Every technological wave brings enormous benefits that lead to both technological and economic advancements. These advancements often lead to improved quality of life, increased productivity, and greater access to information. In a bid to stay ahead, every organisation or nation adopts the latest technology, the latest best practices, and so on. However, these technologies or practices can also pose challenges that society must navigate.

The current wave of technology is artificial intelligence, which has evolved from traditional rule-based systems to systems that generate ideas on their own, as well as agents that are autonomous and can perform actions and tasks. Artificial intelligence (AI) has significantly transformed the global landscape; organisations and nations are leveraging it to sustain their competitive advantages.

The benefits of artificial intelligence are enormous; despite fears and scepticism, most nations and organisations are adopting it for productive purposes in order to maintain their competitive edge. The benefits include business and operational efficiency, enhanced decision-making and insights, and societal advancement in sectors such as healthcare and national security, etc.

While the benefits are undeniable and immense, it's also possible for the drawbacks to equal or surpass the positives, leading to a need for caution. Proper guardrails need to be in place when implementing AI. Policymakers and implementers need to focus on a human-centred approach to optimise the benefits of AI, encourage innovation, and eliminate the concerns that limit the adoption of AI-based solutions.

ADOPTING A HUMAN-CENTRED APPROACH.

Transparency and Explainability

AI systems should function in a way that humans can understand and scrutinise. Users should know when they're interacting with AI and understand how decisions are made. The explanation is only useful if it is meaningful and understandable to the user. Furthermore, creating an environment that empowers users to engage with AI technologies will enhance their trust and willingness to embrace these innovations. By prioritising education and clear communication, stakeholders can bridge the gap between technology and its users, making AI an integral and beneficial part of everyday life.

For instance, a bank using AI for loan approvals should be able to explain to applicants which factors influenced their decision, such as credit history, income verification, or debt-to-income ratio.

Strategies for achievement:

• Implement model cards that document AI system capabilities, limitations, and intended use cases.
• Develop user-friendly interfaces that explain AI-driven decisions in plain language.
• Create audit trails that track how data flows through AI systems.

Fairness and Non-Discrimination

AI systems should ensure equitable treatment of all individuals and groups, avoiding the perpetuation or amplification of societal biases. This can be accomplished by regularly reviewing and updating algorithms to reflect current social dynamics and ensuring diverse datasets are used in training. Engaging with affected communities can yield valuable insights and foster trust in AI systems.

Strategies for achievement:

• Conduct bias audits across different demographic groups before deployment.
• Use diverse and representative training datasets.
• Establish fairness metrics specific to your application domain.
• Implement continuous monitoring to detect discriminatory outcomes.

Example: Healthcare AI systems should perform equally well across different racial and ethnic groups. Microsoft's research into fairness in medical imaging has shown the importance of training on diverse patient populations.

Privacy and Data Protection

AI development must respect individual privacy rights and protect sensitive information throughout the data lifecycle. This requires robust encryption methods and adherence to regulations such as GDPR. Additionally, transparency in data usage and the ability for individuals to access or delete their data can foster trust and accountability in AI systems.

Strategies for achievement:

• Apply privacy-by-design principles from the earliest stages of development.
• Please implement data minimisation by collecting only the necessary information.
• Use techniques like differential privacy and federated learning.
• Establish clear data governance policies with defined retention periods.

Example: Apple's use of on-device processing for Siri requests, keeping voice data on the user's phone rather than sending it to cloud servers

Accountability and Oversight

Clear lines of responsibility must exist for AI system outcomes, with mechanisms for redress when things go wrong. This ensures that stakeholders are held accountable for their decisions and actions. Additionally, regular audits and transparency reports can enhance trust and ensure compliance with ethical standards.

Strategies for achievement:

• Designate AI ethics officers or committees with decision-making authority.
• Create incident response protocols for AI failures.
• Establish human-in-the-loop processes for high-stakes decisions.
• Develop clear escalation paths for ethical concerns.

Example: The EU's AI Act requires high-risk AI systems to have human oversight, particularly in areas like employment, law enforcement, and critical infrastructure.

safety and robustness

AI systems should perform reliably under various conditions and fail gracefully when encountering unexpected situations. This ensures that users can trust the technology, knowing that safeguards are in place. Furthermore, ongoing assessments and updates will be crucial to adapt to new challenges and maintain ethical standards in AI development.

Strategies for achievement:

• Conduct adversarial testing to identify vulnerabilities.
• Implement redundancy and fallback mechanisms.
• Regular security audits and penetration testing
• Establish performance benchmarks and monitoring systems.

Example: Autonomous vehicle companies like Waymo conduct millions of simulated miles of testing. Before deploying the vehicles on public roads, they test edge cases and unusual scenarios.

Beneficial and Purpose-Driven

AI should be developed and deployed to serve genuine human needs and contribute positively towards society. Selfishness and wickedness must not be at the core of AI deployment or use. To achieve this, proactive collaboration between developers, ethicists, and policymakers is essential, ensuring that AI technologies are aligned with societal values. By prioritising transparency and accountability in AI systems, we can foster trust and encourage widespread acceptance among the public.

Strategies for achievement:

• Conduct impact assessments before deployment.
• Engage stakeholders, including affected communities, in the design process.
• Establish clear success metrics beyond pure technical performance.
• Regular review of whether the system still serves its intended beneficial purpose

Example: Google's AI for Social Good program applies machine learning to challenges like disaster response, environmental conservation, and accessibility for people with disabilities..

Conclusion

A people-centered approach is beneficial for maximising AI's potential. This makes the adoption universal and more organic. If human goals are not aligned with AI development, the use and development of AI systems could result in irresponsible applications that cause harm or serve harmful agendas.

Developing AI in a responsible way is a long-term process that needs ongoing dedication and teamwork. This journey must involve diverse stakeholders, including ethicists, technologists, and community representatives, to ensure that the systems created are not only effective but also equitable. By fostering a culture of transparency and inclusivity, we can better navigate the complexities of AI and harness its benefits for all.