Introduction: When Technology Creates New Risks for NGOs
There’s a whole new world opening up for non-government organizations, charities, and aid agencies thanks to artificial intelligence. Just picture what this technology can do to identify the right locations, analyze poverty, and optimize assistance delivery. How could charities ignore the temptation of faster, smarter decisions?
But, as our humanitarian work ventures more deeply into digital territory, we face two major threats: the castle of data security and the swamp of algorithmic bias. Consider this: do good organizations spend time gathering personal information about people experiencing poverty, displacement, or discrimination? This information is a vital lifeline to support services, but it would be extremely dangerous if it got into the wrong hands.
More than that, can AI systems actually be the objective judge that we demand? If we are limited to biased, partial information of the past, they might be better at maintaining inequality than solving it. For aid organizations, the solution isn’t just the introduction of new technology, but that it preserves privacy, that it respects dignity, and that it makes decisions that are just.
Why Data Security Matters for Humanitarian Organizations
Data is the lifeblood of any modern aid operation. Think about all the information NGOs collect: beneficiary details, income levels, healthcare requirements, family connections, location, education and employment history, and access to basic services. How do these things fit together? They create a clear picture that allows organizations to understand the needs of communities, identify their beneficiaries, determine the effectiveness of projects, and illustrate impact to donors.
And yet, the harvesting of personal information looms as a source of ongoing responsibility. What if sensitive data falls into the wrong hands? The consequences could go well beyond financial loss or operational disruption. Imagine the locations of displaced families exposed, the dignity of rape survivors compromised, and the political asylum seekers exploited.
And in conflict zones, unstable regions, or persecuted communities, they get worse, and they get more perilous. Are we prepared to be that naive? Protecting data needs to be right at the core of what we do, not just a technology issue.
The Red Cross Cyberattack: A Warning for the Humanitarian Sector
The ICRC was also confronted with a grim reality in January 2022 when a highly advanced cyberattack infiltrated their servers, resulting in the leak of personal information. How do you safeguard the most vulnerable when the world’s ‘guardians’ are no longer able to keep themselves safe? The breach targeted information of more than 515,000 missing persons, families separated by armed conflict or migration, and aid recipients—from at least 60 Red Cross and Red Crescent National Societies.
The attack not only pilfered data, but it also interrupted the ICRC’s critical Restoring Family Links program, which reunites families torn apart by war, disaster, and migration. As the organization rushed to respond, the attack left compromised systems temporarily offline, underlining how no organization is safe,” said a statement.
This is a reality check: our data is not just data; it is real lives, stories, and experiences. The damage caused by a data breach is compounded when the breach involves refugees, missing persons, or individuals in conflict zones.
It’s a lesson that applies even to small NGOs too. You don’t have to be handling millions of records to attract unwanted attention. A small database with basic information about your beneficiaries or your donors, for example, could put vulnerable people in harm’s way, making data security a concern for even the smallest organizations. So how do we keep those tools safe?
How AI Increases Data Privacy Concerns
AI systems often require information to generate recommendations, identify patterns, summarize documents, or predict potential outcomes. Depending on how a tool is designed and used, the information entered into it may include confidential or personally identifiable details.
This creates several challenges for NGOs.
1. Sharing sensitive information with AI tools
NGO worker training an AI chatbot to help put together a project proposal, to summarize interviews with project beneficiaries, to analyze survey data, or to write a donor report. Then imagine that, unintentionally, they input donor, beneficiary, or staff names and addresses; medical histories or other diagnosis information; identification documents; or other sensitive case records to the chatbot. Would that have a risk of exposing that information? The answer depends on the privacy, data retention, security, and contractual arrangements of each AI tool.
2. Unauthorized access and cyberattacks
Humanitarian organizations may store information across laptops, email accounts, cloud platforms, databases, and third-party software. Every additional system or connection can introduce security considerations.
Weak passwords, phishing emails, outdated software, excessive access permissions, and poorly secured databases can make it easier for unauthorized individuals to gain access to confidential information.
AI adoption can add further complexity when staff connect external applications or upload organizational documents to services that have not been properly reviewed.
3. Third-party service providers
Many NGOs rely on external providers for cloud storage, data analysis, communications, and AI-powered services. These partnerships can be useful, especially for organizations without large technology teams.
However, NGOs remain responsible for understanding the risks associated with the information they share. They should assess providers’ security practices, data handling arrangements, retention policies, and procedures for reporting breaches.
A convenient tool should not automatically be considered a safe tool.
4. Collecting more data than necessary
Organizations sometimes collect extensive personal information because it may prove useful for future analysis or reporting.
But storing information without a clear purpose increases the potential impact of a breach. It can also make it harder to manage consent, access requests, and retention requirements.
A more responsible approach is to collect only the information needed for a clearly defined purpose and establish a schedule for reviewing and deleting records that are no longer required.
Understanding Algorithmic Bias in AI Systems
Data is only the tip of the iceberg. But what about the equity of AI recommendations? How can NGOs make sure AI systems make equitable decisions? Algorithmic bias is the monster hiding in the closet, taking credit when automated systems consistently produce unfairly positive results for specific groups or individuals. Data bias can take root in the data used to train an algorithm, in the framing of the problem, in the selection of variables, or in the fundamental assumptions underlying the model.
AI programs learn from the patterns in the data they digest. If the data contains echoes of past biases, disparities in service, or underrepresentation, the program may repeat the patterns. But this can be hard to detect; recommendations generated by computers can seem scientific and objective, and if the recommendation was made by a computer, it must be fair, right?
This is especially important for human rights organizations. Decisions about allocations of aid, prioritization of communities, deployment of resources, and the definition of success for project outcomes are all driven by AI.
How Algorithmic Bias Can Affect NGO Programs
1. Bias in beneficiary selection
Imagine an NGO using an AI system to identify households most likely to benefit from emergency financial assistance.
The system evaluates income records, employment history, household size, access to services, and other available information.
However, some of the poorest households may have incomplete documentation because their members work in informal employment, have been displaced, or lack access to formal financial services.
If the system treats missing information as evidence of lower need, these households could receive lower priority despite experiencing severe hardship.
The algorithm may appear to apply the same rules to everyone, but those rules may disadvantage people whose circumstances are poorly represented in the data.
For this reason, beneficiary selection should not depend entirely on automated scores. NGOs need clear eligibility criteria, community-level verification, accessible appeals processes, and human review of uncertain or exceptional cases.
2. Gender bias in employment and livelihood programs
Consider an NGO running a vocational training program intended to improve employment opportunities for women and young people.
An AI system recommends participants based on previous employment records, work experience, availability, and predicted job placement rates.
If historical employment data reflects unequal access to education, professional networks, or paid work, women may receive lower scores than men with similar potential.
Women who have taken career breaks for caregiving responsibilities may also be disadvantaged by criteria that reward uninterrupted employment histories.
The result could be that the people a program was designed to support are overlooked by the very system intended to improve its effectiveness.
NGOs should therefore examine whether selection criteria measure genuine programme needs or simply reproduce patterns from an unequal past.
3. Racial, ethnic, and social discrimination
In some settings, historical records may reflect unequal access to healthcare, education, housing, financial services, or public assistance.
If an AI system uses these records without considering their limitations, it may incorrectly interpret existing disadvantage as evidence that certain communities are less deserving of support or less likely to succeed.
The same concern applies to language and geographical representation. A model trained primarily on data from urban or well-documented populations may perform poorly when evaluating rural communities, minority-language speakers, or people living in areas with limited digital access.
These risks are particularly relevant to international NGOs working across different countries and cultural contexts.
An AI system that performs reasonably well in one community cannot automatically be assumed to produce reliable results in another.
4. Bias in needs assessments and impact measurement
AI tools can help NGOs analyze survey responses, summarize field reports, and identify patterns across large datasets.
However, the resulting analysis may be incomplete if certain groups are underrepresented in the information collected.
