wide variety of projects during the Covid-19 pandemic. These used aggregated, anonymized data to create population-level insights whilst upholding the standards laid out in the ‘Covid-19 Privacy Guidelines’.

5. Sustainable Business Section FAQs

What do you mean by sustainable? What is a sustainable business model?

‘Sustainable’ means that an initiative isn’t just a one-off. The AI for Impact initiative uses the term to refer to a project that receives the resources and funding required to remain relevant in the long-term. A sustainable business model is the funding mechanism that ensures that each party involved in delivering a solution has a means of funding the resources that are needed to support the work on an ongoing basis, and an incentive to invest in the solution in future.