An AI system wrongly denies a home loan, not due to bad credit, but because the training data was historically biased. In banking, this isn’t hypothetical. It’s happening.
As AI transitions from back-office automation to customer-facing decisions, its biases also accompany it. Tools like ChatGPT and generative AI have compelled banks to confront ethics, fairness, and accountability in ways they have never done before.
A recent City AM article highlights a critical shift in the world of finance: banks have long used AI for decades, but the rise of tools like ChatGPT now brings issues of built-in bias to the forefront. What once felt theoretical is now practical: AI systems that affect lending decisions, customer service, and risk assessments can unintentionally perpetuate discrimination. As financial institutions unleash generative AI, they’re forced to confront ethical and operational blind spots.
This moment is more than just a tech dilemma; it’s a talent challenge. Developing responsible AI requires more than algorithms; it demands skilled people equipped to understand bias, design robust systems, and evaluate outputs critically. That’s where mthree’s talent programmes provide real value, building a pipeline of tech professionals who don’t just run AI tools, but govern them with care.
> Why AI bias is more than just a technical glitch
Industry experts and regulators alike have sounded alarms about algorithmic bias. Bias can emerge from many sources, from misrepresentative training data to the models themselves. For banking, that means potential harm in:
- Loan approval: Certain groups may be unfairly downgraded due to biased data
- Risk assessment: Mistaken correlations used in policing or compliance can affect outcomes disproportionately
- Customer interactions: Generative AI chatbots may reinforce stereotypes
This is far from a one-off. As major banks explore using AI tools to generate content and support decision-making, including ones they've built in-house, the risk of bias is very real. Financial services need more than just technical capability; they also need ethical oversight, a workforce that understands fairness in AI, and can implement guardrails at every stage.
> Talent: the overlooked piece in ethical AI
As the article suggests, banks have used AI for decades, but ChatGPT and generative AI have accelerated awareness and scrutiny of the biases that using AI brings. The next wave of AI demands more from teams. It’s no longer enough to have developers who can write code, they must also:
- Recognise when models should be audited
- Apply fairness metrics and counterfactual testing
- Integrate responsible AI principles in workflows
- Collaborate across compliance, legal, and data governance teams
Yet, while demand for this skillset skyrockets, supply remains limited. Senior hires with this expertise are scarce and expensive. Many entry-level technical professionals lack necessary exposure. Traditional hiring models simply aren't bridging the gap, but there's a solution.
> How mthree builds talent for responsible AI futures
At mthree, we've built a solution around the core problem: talent. Through our Hire Train Deploy model, we identify promising junior talent and equip them to succeed in complex tech environments, including those adopting AI responsibly. We also work with organisations to identify existing team members who can be reskilled or upskilled, ensuring the current workforce evolves alongside emerging technologies.
Here’s how we bridge the gap:
- Custom Reskilling on Real Technologies
Candidates receive training tailored to your environment, whether that’s implementing bias-checking tools, integrating explainable AI frameworks, or managing AI lifecycle governance.
- Embedding Ethics and Governance in the Foundation
Our curriculum goes beyond syntax. We instil critical thinking, model accountability practices, fairness testing, and ethical design, so junior talent can make responsible decisions, not just technical ones.
- Hands-On Projects in Live Environments
Trainees work on real or simulated tasks mirroring production environments. They learn to monitor outputs, detect anomalies, and collaborate with compliance teams from day one.
- Building Diverse, Cross-Functional Teams
Bias in AI often stems from narrow design perspectives. Our training attracts diverse talent from different backgrounds and prepares them for collaborative roles with legal, HR, and tech teams.
- Developing Mid-Level Talent for Leadership
Our model ensures continuous development. As candidates grow, they become mid-level experts able to guide bias audits, coach new hires, and contribute to governance decision-making.
> Why this matters now
Banks can’t afford to get AI wrong. Regulatory pressure is rising. Customers are paying attention. And every biased decision made by a machine still points back to a human, someone who did (or didn’t) catch it. For emerging compliance frameworks and heightened customer expectations, oversight is no longer optional. But without the right talent in place, the risks associated with AI, legal, operational, and reputational, only grow.
This is where mthree adds real value:
- We reduce risk by embedding ethical accountability into junior training
- We accelerate time-to-impact by deploying candidates who are both technically and culturally informed
- We amplify ROI by growing mentors and mid-level leaders from within, instead of relying on expensive external hires
> What responsible AI teams look like
- Technical Aptitude – Candidates master the tools and platforms your organisation uses
- Governance Awareness – They understand bias sources, regulatory compliance needs, and transparency frameworks
- Cross-Domain Workflow – They can collaborate between development, risk, and legal teams
- Continuous Improvement Mindset – They’re trained to audit, monitor, learn, and iterate, just as AI models require continuous improvement.
These are exactly the competencies we focus on in mthree’s Hire Train Deploy model for AI roles.
> A new blueprint for building ethical tech teams
As the City AM article underlines, the coming era of AI isn’t just about capability, it’s about responsibility. Banks and other industries must evolve how they build talent, establish governance, and deploy AI tools.
Rather than relying solely on senior hires or external consultants, savvy employers are choosing to grow their own and mthree helps make it happen.
In Summary
- AI bias is no longer theoretical; its effects are real and potentially damaging
- Responsible AI requires more than algorithms; it needs people trained in ethical oversight and governance
- mthree’s Hire Train Deploy model develops junior talent who understand both tech stacks and AI fairness principles from the start
- This builds resilient, ethical, and future-ready teams, minimising bias and boosting compliance, innovation, and trust
Strengthen your AI capabilities with the right people, right now.
Partner with mthree to build tech teams that don’t just deploy AI, they govern it responsibly. Let’s create a future where AI is powerful and fair.