For example, an online survey may receive responses primarily from people with internet access, while excluding individuals who are less digitally connected. An AI-generated report based on those responses might underestimate the needs of the excluded population.
Similarly, if an organization measures success using indicators that are easy to quantify, it may overlook important changes such as improved confidence, stronger community relationships, greater personal safety, or increased participation in decision-making.
AI can help interpret evidence, but it cannot automatically correct weaknesses in the evidence itself.
Why These Two Challenges Are Connected
Data security and algorithmic bias are often discussed separately, but they are closely related.
Bias depends partly on the information available to a system. Protecting that information is essential, but so is understanding whether it accurately represents the people being served.
For example, an NGO might securely maintain a database containing beneficiary records. However, if the database excludes people without formal identification, the organization may still develop an incomplete understanding of community needs.
Conversely, an organization might build a relatively representative dataset but store it in an insecure environment, exposing vulnerable people to privacy risks.
Responsible AI requires both protecting information and ensuring that its use does not create unfair outcomes.
Neither strong cybersecurity nor accurate predictions alone are sufficient. Humanitarian organizations must consider privacy, fairness, accountability, and the real-world consequences of automated decisions together.
Practical Steps NGOs Can Take to Protect Beneficiary Data
The good news is that organizations do not need enormous technology budgets to begin improving their data protection practices. Many meaningful safeguards start with clear policies, staff awareness, and responsible everyday decisions.
1. Establish a clear data protection policy
Every NGO should define what information it collects, why it collects it, who can access it, where it is stored, and how long it is retained.
The policy should also explain how staff may use AI tools, which categories of information must never be entered into unapproved services, and what steps employees should take if they suspect a breach.
Policies should reflect applicable data protection laws, donor agreements, and the risks associated with the communities the organization serves.
2. Use strong passwords and multi-factor authentication
Employees should use unique passwords for organizational accounts and enable multi-factor authentication wherever possible.
Multi-factor authentication adds another layer of protection by requiring an additional verification step when someone signs in.
Organizations should also regularly review account permissions, promptly remove access when employees leave, and avoid sharing login credentials among team members.
These measures are relatively straightforward and can significantly strengthen basic account security.
3. Limit access to sensitive information
Not every employee needs access to every beneficiary record.
An NGO can reduce exposure by giving staff access only to the information required for their responsibilities. Financial records, medical details, case histories, and identifying information should receive appropriate levels of protection.
Where possible, organizations should use anonymized or pseudonymized data for analysis. Anonymization removes identifying details, while pseudonymization replaces direct identifiers with substitutes but may still allow identification if additional information is available.
These methods reduce certain risks, although they do not guarantee that re-identification is impossible.
4. Evaluate AI tools before using them
Before adopting an AI service, NGOs should review its privacy terms, security measures, data retention practices, access controls, and contractual protections.
They should determine whether submitted information may be retained or used to improve the service, whether administrators can manage access, and whether the provider offers appropriate protections for confidential organizational data.
Where the risks are significant, organizations should consult their legal or data protection advisers before processing sensitive information.
Staff should use approved tools and avoid uploading identifiable beneficiary records unless there is a legitimate purpose and suitable safeguards are in place.
5. Train staff and volunteers
Human error remains an important cybersecurity risk. A staff member may accidentally share a confidential document, click a fraudulent link, or enter sensitive information into an inappropriate application.
Regular training should cover phishing, password safety, secure file sharing, privacy expectations, and responsible AI use.
Training should be practical and relevant to daily NGO work. Employees should understand not only which actions are prohibited, but also why those actions can endanger the people the organization serves.
6. Prepare an incident response plan
Every organization should know what to do if information is lost, stolen, or accessed without authorization.
A basic response plan should identify the people responsible for investigating incidents, securing affected systems, assessing potential harm, notifying relevant authorities or affected individuals where required, and restoring services safely.
Organizations should periodically test their plans and ensure that staff know how to report suspicious activity without delay.
