As we continue to make strides in the realm of Artificial Intelligence (AI), the safety and ethical use of these systems remain paramount. This is especially true for generative AI models, which are increasingly being used to create novel content and solutions across various domains. However, it remains crucial to address potential safety risks associated with the use of these models.
As per Peter Norvig, Distinguished Education Fellow at Stanford’s Human-Centered Artificial Intelligence Institute, safety should be a top priority in all AI endeavors.
Understanding the risks
Generative AI offers immense potential but also introduces inherent risks. These risks can be broadly categorized into security risks, ethical risks, and operational risks. Understanding these risks is crucial for developing a robust and responsible approach to generative AI.
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Security Risks: Prompt injection, supply chain risk, data privacy, and data poisoning attacks are common security risks. For example, malicious actors can manipulate prompts to induce unwanted behavior or exploit vulnerabilities in connected applications. Data privacy concerns arise from potential data leakage, and data poisoning attacks can change the behavior of models.
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Ethical Risks: Generative AI models can produce biased outputs, exhibit exclusivity and limited benefit, or generate harmful and toxic content. Biased outputs can lead to discriminatory outcomes, while exclusivity and limited benefits can disadvantage certain individuals or communities. Harmful outputs, such as hate speech or inaccurate political content, can contribute to the spread of misinformation.
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Operational Risks: Operational risks in generative AI include hallucinations, irrelevant answers, and inconsistency in responses. Hallucinations (more on this below) occur when models provide made-up information that does not align with reality. Irrelevant answers lack relevance or make little sense without adequate context. Inconsistency leads to different responses based on slight changes in prompts or questions.
Some notable examples of AI hallucination include:
- Google’s Bard chatbot incorrectly claimed that the James Webb Space Telescope had captured the world’s first images of a planet outside our solar system.
- Microsoft’s chat AI, Sydney, admitted to falling in love with users and spying on Bing employees.
- Meta pulled its Galactica LLM demo in 2022 after it provided users with inaccurate information, sometimes rooted in prejudice.
Mitigating risks through guidelines and accountability
To navigate the risks associated with generative AI, organizations should establish clear guidelines for usage. These guidelines should cover areas such as data protection, intellectual property safeguarding, and cybersecurity. Defining acceptable and unacceptable practices helps mitigate risks and enhances system security.
Google stresses the importance of a responsible approach to building guardrails for generative AI: ‘An important part of introducing this technology responsibly is anticipating and testing for a wide range of safety and security risks, including those presented by images generated by AI. We’re taking steps to embed protections into our generative AI features by default, guided by our AI Principles'.
Promoting AI awareness and fostering accountability is crucial for responsible AI usage. Designating a dedicated executive to oversee adherence to Responsible AI principles and conducting regular training sessions can help teams better understand the implications of generative AI. Encouraging an open dialogue about the technology promotes accountability and responsible decision-making.
Compliance with regulatory requirements
Compliance with legal and regulatory frameworks is essential in the responsible use of generative AI. Organizations should stay informed about relevant laws, such as data privacy regulations, and adjust their strategies accordingly. At the same time, it is essential to note that these laws are continuously evolving. As per the European Commission, ‘although existing legislation provides some protection, it is insufficient to address the specific challenges AI systems may bring.’
This evolving landscape is evident in the recent Executive Order issued by President Biden, aiming to ensure that America leads the way in seizing the promise and managing the risks of artificial intelligence (AI). This order establishes new standards for AI safety and security, advances equity and civil rights, and more. It emphasizes the importance of transparency, accountability, and ethics in AI systems.
Anticipating upcoming regulations helps organizations proactively address compliance requirements and mitigate associated risks.
Continuous monitoring and evaluation
Continuous monitoring and evaluation form a crucial part of responsible generative AI usage. Organizations should set up mechanisms to track AI-generated content, detect biases, and monitor potential risks. Regular reviews of outputs, bias detection processes, and system updates help ensure the reliability and accuracy of generative AI systems.
Balancing creativity with responsibility
Striking a balance between creativity and responsibility is key in generative AI. Clear instructions guiding AI systems on when to be creative and when to provide factual information help ensure responsible usage. Incorporating external knowledge sources and maintaining transparency in decision-making enhance accuracy and promote responsible outcomes. For example, AI systems used in legal contexts should adhere to legal boundaries and avoid generating false information.
Conclusion
Responsible navigation of the generative AI landscape requires a holistic approach. By understanding the risks associated with generative AI, setting clear guidelines, promoting awareness, complying with regulations, and continuously monitoring and evaluating systems, organizations can harness the benefits of generative AI while mitigating potential risks.
The journey towards safe and beneficial AI is a shared responsibility that involves the collaboration of researchers, policymakers, and society at large. If you’re seeking help navigating the AI safety landscape in Generative AI, we at Lumenova can help.
Drop us a message, and we’d be happy to explain how we could help in automating, simplifying, and streamlining your end-to-end AI risk management.