Bias in AI is all the rage at the minute as the media, regulators and AI Pundits shine the light on generative AI models and algorithms that are serving up distortion in results, ranging from amplified gender and racial stereotypes, to misrepresenting demographics and other metrics in their analyses, recommendations and predictions.
For AI to be truly trusted, bias needs to be driven out of models and algorithms.
Some bias in AI is almost unavoidable as LLMs train on historical data, and algorithms use factors that, if inspected, would show how obvious or even subtle bias is already baked in. Ranging from training data not reflecting underrepresented groups creating racial, age or gender bias to more insidious results from algorithms that have biases deeply embedded in their weighting factors, AI can deliver information, images and videos that can skew our perceptions and decisions in ways that perpetuate the behaviours we believe we’re stamping out with ‘objective’ technology. Making matters worse, some bias is extremely hard to identify if the algorithms are considered Intellectual property by their owners and not willingly offered up for inspection.
For instance, a recent study, AI-generated faces influence gender stereotypes and racial homogenization, revealed bias in Stable Diffusion, a popular text-to-image generative model – resulting in underrepresentation of certain races in searches and analysis.
Do we really need academic investigations and studies to force the hand of organizations that hide bias in their AI models?
The impact of bias not only erodes the trust of stakeholders, consumers and partners that rely on the efficacy and explainability of AI results, but exposes organizations to reputational damage, and legal consequences that can be hefty as regulators start to dole out fines meant to send a message, especially in these early days of AI. Ultimately, bias negatively impacts our society, where deliberately or unintentionally skewed results are served up at machine speed without the benefit of any human in the loop.
Identifying and eliminating bias is squarely the responsibility of AI governance, and in particular Model Governance, which involves a meticulous curation of training data, utilizing robust data governance steps to ensure transparency, privacy protection and fairness. This means evaluating pre-processed data against strict criteria, conducting data clearance document quality checks, and conducting reviews to ensure the highest quality data is used for model training.
Extreme vigilance and deep data modelling expertise are table stakes for identifying and rooting out bias. Like weeding a garden, it’s not a one-and-done thing but requires continuous improvement and monitoring of data against trust metrics to ensure results are fair, transparent and compliant. With organizations facing an AI technology skills crunch, getting the right attention on tools that are built in-house or purchased may not be as easy as you think – some teams will cut corners or simply make mistakes due to lack of skills or experience, exacerbated by the dizzying speed of evolution of the technology itself – it’s hard to keep up.
Fairness and Bias Detection
In addition to adhering with extreme diligence to the Model Governance processes of data clearance and acquisition, preprocessing and tokenization outlined above, we adhere to core trustworthy principles that further protect our products against creeping bias.
4CRisk endeavors to ensure any models/systems are fair and free from harmful bias. We ensure our training data and algorithms avoid discriminatory outcomes and comply with laws on accessibility and inclusiveness.
How?
4CRisk products are designed to ensure zero bias and provide proof of fairness. Our ensemble of language models is pre-trained on a carefully curated corpus of regulatory, risk, and compliance corpora from public domain sources, following a strict data governance and clearance process. Customer data is not used in training our models. Our solutions and AI models can be deployed in a dedicated environment to further enhance privacy and security of business data.
Accuracy, Validity and Data Quality
We know the importance of high-quality, accurate and continuously validated data to train effective and unbiased AI models. At 4CRisk, our models prioritize accuracy and are designed to minimize drift. We focus on ensuring our models, pre-trained on regulatory corpora from authoritative sources, undergo continuous validation to maintain data accuracy and relevance over time. Rigorous quality assurance protocols, data cleaning and preprocessing, and performance monitoring ensure high accuracy, validity, and data quality.
Explainability
We build and deploy AI products that are understandable and explainable, fostering trust in their decisions. 4CRisk products provide meaningful explanations for AI decisions through confidence scores, visual mappings (Sankey diagrams), and references to source documents. Users can export, filter, sort information, and challenge any results.
Human in The Loop
We ensure that Humans in the Loop verify at the appropriate stages of the business process, providing sufficient training as systems evolve. 4CRisk products are designed by experts in risk, compliance, audit, and governance, who understand how to leverage AI to augment human capabilities. Subject matter experts can review and override model predictions, ensuring informed decision-making. 4CRisk products incorporate process steps where professionals can verify, collaborate on, and revise AI-generated results to ensure reliability, accuracy, and build trust.
Social Benefit and Justice
We ensure our AI products are designed to address social challenges and improve human well-being and equity. 4CRisk prioritizes transparency in AI usage, starting with a clear articulation of user needs and public benefits. We offer evaluation tools, ROI models, and thought leadership resources like blogs, e-books, and webinars to educate customers and the broader community about AI.
Organizations and AI product vendors need to commit to an AI governance program that does in fact, ensure any models/systems are fair and free from harmful bias. This means, at a minimum, ensuring training data and algorithms avoid discriminatory outcomes and comply with laws on accessibility, equity and inclusiveness. Regulations, like the EU IA Act, spell out specific consequences for those who cannot comply with sustainable and trustworthy AI principles that minimize bias.
Vendors and AI teams get extra points for openness by sharing AI design concepts, training data, and relevant information, while safeguarding personal information, system integrations, and national security interests. As a general practice, AI governance programs need to be able to show, and in fact prove, fair, explainable and transparent results of ongoing monitoring, evaluation, and improvement of your AI strategy, principles, models and use.
We look forward to more clarity and enforcement of AI Model Governance and the elimination of bias in AI.
About 4CRisk.ai Products: Our AI products use language models specifically trained for risk, compliance and regulatory domains to automate manual, effort-intensive tasks of risk and compliance professionals, providing results in minutes rather than days; up to 50 times faster than manual methods.
Would you like a walkthrough to see what 4Crisk products can do for your organization? Contactus@4crisk.ai or click here to register for a demo
4CRisk products: Regulatory Research, Compliance Map, Regulatory Change and Ask Aria Co-Pilot are revolutionizing how organizations connect regulations with their business requirements.
4CRisk.ai
AVP, Product Head, Product Management
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