Responsible AI is the practice of developing and deploying AI in a fair, ethical, and transparent way to ensure that AI aligns with human values and does not harm individuals or society. Holistic AI identifies five pillars of responsible AI, including data governance, stakeholder communication, engagement at the board-level & collaboration, developing human-centred AI, complying with relevant regulation, explainable AI, and taking steps towards external assurance. By employing a responsible AI approach, businesses can seamlessly integrate AI into their operations while reducing potential harms. This is becoming increasingly important as the use cases and normalization of AI in everyday life and industry have simultaneously resulted in increased regulation and consequences to future-proof and protect consumers from the potential harm of unchecked adoption of AI.

Artificial Intelligence (AI) is projected to increase global GDP by $15.7 trillion by 2030, but with great power comes responsibility. Responsible AI is an emerging area of AI governance that covers ethics, morals, and legal values in the development and deployment of beneficial AI. However, the growing interest in AI has been accompanied by concerns over unintended consequences and risks, such as biased outcomes and poor decision-making. Governments worldwide are tightening regulations to target AI, and businesses will need to comply with global AI regulations and take a more responsible approach to remain competitive and avoid liability. Ensuring responsibility in AI helps assure that an AI system will be efficient, operate according to ethical standards, and prevent potential reputational and financial damage down the road.

Organizations are investing in AI tools and strategies to streamline their processes and gain a competitive edge, but this transition to a reliance on AI comes with heightened risk that must be managed. The National Institute of Standards and Technology (NIST) defines AI risks as potential harms resulting from developing and deploying AI systems. Effective AI governance, risk, and compliance processes enable organizations to identify and manage these risks. AI Risk Management involves identifying, assessing, and managing risks associated with using AI technologies and addressing both technical and non-technical risks. The potential risks and benefits of AI must be understood, and strategies and policies developed to mitigate potential risks. AI regulation is coming, and transparency regarding AI algorithms is a crucial first step. Organizations that implement a risk management framework can move away from a costly, reactive, ad hoc approach to regulation and increase trust and scale with confidence. While AI adoption is soaring, risk management is lagging, and companies need to implement responsible AI programs sooner rather than later to avoid reputational damage and facilitate legal compliance.

AI ethics is a new field concerned with ensuring that AI is used in an ethical way, and it draws on philosophical principles, computer science practices, and law. The main considerations of AI ethics include human agency, safety, privacy, transparency, fairness, and accountability. There are three major approaches to AI ethics: principles, processes, and ethical consciousness. These include the use of guidelines, legislative standards and norms, ethical by design, governance, and integration of codes of conduct and compliance. AI ethics aims to address concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies.