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As AI continues to change industries and our ways of living and working, it is critical that we maintain an emphasis on responsible AI development and deployment. AI may hold out promise, but it is also fraught — and get downright dangerous if not conducted responsibly. This, in my opinion and many agree with me on this issue is how we should ensure that as leaders AI will be developed or implemented so it remains fair to all stakeholders.
It was recognised that AI systems must behave ethically to all individuals. It may appear as a very simple principle, but it is critical to remember that if this fairness notion is not considered in the design of AI systems. AI can easily entrench biases and discrimination. This may include a case in which an AI system implemented to assist with job application screening favors candidates of one age, gender or race over another. This sometimes leads to unjust results and perpetuates existing social injustices.
To the extent that we believe in deliberate fairness and inclusion within AI, leaders must hold their systems to these concrete standards. This includes using relevant datasets and embeddings, as opposed to pre-biased models that already contains the bias. Similarly, it is critical to involve experts from a wide range of backgrounds in order that the systems are constructed with an appreciation for social and cultural factors present where they will be deployed.
The whole idea is that AI systems must be interpretable. Automation by Machines should have transparency as AI becomes part of our day-to-day life, decisions taken by these systems need to be trusted and we humans must understand its rationale. It is the interpretability way of understanding what AI algorithms have learned which becomes crucial for building trust and enabling appropriate use by all involved parties.
Some of the ways to interpretability include model-agnostic explanations (eg., SHAP, LIME), which can provide some insights about what lead a complex AI model to make decisions. Sustainable AI design — Leaders need to ensure that we develop all intentional or unintentional bias in our algorithms out of the system and prioritize interpretability from a global perspective, making sure systems give clear explanations of why decisions are made.
Privacy should be present in AI systems. The very public Cambridge Analytica scandal revealed the long-standing vulnerability and clear lack of adherence to privacy regulations, which Canadians demand regarding their personal data. There should be strict rules and regulations regarding privacy while developing AI, all AI systems have to take into account that people's data is their own personal thing — it should not ever get affected.
This ranges from practicing responsible data collection and handling techniques (e.g. obfuscating/ anonymizing / aggregating information) to also processing sensitive user-specific personal private identifiable bits in a eyes-off manner. Leaders also need to ensure compliance with regulation like GDPR and CCPA in addition continuously embedding privacy first thought into all aspects of the organisation.
AI systems should be secure. AI gets worked into the fabric of our everyday life, security is a matter that must be pushed to prevent attacks and maintain an AI-system fit. Leaders should also understand that security is a fundamental part of AI development and they have to secure it as soon as possible.
It also encompasses building AI-resistant systems such as adversarial training and ensures security alerts, monitoring pipelines. These buildouts represent the need to place a high premium on security testing and validation, with policy leadership required in managing shields aimed at detecting AI systems vulnerabilities.
Ultimately, it all comes down to us — the leader with a finger on that button pointing toward responsible AI development and deployment. It additionally wants a large human element,bringing collectively expertise in understanding the moral and social implications of AI, mixed with experience in designing truthful transparent useful methods for all stakeholders.
Leaders must manage their AI bias risk exposure by prioritizing fairness, interpretability, privacy and security to develop responsible AI. by designing AI systems with fair and inclusive concrete goals, by prioritising interpretability and transparency, through responsible data collection practices; security coming in from the ground floor.
These are just some examples of how we can move toward responsible AI in practice and by doing so ensure that it is developed and deployed within an ethical framework designed to work for all involved from the enthusiasts, beneficiaries stakeholders, users or whatever name you want to give them. As leaders, it is our responsibility to take action and ensure that AI is developed and used in a way that is fair, transparent, and beneficial to all.
The engagement in responsible AI is an essentiality not a courtesy. Artificial Intelligence is driving transformation in tech and the human lives alongside, so as we do more with AI it becomes even crucial to focus on ethical development and practices of Artificial intelligence. We lead with a focus on fair, interpretable AI that protects against abuse and attack to make information work for ethical business