The nonprofit sector has long relied on innovation to expand its reach and improve its impact. Today, artificial intelligence (AI) offers unprecedented opportunities for nonprofits to operate more efficiently, personalize outreach, and enhance decision-making. However, the adoption of AI comes with significant challenges that nonprofits must address to fully and ethically realize its potential. Five key areas demand attention: data privacy and security, intellectual property concerns, governance and ethical oversight, risk management, and training and education.

1. Data Privacy and Security

Data is the lifeblood of AI systems, but with great power comes great responsibility. Nonprofits handle vast amounts of sensitive information, from donor records to beneficiary details. As they integrate AI into their operations, ensuring data privacy and security becomes paramount.

AI systems rely on vast datasets to function effectively, which often include personal information. Nonprofits must comply with privacy laws such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. These regulations impose strict requirements for the collection, storage, and use of personal data. Nonprofits must establish robust data protection measures to avoid breaches that could compromise donor trust and organizational integrity.

One major risk lies in sharing sensitive data with AI platforms. When data is input into AI tools, there is often a lack of clarity about how that data will be used or stored. Many platforms’ terms of service allow them to use input data to train their models, which could lead to proprietary or sensitive information being inadvertently exposed. For nonprofits, this could mean that donor lists or internal strategies could become accessible to competitors or other third parties.

To mitigate these risks, nonprofits should:

• Implement strict data governance policies.
• Vet AI platforms carefully for their privacy and data-use policies.
• Limit the sharing of sensitive or proprietary data with external platforms.
• Regularly update cybersecurity protocols to guard against breaches.

By treating data privacy and security as foundational elements of their AI strategy, nonprofits can maintain the trust of their stakeholders while leveraging the benefits of AI.

2. Intellectual Property and Copyright

The use of AI brings with it complex questions about intellectual property (IP) rights and copyright. Nonprofits often create and use content—whether marketing materials, research reports, or digital assets—that could be impacted by AI.

One pressing concern is the ownership of AI-generated content. Historically, copyright law has required “human authorship” for protection. This means that outputs from AI systems may not be copyrightable unless significant human creativity is involved in the final product. For nonprofits producing reports or digital campaigns using AI, clarifying ownership is critical to avoiding disputes.

There are also risks associated with inputs. For example, when nonprofits input proprietary materials into an AI tool, those materials might inform the platform’s future outputs for other users. This raises concerns about whether the nonprofit’s IP could be indirectly shared or even exploited without their consent. Additionally, data scraping practices used to train AI systems might inadvertently violate copyright laws, creating legal exposure for organizations.

To navigate these challenges, nonprofits should:

• Review the terms of use for any AI platforms they engage with to understand ownership and usage rights.
• Avoid using proprietary or confidential materials as inputs without clear protections.
• Establish internal policies on the use of AI-generated content, including how it is attributed and shared.
• Monitor developments in copyright law and adjust practices accordingly.

Taking these precautions ensures that nonprofits can innovate responsibly without jeopardizing their intellectual property or violating others’ rights.

3. Governance and Ethical Oversight

As AI becomes more integrated into nonprofit operations, establishing a governance framework is essential. Governance in this context refers to creating policies, structures, and accountability mechanisms to oversee AI’s ethical and effective use.

Ethical concerns are at the heart of AI governance. AI systems are only as unbiased as the data they are trained on, and without oversight, they can perpetuate discrimination or inequality. For example, an AI-driven donor outreach system might prioritize certain demographics over others, inadvertently sidelining potential supporters. Similarly, AI tools used in hiring or program eligibility determinations could embed systemic biases.

To address these risks, nonprofits should create an AI governance framework that includes:

• Ethical principles such as fairness, transparency, and accountability.
• Defined roles and responsibilities for board members, staff, and external advisors in overseeing AI use.
• Regular audits to assess the impact of AI systems on organizational goals and stakeholders.

Transparency is another key element. Nonprofits must communicate openly with their stakeholders about how AI is being used, what data it relies on, and how decisions are made. Engaging stakeholders—from donors to beneficiaries—in these discussions fosters trust and ensures that AI aligns with the organization’s mission.

4. Risk Management and Liability

AI adoption introduces new risks that nonprofits must actively manage. These include reputational risks from inaccurate or biased outputs, legal risks related to data use and copyright, and operational risks from technology failures.

For example, if an AI tool generates false or misleading information that a nonprofit publishes, the organization could face reputational damage or even legal action. Similarly, security breaches targeting AI systems could expose sensitive data, leading to financial and ethical repercussions.

To mitigate these risks, nonprofits should:

• Conduct regular impact assessments to identify potential vulnerabilities in AI systems.
• Develop an incident response plan specifically for AI-related issues, such as errors or breaches.
• Invest in liability insurance that covers AI-related risks.
• Work with legal counsel to ensure compliance with evolving AI regulations.

Additionally, nonprofits must be cautious about over-relying on AI. While AI can enhance decision-making, it should not replace human judgment. Ensuring that AI complements rather than dictates organizational decisions is a key aspect of risk management.

5. Training and Education

The successful adoption of AI in nonprofits hinges on building organizational capacity through training and education. AI is a transformative technology, but its potential can only be realized if staff, board members, and volunteers understand how to use it effectively and responsibly.

Many nonprofits face a steep learning curve with AI. Staff may be unfamiliar with its capabilities, wary of its implications, or concerned about job displacement. Providing comprehensive training can help alleviate these concerns and empower teams to embrace AI as a tool for enhancing their work rather than replacing it.

Training programs should include:

• Basic AI literacy for all staff, covering how AI works and its potential applications.
• Ethical considerations, including how to recognize and mitigate bias.
• Best practices for data privacy and cybersecurity.
• Hands-on workshops to build confidence in using specific AI tools.

Board members also play a critical role in AI adoption. As stewards of the organization’s mission and strategy, they need to understand AI’s implications to provide informed oversight. Training for board members should focus on high-level governance issues, such as aligning AI use with the nonprofit’s values and ensuring compliance with legal and ethical standards.

Finally, nonprofits should foster a culture of continuous learning. AI is an evolving field, and staying informed about new developments, tools, and best practices is essential. By investing in training and education, nonprofits can position themselves to harness AI’s benefits while navigating its challenges.

Conclusion

Artificial intelligence holds transformative potential for nonprofits, offering tools to enhance efficiency, personalize engagement, and amplify impact. However, its adoption must be approached thoughtfully and responsibly. By focusing on data privacy and security, intellectual property, governance, risk management, and training, nonprofits can navigate the complexities of AI while staying true to their missions. As the nonprofit sector continues to embrace innovation, addressing these key areas will ensure that AI serves as a force for good rather than a source of unintended harm.