Standardizing AI Safety and Practices: Ensuring a Responsible Future

Artificial intelligence (AI) stands out as a transformative force in today's rapidly evolving digital landscape.

The hype around ChatGPT, Dall-E, Google Bard and hundreds of other AI tools competing for our attention is growing by the day even as the big tech companies continue to advance their Large Language Models (LLMs) to think much like humans.

From powering personalized content recommendations to driving autonomous vehicles, AI is making significant inroads into our daily lives. However, as with all groundbreaking technologies, AI comes with its own set of challenges, particularly around safety and ethical considerations. Standardizing AI safety and practices has emerged as a crucial necessity. Let's delve into its importance, areas of interest, and the exciting horizon ahead.

Why AI Safety is Necessary

AI safety is crucial because as artificial intelligence systems become more powerful and integrated into various aspects of our lives, there's potential for unintended consequences or misuse. If AI is not properly designed, supervised, or constrained, AI could make errors with large-scale implications, propagate biases, or be exploited for malicious purposes.

Ensuring AI safety helps in fostering trust, preserving human values, and ensuring that the deployment of these systems brings about societal benefits without posing undue risks. It's a proactive approach to anticipate, understand, and mitigate potential issues before they manifest on a large scale.

The Imperative of Standardization

AI, in many ways, remains a 'wild west', with diverse methodologies, varying levels of transparency, and sometimes ambiguous accountability. Without standardization, AI could continue to act wildly like a stay dog causing havoc in every area of our lives, such as by infringing on our privacy or violating our data protection rights. There is a need for the creation of standards to guide the development and use of AI. The following are the three reasons why I believe AI standardization is necessary:

1. Lack of Consistency: Different organizations might implement AI with variable standards, leading to unpredictable results. Standardization will ensure that organizations meet particular standards of quality and safety measures when developing or using AI.
2. Ethical Concerns: Without universally accepted guidelines, AI could be used in harmful ways or perpetuate biases. A wild animal must be caged to avoid harm to the general public.
3. Safety Issues: Especially in critical applications like healthcare or transportation, inconsistent AI practices can pose significant safety risks.

Areas of Interest

The domain of AI has grown wider than it was a decade ago. There has been a lot of research and development that has gone into AI and this has unravelled the diverse potentials of AI. Several domains urgently require standardization in AI practices:

1. Transparency and Explainability: As AI models become more complex, understanding how they arrive at decisions is crucial. Standardizing ways to make AI 'explainable' will boost trust among stakeholders. Example: In healthcare, if an AI system diagnoses a patient, doctors should understand how that decision was made. Perhaps the AI tool should present a case rather than just generate an isolated decision.
2. Bias and Fairness: AI models can unintentionally perpetuate societal biases present in the training data. Standards should ensure AI systems are fair and don't reinforce prejudices. Example: AI recruitment tools should not favour candidates based on gender, race, or other irrelevant factors. However, this may also depend on the quality of the data that the AI was trained on.
3. Robustness and Security: AI systems must be resilient to malicious attacks and operate reliably in varied conditions. Example: Autonomous vehicles should be impervious to adversarial attacks that attempt to trick their vision systems.
4. Privacy: With AI often requiring vast amounts of data, standardizing how AI respects user privacy is paramount. Example: AI-driven personalized marketing should not compromise an individual's private data.
5. Deployment and Monitoring: Post-deployment monitoring is crucial to ensure AI systems function as intended in the real world. Example: An AI chatbot for customer support should be regularly monitored and updated to handle new queries effectively.

The Future Landscape

1. International Collaboration: As AI transcends borders, international bodies could emerge, similar to the World Health Organization (WHO), to oversee global AI safety and practices.
2. Certifications and Badges: Organizations might receive certifications for adhering to AI standards, similar to ISO certifications in other industries.
3. Public and Private Partnerships: Governments and private enterprises could collaborate, combining regulatory oversight with on-ground practicality.
4. Evolving with AI's Progress: As AI advances, the standards will need periodic revisiting and revision to remain relevant.
5. Educational Initiatives: Universities and institutions might introduce courses and curricula focused on standardized AI practices, instilling these values in future AI practitioners.

Disclaimer: I am not an expert in AI but a researcher in AI Law and Policy, and this article does not comprise any policy advice.