How SaaS Companies Can Reduce AI Model Bias

Forbes

16-06-2025

As businesses realize the high value of artificial intelligence in improving operations, understanding customers, setting and meeting strategic goals, and more, embedding AI into their products is moving from a 'nice to have' feature to a competitive necessity for software as a service companies. However, it's essential to tread carefully; SaaS companies must be aware of the risk that both implicit and explicit bias can be introduced into their products and services through AI.
Below, members of Forbes Business Council share strategies to help better detect and minimize bias in AI tools. Read on to learn how SaaS companies can ensure fairness and inclusivity within their products and services—and protect their customers and brand reputation.
To build AI tools that people trust, businesses must embed ethical AI principles into the core of product development. That starts with taking responsibility for training data. Many AI products rely on open, Web-scraped content, which may contain inaccurate, unverified or biased information. Companies can reduce exposure to this risk by using closed, curated content stored in vector databases. - Peter Beven, iEC Professional Pty Ltd
It is impossible to make AI unbiased, as humans are biased in the way we feed it with data. AI only sees patterns in our choices, whether they are commonly frowned upon patterns, like race and location, or not-so-obvious patterns, like request time and habits. Like humans, different AI models may come to different conclusions depending on their training. SaaS companies should test AI models with their preferred datasets. - Ozan Bilgen, Base64.ai
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You can't spot bias if your test users all look and think the same. Diverse testers help catch real harms, but trying to scrub every point of view just creates new blind spots. GenAI's power is in producing unexpected insights, not sanitized outputs. Inclusivity comes from broadening inputs, not narrowing outcomes. - Jeff Berkowitz, Delve
Evaluations are key. SaaS businesses cannot afford expensive teams to validate every change when change is happening at a breakneck speed. Just like QA in software engineering has become key, every business must implement publicly available evaluations to validate bias. This is the most thorough and cost-effective solution out there. - Shivam Shorewala, Rimble
Using third-party AI tools for independent audits is key to spotting and correcting bias. This approach helps SaaS companies stay competitive and maintain strong client trust by ensuring fairness, transparency and accountability in their AI-driven services. - Roman Gagloev, PROPAMPAI, INC.
SaaS companies need to extend prelaunch audits with real-time bias monitoring to monitor live interactions. For example, one fintech customer reduced approval gaps by 40% by allowing users to flag biases within the app, dynamically retraining models. Ethical AI requires continuous learning and fairness built up through user collaboration, not solely code. - Adnan Ghaffar, CodeAutomation.AI LLC
SaaS companies can reduce bias by diversifying their training data and using interdisciplinary teams when developing an AI model. They should also implement routine audits to verify that algorithms are fair and transparent, ensuring their AI is inclusive and equitable. This is essential to mitigate alienating customers and damaging brand equity, as biased AI systems lead to inequity. - Maneesh Sharma, LambdaTest
Bias starts with who's at the table. If your team doesn't reflect the people you're building for, neither will your model. Audit your data before you code. Fairness isn't a feature you add later, but one that should be baked into the build. If you get that wrong, the harm done is on you. Inclusivity is a strategy, not charity. If your strategy's biased, so is your bottom line. - Aleesha Webb, Pioneer Bank
We embed fairness audits at each stage of model development—data curation, training and output testing—using diverse datasets and human-in-the-loop validation. For SaaS, where scale meets intimacy, unchecked bias can harm thousands invisibly. Building trust starts with building responsibly. - Manoj Balraj, Experion Technologies
In the age of social media, the best way to minimize bias is to let the users tell you about it. Collecting user-generated opinions through testing, MVPs and feedback forms is the best way to ensure your product is free from developer or even marketer biases. Just make sure you have a good number of users to test your AI product. - Zaheer Dodhia, LogoDesign.net
One powerful way SaaS companies can tackle bias in AI models is by rigorously testing them against open-source and indigenous datasets curated specifically to spotlight underrepresented groups. These datasets act like a mirror, reflecting how inclusive or exclusive your AI really is. By stepping outside the echo chamber of standard data, companies gain a reality check. - Khurram Akhtar, Programmers Force
Most teams focus on fixing bias at the data level, but the real signs often surface through day-to-day product use. I tell SaaS companies to loop in support and success teams early. They're closest to the users and usually flag issues first. Their feedback should feed directly into model reviews to catch blind spots that don't show up in training data. - Zain Jaffer, Zain Ventures
SaaS companies should simulate edge-case users, including small sellers, niche markets, nonnative speakers and more, to test how AI performs for them. Real inclusivity means optimizing for the exceptions, not just the averages. If your product works for those on the edges, it'll work for everyone. - Lior Pozin, AutoDS
Integrate diverse voices at every stage, from design and data to deployment. Uncovering bias begins with owning our blind spots, so use honesty as a guide. Inclusive AI isn't just ethical—it's also essential for relevance, reach and trust in today's diverse world. - Paige Williams, AudPop
SaaS companies should establish a continuous feedback loop with external experts, such as ethicists and sociologists, to review AI model outcomes. These experts can identify unintended consequences that technical teams might miss, ensuring the AI model serves all communities fairly. This proactive approach helps avoid costly mistakes, improves user satisfaction and strengthens long-term brand credibility. - Michael Shribman, APS Global Partners Inc.
Treat bias like a security bug by documenting it, learning from it and making spotting it everyone's job rather than just the AI team's responsibility. Build bias reports into internal processes and reward early detection. Building operational systems around bias detection keeps products fair, inclusive and trusted. - Ahva Sadeghi, Symba
What finally shifted things for us was bringing real users from underserved communities into our QA process. We stopped pretending to know what fairness looks like for everyone. It turns out, when you ask the people most likely to be excluded, they'll tell you exactly how to fix it. - Ran Ronen, Equally AI
One way SaaS companies can detect and minimize bias in their AI models is by conducting equity-focused impact assessments. These assessments can evaluate whether the model produces better, worse or neutral outcomes for each user group. This is important, because equity ensures that users from different backgrounds receive fair and appropriate outcomes, promoting true inclusivity and preventing systemic disadvantage. - Ahsan Khaliq, Saad Ahsan - Residency and Citizenship
One way SaaS companies can better detect and minimize bias in their AI models is by actively inputting their own unique ideas and diverse perspectives into the system. In this way, the AI can be guided to develop solutions that reflect true inclusivity, ensuring that the outcomes are fair and representative of a wide range of users. - Jekaterina Beljankova, WALLACE s.r.o
SaaS companies must shift from a 'software as a service' mindset to a 'service as software' mindset to recognize AI as a dynamic, evolving system. This mindset encourages continuous bias audits, inclusive datasets and real-world feedback loops, which are essential for fairness, trust and long-term relevance in diverse markets. - Kushal Chordia, VaaS - Visibility as a Service