For humanitarian NGOs, an incident response plan should consider beneficiary safety alongside technical recovery. In certain situations, the priority may be preventing sensitive information from being used to locate or target vulnerable people.
How NGOs Can Reduce Algorithmic Bias
Protecting information is only the first step. Organizations must also ensure that AI-assisted decisions remain fair, explainable, and accountable.
1. Examine the quality and representation of data
Before using an AI system, NGOs should ask whether the data represents the communities they serve.
Does it include rural and urban populations? Are women, people with disabilities, minority-language speakers, displaced people, and other relevant groups adequately represented? Are some records missing because certain communities have limited access to formal systems?
The answers will depend on the programme and context. Organizations should identify relevant gaps rather than assume that every dataset is equally representative.
2. Test outcomes across different groups
Where appropriate and lawful, NGOs should examine whether an AI system produces substantially different outcomes for different demographic groups.
For example, an organization using AI to shortlist applicants for a training programme could compare selection rates across relevant groups and investigate unexplained differences.
Such comparisons do not automatically establish that discrimination has occurred. Differences may have several causes, and demographic data itself must be handled carefully. Nevertheless, testing can reveal problems that would otherwise remain hidden.
3. Keep humans involved in important decisions
AI should support professional judgment rather than replace it in decisions that significantly affect people’s lives.
An automated system may help organize applications, identify missing information, or flag cases for additional review. However, decisions involving emergency assistance, safeguarding, healthcare, or other high-impact services require appropriate human oversight.
Human reviewers should have the authority and information needed to question recommendations, correct errors, and consider circumstances that the system may not recognize.
Human involvement must also be meaningful. Simply asking an employee to approve every AI recommendation without reviewing the evidence does not provide effective oversight.
4. Make decisions understandable
NGOs should be able to explain, in language that affected people can understand, how AI contributes to decisions about eligibility, prioritization, or access to services.
Where an automated recommendation influences a decision, organizations should document the criteria used, the system’s limitations, and the process for correcting errors or challenging an outcome.
Beneficiaries should have an accessible way to ask questions and seek a review, particularly when an incorrect decision could prevent them from receiving essential assistance.
Transparency helps organizations identify mistakes and strengthens accountability to the communities they serve.
5. Involve communities in technology decisions
People affected by a programme should not be treated merely as sources of data.
NGOs can consult beneficiaries, local leaders, community organizations, and frontline workers when selecting or evaluating AI systems. These discussions can reveal practical concerns that may not appear in a dataset or technical assessment.
For example, a system may use criteria that appear reasonable to its developers but fail to reflect local employment patterns, caregiving responsibilities, cultural practices, or barriers to accessing services.
Community participation helps organizations recognize these limitations and design more appropriate solutions.
6. Monitor systems after deployment
Bias is not always visible during initial testing. Community needs, data quality, and programme conditions can change over time.
NGOs should periodically review AI-assisted decisions, investigate complaints, and assess whether the system continues to perform appropriately.
If an AI tool consistently produces unreliable or unfair outcomes, the organization should be prepared to modify its use, introduce additional safeguards, or stop using it altogether.
Responsible AI adoption is an ongoing process, not a one-time approval.
The Role of Donors and Technology Partners
NGOs should not have to manage every aspect of data security and AI accountability alone. Donors, technology companies, governments, and humanitarian networks also have important responsibilities.
Donors can support stronger cybersecurity by funding essential infrastructure, staff training, secure data storage, and independent assessments. These investments should be recognized as part of programme delivery rather than treated as unnecessary administrative costs.
Technology providers should explain their products’ limitations, offer appropriate security controls, and make their data handling practices understandable to the organizations using them.
Partnership agreements should clearly establish responsibilities for protecting information, responding to security incidents, and addressing problems with automated decisions.
Funders can also encourage responsible innovation by asking organizations how they protect beneficiary information and evaluate AI-related risks, without creating reporting requirements that smaller NGOs cannot realistically meet.
For grassroots organizations, affordable tools, accessible guidance, and practical training can make a substantial difference.
