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Highlights
- Trust and transparency are the bedrock of AI technology adoption.
- Enterprises need a comprehensive understanding of the ethical, security, and compliance risks associated with AI adoption.
- Responsible AI instils accountability by embedding a value-driven stakeholder-centric mindset at every step of an enterprise’s AI adoption journey.
In this article
In this articleinpage
Taking stock of AI
Ethical AI is vital to the widespread adoption of enterprise AI solutions.
As a disruptive technology, AI plays a pivotal role in spearheading digital transformation. Its ability to reason and make decisions can sometimes have unintended outcomes in the form of ethical, security, and compliance risks for enterprises. Automated algorithms without a framework of ethics may also perpetuate pre-existing biases.
To enable AI technology adoption at scale, organizations must address any gaps in trust, privacy, and compliance. Ethics is an overarching concern. UNESCO’s draft on responsible AI implementation emphasizes the importance of a robust ethical foundation. Ethics must be embedded into the AI governance framework through a set of values, principles, and policies.
Responsible AI brings together ethics, transparency, accountability, fairness, security, privacy, and human centricity to transform enterprises.
A stakeholder view
The three key stakeholder groups in any AI application are the consumers, the enterprise, and the community.
Responsible AI must address the ‘human’ element and understand the trade-offs and metrics for its use cases. This is achieved by aligning the core aspects of responsible AI (AI transformation, governance, and engineering practices) with stakeholders’ needs rather than addressing them individually. Responsible AI sits at the center of three key stakeholder groups—consumer, enterprise, community—each with clearly defined expectations.
Consumer
A human-centric AI empathizes with users. This helps users augment the AI with some degree of human intervention in the AI-led decision-making process. Respecting consumers' privacy and letting them decide what their personalization level should be is the way to build trust.
Community
The community expects AI’s adherence to regulations, social norms, and ethical principles. This includes:
Ensuring demographic parity and bias prevention against people and communities.
Establishing accountability, governance, and redressal mechanisms for AI-based systems.
Preventing malicious use of AI through surveillance and controls.
Addressing the impact of AI systems on sustainability.
Enterprise
Enterprises address consumer and community expectations and standards on the road to business growth. The challenge is to balance business value with trust by carefully navigating brand risks, fairness, ethical principles, and compliance.
Figure 1: Stakeholder groups in AI applications
Adopt responsible AI
The adoption and governance of responsible AI is an enterprise imperative.
As AI-based decision systems get elevated to replace human decision making, they must embrace transparency, fairness, and accountability. Technology leaders must take a responsible route to AI adoption. To drive successful AI-led enterprise growth, organizations must:
Scope the right use cases for investment in terms of value versus risk qualification, process and data readiness, and a target automation level.
Drive the required data, talent, technology, and vendor strategy to ensure the right foundations are in place to deliver required accuracy, robustness, and human-centricity.
Manage the people versus process change impact by embracing disruption. Revamp, reskill, and reorient to build trust and drive AI adoption.
Regulate practices
Building responsible AI requires standardized engineering practices with a framework that is robust, explainable, auditable, and bias-free.
A streamlined AI dataops and modelops life cycle with governance and accountability will ensure transparency and collaboration across teams. These practices need to be supported by an integrated toolset to drive data quality, model performance, explainability, and continuous learning. The toolset should be comprehensive enough to address the variabilities that the enterprise needs such as life cycle, data types, levels of granularity in the analysis, artifacts, access control, and business rules.
Protecting an individual's privacy and being resilient against attacks remains paramount for any toolset. There are techniques such as adversarial training, defensive distillation, and restricted visibility of confidence scores that can offer protection against security attacks and evaluate differential privacy.
Figure 2: Framing the need: Enterprise AI adoption
Paving the way
As AI-based decision systems are elevated to replace human intervention, they must embrace transparency, fairness, and accountability.
Enterprises must take a responsible route to enterprise AI adoption. The AI systems should be designed keeping the human and societal context in mind.
They need to embed a proactive and value-driven stakeholder-centric mindset at every step of the AI transformation journey. A clear path must be paved while maintaining a high degree of customer value, risk mitigation, and ethical lapses.
