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Artificial Intelligence
Shape the future with human-first AI
Highlights
- Adopting AI responsibly is a must to mitigate risks and safely get the best out of the technology.
- A comprehensive responsible AI framework based on five guiding principles—security, accountability, fairness, transparency, and identity protecting — can help identify and mitigate risks associated with AI and generative AI deployments.
- Embracing responsible AI positions organizations to unlock innovative, secure, and equitable advancements by using AI’s transformative power to drive progress and innovation.
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Why responsible AI
Adopting AI responsibly is the only way to identify and mitigate risks that come with the technology.
The future is artificial intelligence (AI). As technology continues to evolve and mature, enterprises must adopt it at scale to stay relevant, compete, and grow. However, AI applications have associated risks. With growing concerns about the possibility of AI generating undesirable outcomes, organizations need to approach the technology with caution. Adopting AI responsibly is the only way to identify and mitigate risks, maintain control over the outcomes, and safely get the most out of AI.
To address potential issues and unwanted consequences from AI, organizations must infuse responsible AI practices into their AI and generative AI (GenAI) applications (see figure 1). This will require them to continually monitor and measure the AI applications to ensure they comply with the fundamental aspects of responsibility. Some of the key indicators that organizations can use to ensure responsible AI are:
- Brand reputation: Measures changes in customer trust, shifts in public sentiments, and frequency of customers raising concerns related to data privacy.
- Operational efficiency: Confirms AI systems operate without compromising service quality and measures the effectiveness of incident response teams.
- Revenue growth: Assesses increase in sales revenue attributed to AI-driven initiatives and improvements in an organization’s market position.
- Enterprise AI innovation: Evaluates the rate at which new ideas are conceived, developed, and deployed using ethical AI principles.
- Customer engagement: Measures improvements in customer feedback and customer-specific metrics such as interaction, churn, and conversion rates.
- Regulatory compliance: Identifies reductions in fines or penalties and brings down incidents leading to them.
Figure 1: Responsible AI addresses issues and risks in AI projects
Building AI solutions responsibly to ensure outcomes are safe, secure, fair, and explainable enables enterprises to enhance trust and reputation and mitigate legal and regulatory risks. It also helps them realize the technology’s potential to accelerate business transformation and growth. Clearly, responsibility is core to success with AI.
A framework for responsible AI
A framework based on five core tenets can help deploy AI solutions responsibly.
A responsible AI framework built around five core tenets—secure, accountable, fair, transparent, and identity protecting—can help enterprises safely unleash the potential of AI. Figure 2 provides a brief overview of each of these tenets, which can serve as the guiding principles for the development of any AI solution.
Figure 2: A responsible AI framework built around five principles
Breaking down the five core tenets into various sub-tenets and mapping them to specific metrics can help organizations evaluate the extent to which an AI solution complies with the tenets and interventions required to mitigate possible violations of those principles.
Applying the framework
Such a framework can be applied across the entire AI life cycle to accelerate and de-risk organizations’ adoption of AI at enterprise scale.
It can serve as a set of controls or guidelines for effective governance, design, assurance, and advisory for AI adoption as detailed below:
Responsible AI advisory: Enterprises looking to embark on their AI journey need to have their AI systems and solutions assessed to see if they meet ethical AI standards. The advisory needs to identify high-risk AI applications and provide recommendations and roadmaps for improving them. We believe that three types of responsible AI assessments can be particularly effective:
- Readiness assessment: Gauges how prepared an organization is to implement AI responsibly. It should focus on foundational elements such as governance, ethical principles, and stakeholder engagement strategies.
- Maturity assessment: Measures the depth and breadth of an organization's responsible AI implementations, looking at practices across accountability, transparency, fairness, reliability, privacy, and security within an organization’s AI deployments.
- Risk assessment: Evaluates whether a use case adheres to responsible AI principles, scrutinizing the model's development, deployment, and deployment practices followed during the life cycle.
The readiness and maturity assessments can be executed at the enterprise level, while the risk assessment can be conducted at a use-case level. The combined results of the three assessments can be used to generate a responsible AI maturity index, which can serve as a progressive five-point scale indicating the extent to which an enterprise addresses the five core tenets of responsible AI (see figure 3). Indices like this can help companies pinpoint weaknesses in the aspects of responsibility while deploying AI applications.
Figure 3: Five levels of responsible AI maturity
Responsible AI governance: Responsible AI governance encompasses the people, processes, policies, standards, organization, and technologies required to ensure AI is developed and used in a way that’s ethical, transparent, accountable, and aligned with human values. The goal should be to:
- Promote transparency and trustworthiness.
- Ensure fairness and equity.
- Protect privacy and ensure data security.
Robust responsible AI frameworks can help organizations prioritize transparency, fairness, and privacy. Such frameworks should be built around three core components:
- Foundations and principles, which lay the foundation and define the guiding principles for setting up responsible AI practices. This covers relevant regulatory compliance and ethical standards, as well as impact assessment and policy implementation.
- Technical implementation, which involves implementing and enforcing responsible AI technical standards and practices at an enterprise level. Responsible AI strategy, center of excellence, ethical/responsible AI governance board, and guidelines for responsible AI development should also be considered.
- Evaluation and adaptation, which focuses on continuous monitoring and improvements, covering such aspects as behavior analysis, risk assessment, model performance, stakeholder engagement, and change management.
Responsible AI design: Enterprises looking to tap AI opportunities must embrace responsible AI principles whilst designing AI/GenAI-based applications. A responsible AI framework like the one outlined above can help embed the right mitigation steps and interventions throughout the AI life cycle. It can help align AI initiatives with the organization's values and risk appetite, and ensure AI systems adhere to legal, regulatory, and ethical considerations. This will include activities at every step of the AI life cycle—discovery, foundation creation, data preparation, modelling, and building, deploying, and managing the solution (see figure 4).
Figure 4: Responsible AI interventions across the AI life cycle
Responsible AI assurance: Assurance or testing to ensure desired outcomes is one of the key aspects to focus on when implementing responsible AI. It helps ensure that:
- AI systems are safe, secure, reliable, transparent, fair, and privacy compliant.
- Risk mitigation interventions are made at each stage of the AI life cycle.
- Systems adhere to legal and regulatory requirements.
- Systems follow defined policies.
- Systems adhere to defined thresholds.
A responsible AI framework can play a crucial role in validating AI systems for ethical use. Whether it’s in assessment or testing, or identification of responsibility gaps and mitigation interventions necessary to ensure the responsibility of AI solutions, they can help enterprises set themselves up for the AI future.
The way forward
As AI becomes more and more entrenched in the business world and our personal lives, ensuring the technology operates responsibly is vital.
Responsible AI is a commitment to a future where technology serves humanity safely, with fairness. Global organizations have an opportunity to lead by example. By embedding ethical AI frameworks into their strategic initiatives and focusing on responsibly conceived and deployed solutions and systems, they can ensure that AI works to enhance human capabilities and business value, achieving the right balance between controls and innovation.
