Data is the lifeblood of AI models, providing them with the information they need to work effectively and improve over time. However, with great power comes great responsibility. Ensuring high-quality, secure, and private data is paramount to using AI technology effectively and responsibly.
The world of AI is in the midst of a revolution, creating powerful new assets: AI's ability to process vast amounts of data and automate tasks is transforming industries, uncovering hidden business opportunities, and streamlining operations.
This technological leap will enable organizations to gain a competitive advantage, increase productivity, and pave the way for breakthrough discoveries. By embracing AI, companies can realize enormous business value and become leaders in this exciting new era.
But without data, there is no AI. All algorithms and models use data to calculate solutions or generate answers to questions. In essence, data is the lifeblood of AI models, providing the input they need to function effectively and improve over time.
Responsible AI (RAI) is about using AI technologies ethically, fairly, and responsibly, and minimizing risks. This includes putting guardrails in place to ensure AI is used for good. Data governance plays a key role in RAI because the data used to train AI systems is the basis for decision-making.
Data access in AI must be governed by strict privacy and security controls: AI models must be allowed access only to data that is essential to their operation and that has been properly authorized.
Therefore, preparing high-quality, secure, and private data for AI is paramount. Data quality is important because it directly impacts AI outcomes, while security and privacy are essential to protect sensitive information and comply with regulations.
While all aspects of data governance are important, these three areas have a significant impact on the performance and reliability of AI systems. A comprehensive governance strategy that addresses these areas will facilitate effective and responsible use of AI.
Leaving data unprotected from AI can be a costly gamble. Hackers can use AI to launch targeted attacks, leading to huge fines, legal battles, and costly remediation efforts. Even if the breach is not malicious, the reputational damage can be significant.
Exposed data also puts us at risk of identity theft and manipulation by AI systems designed to exploit it, so an investment in data protection is an investment in security and peace of mind.
In the corporate world, when AI systems have access to incorrect data, it can have a cascading, detrimental effect: the reliability of AI outputs can be compromised, raising questions about their trustworthiness.
For example, bias in data could skew AI decisions and violate an organization's fairness and diversity commitments. This erosion of trust in AI accuracy could have long-term negative effects on the acceptance and use of AI.
Legal issues are also a major concern, as mishandling of data can lead to regulatory violations and non-compliance with ethical standards, and diverting resources to unproductive AI projects can lead to financial and operational losses.
Strict data governance mitigates these risks and ensures that AI remains a reliable and efficient business tool. Implementing AI systems is exciting, but it's important to address ethical considerations. Here are some tips to get you started in the right direction:
Align your AI goals with your organisation's values and aim to have a positive impact on society. Transparency is key – you must be able to explain how your AI makes decisions. This builds trust and allows for human intervention where necessary (this is part of the European Union's AI law).
Data governance is the foundation of RAI: we minimize bias by using diverse, high-quality data that reflects the real world, and ensure fair outcomes by involving people from different backgrounds in the development process. We implement robust data security measures to protect user privacy and comply with data privacy regulations.
RAI is an ongoing process: regularly monitor the output of your AI systems for bias and refine your data and algorithms as needed. Actively learn and adapt as AI technology evolves and societal norms change.
Data governance is a key part of the responsible use of AI technology, so focus on data quality and protection to avoid negative consequences such as compromise of AI output and legal and financial issues – after all, data is your most valuable asset.
Karin Olivier, Principal Transformation Consultant, NTT Data Middle East and Africa;
