As artificial intelligence (AI) becomes increasingly integrated into the operations of nonprofit organizations, the potential for unintentional bias presents a significant challenge. Ensuring that AI systems are fair, transparent, and equitable requires intentional policies and practices. Allison Fine, president of Every.org, underscores the importance of human oversight in AI deployment: “We cannot unleash the bots on the world without human supervision, and we have to always stay deeply human-centered in this work.”

Fine’s caution highlights a critical issue: the inherent bias in AI systems. Often programmed by homogenous teams and tested on historical data sets, these systems can perpetuate and even amplify existing biases, disproportionately affecting marginalized groups. This article explores best practices for nonprofit leaders to mitigate AI bias, ensuring their technology serves all communities equitably.

Understanding AI Bias in Nonprofits

AI bias occurs when the outcomes of an AI system are systematically prejudiced due to biases in the data used for training or in the algorithms themselves. For nonprofits, this can lead to skewed results in areas such as workflow improvement, hiring, and service provision. Fine notes that AI systems are often programmed and tested on data that reflects societal biases, particularly benefiting white men and disadvantaging people of color and women.

The implications of AI bias in nonprofits are profound. For instance, biased hiring algorithms can result in less diverse teams, while biased service provision tools can inadequately address the needs of underrepresented communities. Addressing these biases is crucial for nonprofits committed to equity and justice.

Interrogating Data Sources

A fundamental step in avoiding AI bias is critically examining the data used to train AI systems. Nonprofits must ask tough questions about the assumptions built into these tools and the provenance of the data. IT leaders should not accept AI technology as it comes but should instead interrogate the data skeptically and critically.

For example, it is essential to understand whether the data includes diverse demographics and if users had the option to opt out to protect their privacy. By identifying potential biases in the data upfront, nonprofits can take proactive steps to mitigate them. This interrogation should include:

Demographic Diversity: Ensuring the data reflects the populations served, including various ethnicities, genders, ages, socioeconomic statuses, and geographic locations.

Privacy Considerations: Understanding the ethical implications of data collection and ensuring that individuals’ privacy is respected.

Implementing Gradual AI Integration

Integrating AI gradually through pilot programs can help mitigate the risks associated with biased outcomes. Small-scale implementation allows nonprofits to monitor the technology’s impact closely and make necessary adjustments before full-scale deployment. Allison Fine suggests running “tiny” AI pilots with clean, sorted, and complete data to avoid AI hallucinations and other inaccuracies.

Gradual integration provides several benefits:

• Risk Mitigation: Small-scale implementation reduces the risk of widespread negative impacts.
• Stakeholder Engagement: Gradual changes allow staff, donors, and other stakeholders to adapt to new technologies without significant disruption.
• Learning and Adaptation: IT teams have more time to educate themselves on AI use cases and to refine the technology based on initial feedback.

Training AI on Diverse Data Sets

The quality of AI outputs heavily depends on the diversity of the data sets used for training. Rodger Devine, president of the research organization Apra, emphasizes the importance of considering the amplification of bias: “All AI-powered tools are subject to their training data, and garbage in, garbage out.”

To enhance AI sophistication and fairness, nonprofit IT teams should:

• Use Diverse Training Data: Ensure the data set represents the demographics of the populations served.
• Continuously Update Data: Keep feeding the AI model with updated and relevant data to reflect current realities.
• Monitor Outputs: Regularly study the quality of AI outputs to identify and correct biases.

Working with tech partners such as CDW can provide additional confidence that nonprofits are working with high-quality data and best practices.

Regular Bias Audits

AI systems are dynamic and require ongoing oversight. Regular bias audits are essential to ensure the technology does not perpetuate or introduce new biases over time. These audits, typically conducted by third parties, can assess whether certain groups are unfairly advantaged or disadvantaged by the AI system.

Bias audits should include:

• Algorithm Review: Regularly reviewing the algorithms to identify potential biases.
• Impact Analysis: Assessing the impact of AI outputs on different demographic groups.
• Stakeholder Feedback: Incorporating feedback from diverse stakeholders to understand the real-world implications of AI decisions.

Enhancing Transparency and Trust

Transparency is vital for building trust in AI systems. Nonprofits should be open about the AI use cases they are testing, sharing updates with stakeholders on pilot programs, audits, and training initiatives. Sarah Tedesco, executive vice president of DonorSearch, highlights the benefits of transparency: “Responsible and transparent use of AI will help you foster an engaged base of support that’s confident in your organization’s abilities.”

Transparent practices include:

• Clear Communication: Regularly update stakeholders about AI initiatives and findings.
• Inclusive Participation: Involve diverse community members in discussions about AI implementation and impact.
• Open Data Practices: Share non-sensitive data and methodologies used for AI training to allow external validation and feedback.

Successful AI Implementation in Nonprofits

One nonprofit successfully used AI to enhance service provision by implementing a pilot program that used diverse data sets to train its AI models. The organization focused on ensuring the data reflected the demographics of the communities it served, which included a mix of ethnicities, ages, and socioeconomic backgrounds. Regular bias audits and stakeholder feedback helped fine-tune the AI system, resulting in more equitable service delivery.

Another nonprofit addressed bias in hiring by critically examining the data used to train its AI hiring tool. By incorporating data from diverse applicant pools and continuously monitoring the AI outputs, the organization achieved a more inclusive hiring process. Gradual implementation and stakeholder engagement were key to gaining trust and refining the technology.

A nonprofit used AI to improve donor engagement by analyzing patterns in donor behavior and preferences. The AI system was trained on a diverse data set that included donors from various backgrounds and giving histories. Regular audits and transparent communication with donors about the use of AI helped build trust and enhance donor relationships.

Conclusion

Avoiding unintentional bias in AI is a critical challenge for nonprofits committed to equity and justice. By interrogating data sources, implementing gradual AI integration, training AI on diverse data sets, conducting regular bias audits, and enhancing transparency and trust, nonprofits can harness the power of AI while ensuring it serves all communities fairly.

As AI technology continues to evolve, nonprofits must remain vigilant and proactive in their efforts to prevent bias, ensuring their technology aligns with their mission and values. By adopting these best practices, nonprofits can leverage AI to drive innovation and positive social impact while upholding the principles of fairness and equity.