Responsible AI Does Not Mean Avoiding Technology
The risks associated with AI should not lead NGOs to abandon technological innovation altogether.
When properly selected and managed, AI can help organizations analyze complex information, prepare reports, identify potential service gaps, translate materials, and reduce repetitive administrative work. These benefits can be particularly valuable for smaller NGOs with limited staff and resources.
The objective is to adopt technology in ways that support humanitarian goals without creating avoidable harm.
For example, an NGO could use an approved AI tool to summarize anonymized survey responses, helping its team identify recurring community concerns more quickly. Staff could then validate the findings, consult local stakeholders, and decide how to respond.
Similarly, AI could help organize beneficiary applications, while trained staff apply transparent eligibility criteria and review unusual cases.
In both situations, technology improves efficiency without becoming the sole authority over sensitive decisions.
The most appropriate approach depends on the task, the sensitivity of the information, the reliability of the tool, and the potential consequences of an error.
How NGOs.ai Can Support More Responsible NGO Work
Digital platforms designed for NGOs can help simplify administrative tasks such as drafting proposals, preparing concept notes, developing budgets, and organizing project information.
For example, NGOs.ai provides AI-powered tools intended to support common fundraising and project-planning activities. Such tools can help organizations develop initial drafts and structure their ideas, allowing teams to spend more time reviewing project needs and refining their work.
However, convenience should always be accompanied by responsible data practices.
NGOs using any AI platform, including NGOs.ai, should avoid submitting confidential beneficiary details or other sensitive information unless the service’s data handling practices have been reviewed and the use is appropriate. They should also review generated content for accuracy, fairness, and relevance to the communities involved.
AI-generated drafts should remain starting points for human review, not automatic substitutes for professional judgment, community knowledge, or donor-specific requirements.
The broader principle is simple: technology should help NGOs work more effectively while preserving the trust placed in them by beneficiaries, donors, and communities.
Building a Culture of Trust, Privacy, and Accountability
Can technical solutions singlehandedly address the huge challenges of data security and algorithmic bias? Not really. Technical solutions require an organisation to be created in which staff understand their roles, managers are risk-aware, and people who benefit from programs are treated with respect. Picture a workplace where staff are confident to challenge an automated decision they believe is wrong, or flag a potential data breach, or challenge a recommendation generated by AI that conflicts with program goals. That transparency is critical.
Organizations should also be open about what their technology cannot do. AI tools don’t always get it right, they can be unaware of important context and their recommendations may require additional verification. But does admitting to shortcomings threaten an NGO’s reputation? Certainly not – in fact, it shows a desire to understand responsibilities and improve.
Trust is the foundation of the humanitarian ecosystem. Information donors entrust humanitarian actors with their personal data-resilience, needs, and vulnerabilities-to facilitate interventions and reduce harm. This trust spans the entire data lifecycle: from collection through management to use and disposal.
Conclusion: Protecting People Must Come Before Technology
AI is enabling humanitarian organizations, but how do we navigate its potential with the social costs it can cause? In 2022, a cyberattack on the International Committee of the Red Cross (ICRC) highlighted the risk of using humanitarian data: placing it in the hands of hackers can seriously damage the very people we’re trying to protect, even the networks we’re trying to support. Algorithmic bias only adds to the risk.
The key to a balanced approach. Ramp up your cybersecurity. Only collect what you need. Vet your AI suppliers. Identify and mitigate biases. Guarantee human oversight. Consult your communities on decisions that affect them.
Does this mean your organization needs to suddenly ramp up its team and expertise to become an AI or cybersecurity company? Not necessarily. It will require a sustained focus on accountability, education, and responsible doing.
At the end of the day, how should we judge AI’s effectiveness in the humanitarian space? It’s not just a matter of how fast it can sift through data or alleviate admin tasks. It’s about how well it supports organizations’ efforts to treat every person fairly, protect their privacy, and respect their dignity.
Technology should strengthen humanitarian work, not create new risks for the people it is meant to help. For NGOs, responsible AI begins with a simple principle: protect people first, and let technology serve that purpose.
