FINAL WORD risk. This demonstrated that fair lending and accurate risk prediction can coexist with the right techniques.
Transparency
For AI to be trusted, it must be understandable. The complexity of many AI models, especially deep learning systems, can make them feel like black boxes, where even developers cannot fully explain decisions. This lack of transparency decreases trust and raises concerns, particularly in high-stakes areas like healthcare, law enforcement, and finance.
Transparency in AI is more than explaining algorithms; it is about making the decision-making process interpretable to humans. Explainable AI is one way to ensure stakeholders can follow the logic behind decisions.
For instance, in healthcare, AI systems should not only provide a diagnosis but also explain which symptoms led to that conclusion, empowering doctors and patients to trust AI-driven decisions.
In a recent collaboration, a team of experts developed a Lymphoma Data Hub to help researchers use AI for faster early-stage diagnosis and therapeutic innovation. By leveraging computer vision, the project reduced diagnostic times from days to minutes. Data scientists used heatmaps to interpret the model’ s focus, providing experts with clear insights into the decision-making process.
A US study found that Black and Hispanic borrowers were more likely to be denied loans and received less favourable terms even when controlling for credit scores. Even with similar financial profiles, algorithmic models showed racial disparities, due to indirect biases in the data, like zip code or education.
The responsibility of developers and business leaders is to ensure AI systems do not perpetuate or worsen biases. Continuous monitoring and re-evaluation are essential as societal norms evolve. What was once considered fair may no longer be acceptable as fairness grows and changes.
Transparency also involves being upfront about AI limitations. Organisations must communicate clearly about what AI can and cannot do, ensuring stakeholders understand its potential risks and shortcomings.
Data privacy
AI relies on vast amounts of data, much of it personal or sensitive, raising ethical concerns about privacy and security. As AI evolves, so must our commitment to safeguarding individuals ' data.
Protecting data is not just about following privacy regulations; it is about respecting individuals ' autonomy over their personal information. Data privacy should be integrated into AI systems from the start through
Rahul Arya, CEO and Managing Partner, Artefact MENA
AI can also mitigate and resolve the risks it generates. For example, Zest AI, a fintech company, applies fairness constraints in their lending models to reduce disparate impact.
Their models showed a 30 – 40 % increase in approval rates for protected groups without increasing default
AI has the potential to solve complex problems, enhance productivity, and create new opportunities.
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