The choices we collectively make regarding AI will profoundly shape the future world. With the technology in a phase of rapid adoption, those responsible for the creation and use of AI systems are in a position of huge influence. They can steer us towards a brighter future if they embrace the principles and techniques of responsible AI, a school of thought that seeks to make AI ethical, fair, and beneficial for society.
Practising responsible AI involves principles such as addressing bias via data governance, emphasising explainability in algorithmic systems, and seeking external assurance. This commitment, which must extend throughout all levels of an organisation, can also reap major advantages in a business context.
While we will examine the concept of responsible AI itself here, check out our article on the seven pillars of responsible AI for an in-depth explanation. In this article, we’ll focus on the three key reasons that enterprises, companies, and institutions need to operationalise these standards in AI governance.
These pillars are not mutually exclusive – there are many areas of overlap, and they should be considered not in isolation but instead as part of a holistic, integrated approach to AI.
Before we address the commercial, strategic, and legal reasons why organisations need to implement responsible AI, it is important to first examine the ethical angle. This first principle should underpin all uses of AI.
From selecting which posts to display while scrolling social media to making potentially life-changing decisions like granting loans or approving job applications, AI exerts enormous influence over our daily lives and demands a comprehensive ethical evaluation.
AI-made decisions must be carried out fairly and without bias. If not, the result will be unfair outcomes and the perpetuation of discrimination against already-marginalised groups within society.
Organisations have a moral obligation to do everything in their power to proactively mitigate this risk. Developers and deployers of AI should prioritise data governance, taking steps to ensure that the data used to train AI systems is diverse and representative. This creates the conditions for AI systems to make bias-free decisions and avoid discriminatory patterns present in historical data. This requires a comprehensive approach to identifying and, crucially, understanding algorithmic biases before taking robust measures to rectify them.
While there is a risk that AI can crystalise bias if it goes unchecked, the advent of the technology also represents an opportunity to reset systems and take steps towards ridding institutions of deep, culturally ingrained prejudices.
Transparency and explainability are also principles that organisations have a duty to uphold in the context of AI. To build trust and accountability, organisations should provide clear documentation of their AI systems, including data sources, algorithms used, and the decision-making process. This allows users to understand how AI-driven outcomes are reached. Additionally, AI models should be designed to provide human-readable explanations for their decisions, especially in sensitive domains like healthcare and law, where justifications are essential for professionals to trust and validate AI recommendations. These principles fall under the umbrella of explainable AI (XAI), a subset of responsible AI.
Protecting users' privacy and personal information is paramount for individual organisations and in the broader context of responsible AI. Organisations must implement robust data protection measures, like anonymisation and encryption, to secure sensitive user data. Obtaining explicit user consent for data usage in AI training is also crucial. Privacy considerations should be integrated into AI system design from the start to minimise risks and address potential vulnerabilities. Regular monitoring and audits of AI systems should be conducted to maintain high privacy standards and ensure compliance with evolving privacy regulations, fostering a trustworthy relationship between AI technology and users.
The global recognition of AI's transformative potential and the inherent risks it presents has spurred lawmakers worldwide to act. While responsible AI was once primarily an ethical concern, organisations must now proactively implement its principles to avoid potential financial and reputational repercussions.
A notable example of such regulatory measures is the EU AI Act, currently in the final stages of the legislative process. This act holds organisations liable for significant fines, reaching up to €40 million or 7% of their global turnover (whichever is higher), if they fail to comply with its provisions. This legislation is just one of many examples, signalling an upcoming deluge of new regulations spanning almost every jurisdiction in the coming years.
The EU AI Act places strong emphasis on transparency and safeguarding individual rights, aligning closely with the core focus of responsible AI. As organisations navigate the evolving regulatory landscape, adopting a framework that incorporates responsible AI principles becomes essential for achieving regulatory compliance. This approach not only mitigates legal risks but also helps organisations align with societal expectations for ethical and trustworthy AI development and use.
By embracing responsible AI, organisations position themselves on the right path to meet emerging regulatory standards. Beyond mere compliance, it demonstrates a commitment to responsible and accountable AI practices, which can positively impact their reputation and public perception. As responsible AI principles gain traction globally, organisations that actively prioritise them stand to gain a competitive edge by earning the trust and confidence of customers, investors, and stakeholders.
A central tenet of responsible AI is the establishment of trust with stakeholders, both internally and within the wider AI ecosystem. This foundational trust is paramount because, without it, users may be hesitant to fully embrace AI.
A February 2023 report co-authored by KPMG and the University of Queensland found that only one in two people believe that the benefits of AI outweigh the risks. This hesitation could have far-reaching consequences, not only on a holistic level where it has the potential to curtail the societal benefits that AI can offer, but also on an individual organisational level, where the lack of AI adoption can lead to adverse commercial repercussions.
Just as many companies are now actively incorporating environmental, social, and governance (ESG) principles into their operational frameworks to enhance their reputation and brand image, establishing trust in AI systems can have a similarly transformative positive impact. The significance of trust is particularly pronounced in the realm of AI, where the stakes are high, and the impact on individuals and society can be profound. By demonstrating a commitment to responsible AI practices, organisations can attract customers, investors, and partners who value ethical and socially responsible principles.
When users have confidence in an organisation's AI systems and perceive a strong alignment with its values, they are more inclined to remain loyal and satisfied customers. This loyalty extends not only to customers but also to other stakeholders, such as partners and employees. Trust among these key players fosters a culture of collaboration and innovation. When stakeholders have faith in an organisation's AI systems, they are more willing to share data and engage in partnerships, creating an environment ripe for the cultivation of new ideas and the flourishing of innovation.
Building and nurturing trust within the AI ecosystem is a fundamental aspect of responsible AI. By prioritising trust, organisations can harness AI’s full potential, delivering benefits on societal, commercial, and collaborative levels, while positioning themselves as beacons of ethical and trustworthy AI implementation.
Holistic AI is at the forefront of responsible AI. Our dedicated platform and solutions empower organisations to adopt and scale AI with confidence. To find out how we can help your organisation take steps towards external assurance, schedule a call with our expert team.
DISCLAIMER: This blog article is for informational purposes only. This blog article is not intended to, and does not, provide legal advice or a legal opinion. It is not a do-it-yourself guide to resolving legal issues or handling litigation. This blog article is not a substitute for experienced legal counsel and does not provide legal advice regarding any situation or employer.
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