Safeguarding Public Health in the Age of AI: The Importance of Ethical Use of Large Language Models

Recently Who urged AI development community to exercise ethical AI models. Here is the summary of it’s press release WHO calls for safe and ethical AI for health

 Press Release Summary:

“The World Health rganizatioOn (WHO) is urging caution in the use of large language models (LLM),s AI-generated systems that imitate human communication, to protect human well-being, safety, and public health. While LLMs offer potential benefits for healthcare, their rapid adoption without proper scrutiny poses risks.

Concerns include biased training data that may generate misleading health information and LLMs producing authoritative yet incorrect responses. LLMs may be trained without consent and fail to protect sensitive user data. Additionally, they can be misused to disseminate convincing disinformation, undermining trust in reliable health content.

While WHO acknowledges the value of technology, including LLMs, in supporting healthcare professionals, patients, and researchers, caution and adherence to ethical values are crucial. The widespread adoption of untested LLM systems can lead to errors, harm to patients, and loss of trust in AI.

WHO recommends addressing these concerns and measuring benefits before widespread use of LLMs in healthcare. Ethical principles and governance, outlined in WHO guidance on AI for health, should be applied. These principles protect autonomy, promote well-being and safety, ensure transparency, foster responsibility and accountability, ensure inclusiveness and equity, and promote responsive and sustainable AI.

By approaching LLMs with caution, ensuring ethical guidelines, and transparent evaluation, their potential can be realized while protecting public”

Importance of Safeguarding Public Health in the Age of AI

In recent years, large language models (LLMs) powered by artificial intelligence (AI) have gained immense popularity and are being used in various domains, including healthcare. While these models hold great potential for supporting health-related needs, their rapid adoption without careful consideration can pose risks to public health and well-being. In this blog post, we will explore the importance of ethical use of LLMs in protecting public health, addressing concerns related to biased data, misinformation, privacy, and disinformation. By prioritizing ethical principles and responsible governance, we can harness the benefits of LLMs while safeguarding the health of individuals and communities.

The Power of Large Language Models in Healthcare::

LLMs, such as ChatGPT, Bard, and Bert, are AI systems that simulate human understanding and communication. These models have shown promise in revolutionizing healthcare by improving access to information, aiding decision-making, and enhancing diagnostic capabilities. With their ability to process vast amounts of data and generate responses that appear authoritative, LLMs have the potential to support healthcare professionals, empower patients, and advance medical research.

Addressing Biased Data and Misinformation:

One of the critical concerns associated with LLMs is the presence of biased training data. Biased data can perpetuate inequalities and generate misleading or inaccurate health information, potentially leading to adverse health outcomes. To ensure ethical use of LLMs, it is crucial to address this issue by carefully curating diverse and representative training datasets. Rigorous evaluation and transparency are vital to identify and rectify biases in LLMs, allowing for the delivery of accurate and unbiased health-related information.

Protecting Privacy and Informed Consent:

Another aspect of ethical LLM use is protecting user privacy and ensuring informed consent. LLMs may be trained on data without obtaining proper consent, raising concerns about the unauthorized use of personal information, including health data. To address this, stringent data privacy regulations and protocols must be implemented. Developers and organizations utilizing LLMs should prioritize the protection of sensitive user data, providing transparency and clear guidelines on data usage and privacy policies. By doing so, individuals can feel confident in using LLM-based applications without compromising their privacy rights.

Combating Disinformation and Maintaining Trust:

The rise of LLMs also raises concerns about the potential misuse of these models to generate and disseminate disinformation. LLMs have the ability to create text, audio, and video content that can be difficult to differentiate from reliable health information. This can undermine public trust in accurate medical advice and exacerbate the spread of false information. To counteract this, it is crucial to invest in technologies and strategies that can detect and mitigate the dissemination of health-related disinformation. Collaborative efforts between technology companies, healthcare professionals, and regulatory bodies are essential to build robust systems that can maintain public trust and ensure the delivery of reliable health content.

Conclusion:

As AI technology continues to advance, the ethical use of large language models becomes paramount in protecting public health and well-being. By addressing concerns related to biased data, misinformation, privacy, and disinformation, we can harness the potential of LLMs to enhance healthcare while mitigating potential risks. It is crucial for developers, organizations, and policymakers to prioritize transparency, responsible governance, and adherence to ethical principles. By doing so, we can foster a future where AI-powered technologies support healthcare professionals, empower individuals, and promote the overall health of our global community.

Cheers,

Venkat Alagarsamy