As AI reshapes the way we work and lead, business decisions are no longer just technical, they’re ethical. In this fourth article in our AI and Business for Leaders series, we explore how to implement AI responsibly and transparently. From biased algorithms to privacy concerns and upcoming regulation, this is a moment to lead with clarity, ethics, and accountability.
The more embedded AI becomes in our systems, the greater the responsibility leaders have to ensure it works for people, not against them. The ethical implications of how it is developed and deployed are coming under increasing scrutiny. For business leaders, it’s no longer just about what AI can do, but how it should be used, and how to ensure it supports rather than undermines trust, fairness and accountability.
One of the most urgent concerns is bias. AI systems learn from historical data, and that data often reflects deep-rooted inequalities. If these patterns go unchecked, algorithms can reinforce discrimination at scale, sometimes without anyone realising.
A 2023 study from Stanford University, for example, found that ChatGPT offered noticeably different legal advice depending on the perceived race or gender of the user. When presented with identical legal scenarios, the chatbot gave more lenient or supportive responses to white male users than to those identifying as Black or Hispanic. It’s a stark reminder that even the most advanced generative AI tools can reflect and perpetuate societal bias when not properly managed.
The Apple Card case provides another telling example. In 2019, Apple and its banking partner Goldman Sachs were investigated after customers reported that women were being given lower credit limits than men with similar financial backgrounds. The lack of transparency in the algorithm’s decision-making process made it difficult to determine how or why these outcomes occurred, prompting criticism and regulatory scrutiny. This incident showed how opaque systems can create real-world harm and reputational fallout when fairness and accountability are not baked in from the start.
AI systems are increasingly trained on large datasets that include sensitive personal information – everything from biometric identifiers to behavioural data. This raises serious questions about consent, data minimisation, and the right to be forgotten.
While compliance with regulations like the UK GDPR and the forthcoming EU AI Act is essential, it’s just the baseline. Organisations also need a clear ethical stance on how data is collected, used and protected. Customers and employees alike are becoming more data-aware and will expect transparency not just in policies, but in practice. Leaders must ensure their AI initiatives are backed by robust data governance frameworks and a culture that prioritises privacy as a core principle, not an afterthought.
Many AI systems operate as “black boxes,” where inputs go in and outputs come out, but how a decision is made remains unclear. This lack of explainability is a problem when the stakes are high, whether you’re denying someone a loan, prioritising patients for treatment, or screening job candidates.
Without clear reasoning, it becomes difficult for businesses to justify decisions, correct mistakes or maintain public trust. Transparency isn’t about revealing proprietary code; it’s about enabling meaningful understanding. That might include using explainable AI (XAI) techniques, documenting model logic, or clearly communicating the role AI plays in decision-making.
As regulators increase their focus on algorithmic accountability, businesses that fail to explain how their systems work risk not only non-compliance but also a tarnished reputation.
1. Create clear ethical AI policies
Set guidelines for fairness, bias mitigation, explainability, and data usage. Be clear on who is accountable and how decisions will be reviewed.
2. Support reskilling and career development
Upskilling is an investment in long-term capability. Empower people to use AI confidently in their roles and develop new specialisms as the landscape evolves.
3. Make inclusivity part of your AI strategy
Bring employees into the conversation. Encourage feedback. Pilot tools with cross-functional teams. The more voices at the table, the better your outcomes will be.
4. Stay ahead of regulation
The EU AI Act is just the beginning. Global regulators are setting out clearer expectations for the ethical use of AI. Be proactive in reviewing and updating your governance processes to reflect this evolving landscape.
5. Continuously review AI’s impact
Don’t set and forget. Monitor how AI is affecting your workforce, customers and business outcomes. Be ready to adapt when needed.
Leaders today don’t just have a responsibility to embrace innovation. They have a responsibility to do it well, with foresight, fairness, and integrity.
At Decoded, we believe in a future where AI enhances people, strengthens organisations, and drives meaningful innovation.
We work with forward-thinking businesses across sectors to deliver bespoke AI training that equips leaders and teams with the skills to harness AI responsibly and effectively. From financial services to government, our programmes help organisations build confidence, capability, and a culture of ethical innovation.
The future of AI won’t just be built on code, it will be shaped by the values that leaders define today.
Click here to explore our AI for Leaders Immersions.
Sources:
Harvard Business School
Financial Times
IMD
NTT Global Services
Sogeti Labs
McKinsey
Image accreditation: Getty Images for Unplash.com+. Last accessed on 14th April 2025.
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