The AI landscape is evolving at an exponential rate. While this is an exciting fact and represents the unlocking of countless possibilities, it also comes with inherent risks and responsibilities.

Considering the power that accompanies the technology, not only do we need to be diligent in our building of AI systems, but we also need to be aware that new risks are always on the horizon, and what constitutes diligence today may not meet the mark tomorrow.

Hence it is important to not just have a toolkit of techniques, which are useful at a particular point in time, but rather a set of principles to guide us through the dynamic environment as it shifts. Guided by these principles, we can derive techniques on an ongoing basis.

Here are our recommendations for key principles that your organization can adopt for a risk-free AI infrastructure and ethical AI deliveries.

 

Transparency and explainability: Empowering stakeholders with trust and openness

Transparency and explainability are foundational to building trust in AI systems. We emphasize the importance of clear, accessible documentation that details the AI system’s capabilities, limitations, and decision-making processes. This includes outlining how data is processed, the logic behind the algorithms, and any potential biases that could affect outcomes. By making this information readily available, we empower developers and users to understand how an AI system reaches conclusions.

Transparency isn’t just about sharing data; it’s about fostering an environment where meaningful answers can be provided, ultimately leading to more trustworthy and reliable AI system.

This also includes integrating model interpretability tools such as Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) into AI solutions. Such tools promote transparency and allow stakeholders to understand exactly what variables are influencing various predictions, allowing decisions to be made on ethical suitability of their inclusion.

 

Fairness and bias mitigation: Ensuring all is fair in the AI ecosystem

Bias in AI can lead to unfair outcomes that disproportionately affect certain groups. This is why it is the responsibility of all stakeholders involved in the AI lifecycle, from development and testing to deployment, to play their part in recognizing and addressing biases.

Ways to go about this include using diverse datasets, implementing robust testing protocols, and continuously monitoring AI systems in real-world scenarios to ensure fairness. This exercise is one that requires a lot of attention, as bias can present itself in subtle and unexpected ways. For instance, the inclusion of a person’s postcode as a variable in a model used for credit scoring might seem innocent and may in fact be effective. However, it could be the case that such a model may be using the postcode as a proxy for a person’s ethnicity, especially in regions with strong patterns of ethnicity in housing? This is an obvious ethical issue. Awareness of such nuances, combined with the technical expertise to recognize and address them, ensures fairness in AI systems.

 

Own it with accountability and responsibility

All stakeholders involved in AI development, from data scientists to executives, must be accountable for the impact of their creations. This has been captured by UK institutions such as the House of Lords in their paper, AI in the UK: ready, willing and able?.

Ensuring AI models perform as intended is a critical task and responsibility needs to be taken for any unintended consequences. This includes advocating for clear accountability, where roles and responsibilities are defined at every stage of the AI lifecycle. Regular reviews and ethical assessments can keep teams aligned with societal values.

 

Defend and protect with privacy and security

Protecting privacy and ensuring robust security are paramount in an era where data is more valuable than ever. This includes encryption, secure data storage, and regular security audits to identify vulnerabilities.

Incorporating privacy-preserving techniques, such as differential privacy, federated learning and data anonymization ensure AI systems handle personal information responsibly.

Ultimately, the goal is to create AI solutions that are secure and respectful of user privacy. Security and governance of IT products are clearly of paramount concern, as demonstrated by the recent challenges the Post Office has had with their IT systems.

 

 

Citizen-centered designs at the heart of AI

Citizen-centered designs should be at the core of all AI initiatives. This approach ensures AI technologies are developed with a human-in-the-loop focus, prioritizing the needs and values of the individuals who will use them.

Active engagement with end-users throughout the development process, gathering feedback and making iterative improvements can enhance usability and accessibility. By putting people at the center of the design process, organizations can create AI solutions that are intuitive, user-friendly, and aligned with societal goals. The aim? To develop AI that not only solves problems but also enriches lives.

 

 

AI’s role in creating a sustainable environment

Sustainability is a growing concern in AI development, and that is why it is imperative to address the environmental and social impacts of our technologies.

AI systems, particularly those requiring significant computational resources, can have substantial carbon footprints. Using energy-efficient infrastructure and exploring renewable energy options can help with minimizing this footprint. It is also important to support hosting and training of AI to take place in locations that use a higher proportion of renewables in their energy mix. Additionally, it’s important to consider the broader societal implications of AI, such as its effects on employment and the economy.

 

 

Eviden – a world leader in expanding possibilities with AI

Global industry leaders, like Rabobank in the Netherlands, are partnering with Eviden to unlock the full value of AI in line with the above-mentioned principles for a responsible AI system. For its part, Eviden is committed to building responsible AI models that are not only great examples of innovation but also forces for positive social change.

 

  • Gear up to learn more about Eviden’s Responsible AI offering and how it is can accelerate your business.
  • Let’s discuss how businesses are already leveraging responsible AI systems for tangible business value.