Data Governance and AI Model Bias, Part 1
February 21, 2024
By Ronald Zurawski, Prolifics Data Governance Strategist and Solution Architect
Reading data governance blogs related to AI, I see a lot of the same thing presented in many variations. I went to ChatGPT and asked it to write a blog for me on AI bias. You can read that ChatGPT generated blog below, italicized, then I have some thoughts on this subject.
ChatGPT generated:
Introduction:
In the realm of artificial intelligence (AI), the ethical implications of bias in models have gained prominence, demanding meticulous attention from organizations and data governance professionals alike. As data governance consultants, it is imperative to guide businesses on how to handle bias in AI models responsibly. This blog explores the pivotal role of data governance in identifying, mitigating, and preventing bias to ensure AI systems deliver fair and unbiased outcomes.
Understanding Bias in AI Models:
Bias in AI models can arise from various sources, including biased training data, algorithmic design, or even the context in which the AI system is deployed. Recognizing that bias is an inherent challenge, data governance plays a crucial role in establishing frameworks to scrutinize the entire AI lifecycle, from data collection to model deployment. A comprehensive data governance strategy should emphasize transparency, accountability, and ethical considerations, setting the foundation for fair AI practices.
Mitigating Bias through Robust Data Governance:
Data governance should prioritize proactive measures to mitigate bias in AI models. This involves implementing robust data quality controls, ensuring diverse and representative training datasets, and fostering collaboration between data scientists and domain experts to understand the potential implications of biased outcomes. Additionally, data governance frameworks must incorporate mechanisms for ongoing monitoring and evaluation, enabling organizations to detect and rectify bias as models evolve and adapt.
Implementing Ethical AI Principles:
Data governance consultants should advocate for the integration of ethical AI principles into the fabric of organizational data practices. This includes establishing clear guidelines on responsible AI use, promoting diversity in data teams, and developing comprehensive documentation to enhance transparency. By aligning data governance with ethical AI principles, organizations can not only address bias but also build trust among stakeholders and demonstrate a commitment to fairness and inclusivity.
Conclusion:
In the ever-evolving landscape of AI, data governance emerges as the linchpin for addressing bias and ensuring the responsible deployment of AI models. By fostering a culture of transparency, accountability, and ethical considerations, data governance consultants empower organizations to navigate the complexities of bias in AI, fostering a future where AI systems are not just powerful but also fair, unbiased, and aligned with the principles of responsible data governance.
Does this sound familiar? Could it be possible you read something similar before? It is a good article. Let’s look at some of the details:
“…data governance plays a crucial role in establishing frameworks…” Great! How? Let’s skip the generic consultant answer of “it depends” and think about it. If we, as data governance professionals, need to put a basic structure in place, what would that look like? What do the regulatory requirements say today? Can we reasonably expect to be included in the development process early enough to help capture, document, and possibly guide those items that need to be available when the auditor comes around? What might those items be? Let’s read the regulations coming out and see if we can provide value while walking that thin line of regulation interpretation and reduce costs/provide value to our enterprise.
“Bias in AI models can arise from various sources…” Does bias come from various sources or does bias come from our interpretation of the world around us, which we apply to this computer program’s output results? Let’s remember the AI model responds to the training data set. From a data quality point of view, we can certainly add some value to the training set considering bias. We can treat each type of training data set like any other group of critical data elements (CDEs) and start by working with the subject matter experts (SMEs) to review the profiles of the data.
“Data governance should prioritize proactive measures to mitigate bias in AI models.” Hmm, how might that work? This may be a new area to explore for data governance specialists. In the past we have always deferred to the SME to evaluate the data profiling results and followed their guidance. Is it now time for us to take a more active role and provide some push back? First, we would need to define and document the standards for what is and is not acceptable when evaluating bias. Then, apply that standard to the data set being evaluated.
I want to talk more about this topic, but, let’s think about these items first. – Ron ron.zurawski@prolifics.com
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Ronald Zurawski
Ron is Data Governance Strategist and Solution Architect at Prolifics. His experience includes more than 10 years of working policy-driven data governance and more than 20 years in enterprise database and data warehousing systems. His industry expertise includes finance, health care and consumer product goods. Ron’s expertise is in strategic planning, systems architecture, program and project management. His tactical experience includes analytics development, architecture, ETL and database administration. Ron has experience in both Big 4 and boutique professional services organizations. Ron holds an MBA from the University of California and an MSCS from the University of
Colorado.