Manage AI Risks Credit:co-offiz.com
Introduction
In an article published in May, 2024, Turing Award recipient Yoshua Bengio and Nobel Laureate Geoffry Hinton make the point that Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. In their view, improvements in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social damage, malicious uses, and an irreversible loss of human control over autonomous AI-systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how to manage them. Society’s response, despite promising first steps, is not in sync with the possibility of rapid, transformative progress that is anticipated by many experts. AI safety research is lagging, and present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address the potential of autonomous systems.
Benefits and Risks of AI-Systems
The benefits can be summarized as follows: 1) Improved efficiency: AI algorithms can analyse large amounts of data and make predictions or recommendations faster than humans. AI can help businesses make decisions more quickly and efficiently. 2) Improved accuracy: AI algorithms can be trained on vast amounts of data and identify patterns and relationships to make more accurate predictions or recommendations. 3) Increased competitiveness: By using AI to support decision-making, businesses can gain a competitive advantage over their rivals and better serve their customers while reducing time and employee capital investments.
Some of the risks include Bias: AI algorithms can be trained on partial data, leading to discriminatory or unfair outcomes. In mitigating the risk of bias by AI, businesses must ensure that the data used to train AI algorithms is diverse and representative. Black-box decision-making: AI algorithms can be difficult to understand and interpret, making it challenging to know how decisions are made and identify potential issues. Over-reliance: There is a risk of over-reliance on AI algorithms, leading to complacency and reduced human oversight and judgment. Cost: Implementing and maintaining AI can be expensive and requires specialized expertise, which can be difficult for some businesses to acquire.
Problems of Implementing AI-Systems
1.Bias: Humans are innately biased, and the AI we develop can reflect our biases. These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Those learned biases might be perpetuated during the deployment of AI, resulting in skewed outcomes. AI bias can have unintended consequences with potentially harmful outcomes. Examples include applicant tracking systems discriminating against gender, healthcare diagnostics systems returning lower accuracy results for historically underserved populations, and predictive policing tools disproportionately targeting systemically marginalized communities, among others.
- Cybersecurity threats: AI can be used maliciously to launch cyberattacks. AI tools can be used to clone voices, generate fake identities and create convincing phishing emails—all with the intent to scam, hack, steal a person’s identity or compromise their privacy and security. And while organizations are taking advantage of technological advancements such as generative AI, only 24% of gen AI initiatives are secure. This lack of security threatens to expose data and AI models to breaches, the global average cost of which was a whopping USD 4.88 million in 2024.
- Intellectual property infringement: Generative AI has become a deft mimic of creatives, generating images that capture an artist’s form, music that echoes a singer’s voice or essays and poems akin to a writer’s style. Yet, a major question arises: Who owns the copyright to AI-generated content, whether fully generated by AI or created with its assistance? Intellectual property (IP) issues involving AI-generated works are still developing, and the ambiguity surrounding ownership presents challenges for businesses.
- Lack of explainability and transparency: AI algorithms and models are often perceived as black boxes whose internal mechanisms and decision-making processes are a mystery, even to AI researchers who work closely with the technology. The complexity of AI systems poses challenges when it comes to understanding why they came to a certain conclusion and interpreting how they arrived at a particular prediction. This opaqueness and incomprehensibility erode trust and obscure the potential dangers of AI, making it difficult to take proactive measures against them.
- Misinformation and manipulation: As with cyberattacks, malicious actors exploit AI technologies to spread misinformation and disinformation, influencing and manipulating people’s decisions and actions. For example, AI-generated robocalls imitating President Joe Biden’s voice were made to discourage multiple American voters from going to the polls.
Ethical concerns of AI decision-making
According to an article published by RSM Global, at the heart of AI ethics lies a fundamental and unfortunate paradox: the more powerful and complex AI systems become, the less transparent their decision-making processes can be to human oversight. This is the fundamental concept behind what is known as the ‘black box’ problem. This issue is particularly prevalent with deep learning AI models which utilise complex neural networks where data is processed through many layers of interconnected nodes with inputs being assigned ‘tokens’ that arrange data hierarchically. Continuous monitoring and human oversight have become crucial factors in keeping AI in check. The key to more effective, risk-averse, and beneficial AI decision-making is to ensure that human-in-the-loop processes are in place. These frameworks maintain human judgment at critical decision points while leveraging AI’s processing capabilities. Businesses stand to benefit immensely from what AI can offer, but mitigating risk requires the human touch. A robust AI governance framework can also supplement human oversight for further safeguarding. At a governance level, a systematic approach that integrates structured accountability mechanisms, ethical design principles, continuous monitoring, and interdisciplinary oversight will help to mitigate risks. For particularly risk-averse organisations, creating multi-tiered review processes, embedding ethical considerations directly into any and all AI-based architectures, and establishing adaptive governance models, companies can ensure that AI systems remain fundamentally subject to human judgment. The framework should mandate clear chains of responsibility and develop transparent audit trails for further assurances. Additionally, laws and policies across the world have either been made or are in the process of being put in place, many to provide extra safeguards against the improper use of AI.
Some Advice from Bill Gates
In an article published by LinkedIn, Bill Gates offers some advice on how to deal with the risks of AI-systems. In his view the world has learned a lot about handling problems caused by breakthrough innovations. This is not the first time a major innovation has introduced new threats that had to be controlled. Soon after the first automobiles were on the road, there was the first car crash. But policy makers did not ban cars. Instead, they defined speed limits, safety standards, licensing requirements, drunk-driving laws, and other rules of the road. We are now in the earliest stage of another profound change, the Age of AI. AI is changing so quickly that it is not clear exactly what will happen next. But history shows that it is possible to solve the challenges created by new technologies. But we need to move fast. Governments need to build up expertise in artificial intelligence so they can make informed laws and regulations that respond to this new technology. They will need to grapple with misinformation and deep fakes, security threats, changes to the job market, and the impact on education. The law needs to be clear about which uses of deep fakes are legal and about how deep fakes should be labelled so everyone understands when something they are seeing or hearing is not genuine. Political leaders will need to be equipped to have informed, thoughtful dialogue with their constituents. Last but not least they will have to collaborate with other countries rather than going it alone.
Conclusion
AI-systems are increasingly shaping our daily lives, offering opportunities for economic growth, and tackling social, economic and environmental challenges. Yet these systems also present growing risks, such as enabling malicious cyber activity, spreading disinformation, invasive surveillance and privacy violations. As AI continues to evolve, governing these developments becomes even more crucial. AI governance addresses the need to promote the benefits of AI systems while preventing and mitigating their risks. However, the rapid pace of AI developments makes designing governance mechanisms that can stand the test of time challenging. To help address this challenge, it is crucial to include anticipation and forward-looking approaches in AI governance.