AI presents an enormous opportunity to turn data into insights and spark better decision-making. Building and scaling responsible AI requires governance and the ability to direct, manage, and monitor the AI activities of your organisation.
This eBook describes:
- The key benefits gained with automated AI governance are significant for both today’s generative AI and traditional machine learning (ML) models.
- How you can get started with AI governance, the building blocks, and best practices to help your teams accelerate responsible AI.
The eBook emphasises the significance of AI governance in this era of rapid AI adoption. It highlights the numerous benefits of AI, including improved productivity and efficiency, but also emphasises the potential risks and challenges associated with implementing AI solutions.
According to the eBook, AI governance is crucial for ensuring that AI solutions are developed and deployed in a responsible, transparent, and explainable manner. It involves the intersection of people, process, and technology and requires a holistic approach that addresses the various challenges and risks associated with AI.
It identifies three main challenges in scaling AI: operationalisng AI with confidence, managing risk and reputation, and addressing changing AI regulations. Operationalising AI with confidence requires ensuring that AI solutions are reliable, secure, and transparent and that they can be trusted to make decisions and take actions without human oversight. Managing risk and reputation involves mitigating the potential risks and negative consequences associated with AI, such as data breaches, errors, and biases. Addressing changing AI regulations involves ensuring compliance with various laws and regulations related to AI, such as data protection and privacy laws.
The eBook highlights the importance of explanation, ethics, and bias in AI decision-making. It notes that 80% of business leaders see at least one of these ethical issues as a major concern. Explainability refers to the ability to understand and interpret the decisions and actions made by AI systems. Designing and developing AI systems in accordance with human values and principles, such as fairness, transparency, and accountability, constitutes ethics. Bias involves ensuring that AI systems do not perpetuate or amplify existing biases and prejudices.
It discusses the importance of roles across the AI lifecycle, including data scientists, data engineers, business leads, and ML engineers. It highlights the need for collaboration and alignment among these stakeholders to ensure that AI solutions are developed and deployed in a responsible and effective manner.
The eBook also introduces IBM’s watsonx.governance toolkit, which is designed to help organisations build responsible, transparent, and explainable AI workflows. The toolkit provides regulatory compliance management, automatic metadata capture, risk management, and lifecycle governance. It is designed to help organisations address the various challenges and risks associated with AI and to ensure compliance with various laws and regulations.
As you read along, the eBook also highlights IBM’s principles of responsible AI, which include the need for transparency, explainability, and human oversight. These principles emphasise the importance of ensuring that AI systems are designed and developed in accordance with human values and principles and that they are transparent, explainable, and accountable.
The document concludes by emphasising the importance of AI governance in ensuring that AI solutions are developed and deployed in a responsible, transparent, and explainable manner. It encourages readers to take action and start building responsible AI systems and provides links to additional resources, including IBM’s AI ethics and governance information.
In summary, the document provides a comprehensive overview of the importance of AI governance in the era of rapid AI adoption. It highlights the various challenges and risks associated with AI and emphasises the need for a holistic approach to governance that addresses the intersection of people, processes, and technology. The document introduces IBM’s watsonx.governance toolkit as a solution to help organisations build responsible AI workflows and highlights IBM’s principles of responsible AI.
The benefits of AI governance include:
- Improved transparency and accountability
- Enhanced compliance with laws and regulations
- Mitigation of risks and negative consequences
- Increased trust and confidence in AI systems
- Improved decision-making and outcomes
The challenges of AI governance include:
- Operationalising AI with confidence
- Managing risk and reputation
- Addressing changing AI regulations
- Ensuring explainability, ethics, and bias in AI decision-making
The solutions to these challenges include:
- Implementing a holistic approach to AI governance
- Using IBM’s watsonx.governance toolkit
- Following IBM’s principles of responsible AI
- Collaborating and aligning stakeholders across the AI lifecycle
Accelerate responsible, transparent, and explainable AI for generative and machine learning models with IBM watsonx.governance. If you aren’t ready yet, explore watsonx or book a live demo with an IBM expert.
For over eighty years, IBM Australia has actively collaborated with the Australian public sector, driving strategic goals and advancing digital transformation. IBM Australia, a proud subsidiary of International Business Machines, delivers exceptional expertise in cloud, analytics, cognitive computing, mobile, security, and social technologies to the Australian market. The organisation enhances the local economy and achieves measurable outcomes through its diverse offerings, including IT services, business consulting, security solutions, outsourcing, software development, application innovations, and strategic business services.
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