Summary
What are the main risks associated with the use of AI? And how can these risks be managed, so that the full potential of artificial intelligence systems can still be exploited?
Artificial intelligence is revolutionising every sector of activity, offering unprecedented development opportunities for organisations. But like any technological revolution or new scientific advance, its use is not without risks… Only 35% of consumers have confidence in the way companies use AI. Risk management is now one of the major challenges facing the successful deployment of this new technology.
What are the main risks associated with the use of AI? And how can these risks be managed, so that the full potential of artificial intelligence systems can still be exploited? Let’s deep dive.
What are the dangers and risks of AI?
While offering businesses major opportunities in terms of innovation, efficiency and performance, the deployment of AI systems also confronts them with new challenges and risks:
- confidentiality and security risks: AI systems operate on the basis of learning models (machine learning and deep learning), which are fed by data. And the use of this information can present certain risks in terms of confidentiality and security! A faulty algorithm could, for example, extract and use personal data, or disseminate certain sensitive data…
- Ethical risks: facial recognition and mass surveillance, decision-making by autonomous AI systems, algorithmic bias and copyright, etc. The use of artificial intelligence obviously raises a number of ethical and moral questions. If the data on which the algorithms are based contains prejudices or stereotypes, they may, for example, deliver unfair results or give rise to discriminatory decisions;
- operational and economical risks: poor management or over-reliance on AI can leave certain processes vulnerable to breakdowns and malicious attacks. The reliability of AI systems is also crucial: if models are designed or fed with incorrect or incomplete data, so are the results delivered by AI systems! And this failure can lead to a waterfall effect, with major disruptions in certain fields (health or finance, for example).
If they are not anticipated and managed, these risks can obviously have serious legal and financial repercussions. And they can also seriously damage a company’s reputation. Fortunately, by complying with certain rules and implementing appropriate solutions, these risks can be prevented or avoided.
The EU AI Act, the first regulatory framework for risk management
The AI Act, the first legislation in the world to regulate artificial intelligence as a whole, has just been approved and will soon come into force. This innovative legislation classifies AI systems into several categories, according to their potential risks and level of impact. Depending on the level of risk, the AI system is then subject to more or less strict and restrictive rules:
- AI systems with an unacceptable risk are prohibited (all systems that could threaten people’s safety, livelihoods and rights);
- high-risk AI systems are subject to specific legal requirements (all AI systems used in sensitive areas, such as education, employment or law enforcement): in particular, their use must be accompanied by the implementation of a risk management system and human supervision;
- limited-risk AI systems benefit from a more flexible regulatory framework: they are mainly subject to transparency and information obligations.
With this new regulatory framework, the European Union is creating a first level of protection against the risks of AI. And some of these rules will be implemented very soon. AI systems with an unacceptable risk, for example, will be prohibited 6 months after the law comes into force. As well as defining a global strategy, they need to put in place genuine governance and risk management solutions within their organisation.
How can AI risks be managed within companies?
There are a number of solutions that can help businesses anticipate this regulatory transformation and adopt AI that is trusted, secure, responsible and ethical. Today, only 39% of companies have started to implement risk reduction tools and methods.
Implementing genuine AI governance
As an overall management and monitoring framework, AI governance aims to improve the quality and security of the systems deployed. As well as optimising business performance and ensuring regulatory compliance, it also enables risk management to be steered.
Implementing AI governance within a company requires a proactive, multidisciplinary approach. It involves several stages:
- defining the strategy and the main principles: transparency and security, compliance, responsibility and ethics, etc. To ensure the secure and risk-free deployment of its AI systems, the organisation must first define the framework and the main rules of its governance;
- training teams and implementing methodologies: AI management and responsibility must be entrusted to one (or more) dedicated team. All AIS users must be trained and made aware of the risks of AI. And they must understand the rules and processes to be applied;
- using the right tools: the company must equip itself with an effective governance tool. As well as simplifying the management of its AI systems and optimising its performance, setting up an AIMS will enable it to identify and deal with the risks associated with artificial intelligence.
Setting up an AIMS®, to monitor and manage AI risks
As an artificial intelligence management system, an AIMS® is a centralised tool that integrates several functions. As well as making AI systems more reliable and optimising their management, it can support the company in implementing its risk management strategy. AI systems inventory, identification and qualification of risk levels, definition of processes and implementation of governance, training of teams and AI Vigilance… In addition to regulatory compliance, an AIMS® can identify and deal with the risks associated with artificial intelligence.
The first AIMS® on the European market, Naaia is a SaaS solution for AI Governance and risk management. It includes 4 functional modules: Naaia Repository (inventory of AI systems), Naaia Assess (qualification of AI systems), Naaia Core (compliance action plans) and Naaia Event Tracker (risk management). Multi-regulation and multi-framework, this precursory system already incorporates the final version of the AI Act. It enables organisations to respond to the triple imperative of performance, compliance and trust, for the deployment of safe and responsible AI.