Paul Parker

Paul Parker

Director of Consulting Services, Advanced Analytics Centre of Excellence

Can we trust AI? 

We’re beginning to see it everywhere. Netflix and Amazon recommend us products and entertainment daily. Spotify's AI DJ is now responsible for curating our favourite tunes. We don’t even blink as every day we rely on facial recognition as the primary security measure for safeguarding all the data on our phones, including photos and online banking information.

As AI integrates into our working lives, as it inevitably will, shouldn’t we be sceptical of the responsibility we will hold for the decisions our models make? This blog examines how the growing interpretability of AI can address these concerns and shed a moral light on the next wave of AI development.

Artificial Intelligence has become an integral part of modern business and policymaking, promising efficiency gains, cost savings, and improved decision-making. However, a significant obstacle to fully embracing AI is the lack of transparency in AI models.

If stakeholders cannot understand how AI models arrive at their decisions, it becomes challenging to have faith in their accuracy, fairness, and reliability. Without trust, stakeholders may be unwilling to incorporate AI solutions into their operations or policies, which could lead to missed opportunities for efficiency gains, cost savings, and improved decision-making.

Researchers are actively combatting this issue by developing state-of-the-art explainability methods. Below we have grouped the methods into three more specific areas:

  1. Local explanations: for instance, focus on explaining individual AI predictions, helping stakeholders understand the reasoning behind a single outcome even in complex models
  2. Visual explanations: represent the inner workings of AI models through graphical or visual elements, making complex models more accessible and interpretable. These visualisations, such as heatmaps and scatter plots, allow stakeholders to gain insights into model behaviour quickly
  3. Feature importance: It involves evaluating the relative contribution of each input feature in influencing the AI model's decisions. This transparency helps build trust, identify biases, and make informed decisions about the model's application and potential improvements.

For a more technical explanation of such methods, read more

At the core of ethical AI lies the principle of fairness. AI systems must be designed to treat everyone fairly and avoid biases or discrimination. Fairness is not just an abstract ideal; it is essential to build trust among stakeholders and ensure a positive impact on society.

To promote fairness, organisations must proactively identify and address biases arising from various sources, such as biased training data and hidden structural biases in algorithms. Implementing dataset fairness, design fairness, outcome fairness, and implementation fairness can create more just and equitable AI models.

In the realm of ethical AI, respecting user privacy is non-negotiable. AI systems must strictly adhere to guidelines for data collection, usage, and storage, with explicit user consent being the foundation. Prioritising privacy not only complies with legal requirements but also fosters trust between users and businesses.

Above all, safety takes precedence in ethical AI endeavours. AI systems must undergo rigorous testing and verification to minimise risks to human safety and security. Fail-safe mechanisms and continuous monitoring of AI performance help prevent unintended consequences and reinforce public confidence in AI technologies.

Explainable AI (XAI) plays a crucial role in combating bias in AI models. By providing insights into AI decisions, XAI empowers stakeholders to identify and understand potential biases, enabling businesses to take corrective measures to ensure fairness and eliminate biases from their AI models.

In conclusion, building trustworthy AI with explainable decisions is paramount for a fair and equitable future. Stakeholders, policy makers, researchers, and businesses must collaborate to foster transparency, accountability, and fairness in the ever-evolving world of AI. By doing so, we can unlock AI's true potential and create a positive impact on society.

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About this author

Paul Parker

Paul Parker

Director of Consulting Services, Advanced Analytics Centre of Excellence

Paul leads CGI’s UK and Australia Advanced Analytics Centre of Excellence. With a career spanning over 20 years Paul specialises in advanced analytics, artificial intelligence (AI) and machine learning (ML), data engineering and architecture, and has helped guide many well-known global clients to help reimagine ...