Pursuing the exciting opportunities and excellence offered by rapid advancements in AI is no doubt exciting. However, it is pertinent to maintain utmost diligence when harnessing AI technologies to aid your organization. This becomes even more significant as organizations increasingly align their business goals with responsible AI (RAI) standards—not just to attain lofty ideals but as a fundamental necessity to fully leverage AI's potential. Based on research conducted by MIT Sloan and Boston Consulting Group (BCG), which involved over 1,093 participants and interviews with C-level executives and AI subject matter experts, 84% of respondents believe responsible AI (RAI) should be a top management-level priority, but in practice, only 52% of companies are engaged in this policy or system of governance. This is further reinforced as only 79% of these companies admitted that their implementations were hindered by issues of scalability and scope.
So, what is affecting companies’ ability to integrate widespread RAI practices? The primary issue seems to stem from the misunderstanding between RAI and similar contemporary concepts such as ethical AI. In fact, 36% have acknowledged the evolving nature and inconsistencies of the practice as key contributing factors. Other challenges that companies face include struggle for viable expertise and talent (54%), lack of training or knowledge (53%), and lastly, little to no prioritization from business leaders to administer RAI (43%), due to inadequate funding or awareness (43%). With AI’s ever-expanding reach and influence and the insurmountable evidence of the necessity of “responsible” policies and systems, let’s look at what responsible AI entails, putting it through the lens of existing methodologies and leaning into experiences of seasoned RAI leaders.
What is Responsible AI?
Responsible AI is the method to develop, assess, and deploy AI systems that are evaluated and deemed safe, trustworthy, and responsible . Responsible AI emphasizes practical aspects of operating AI with a keen focus on stakeholder engagements and operationalization driven by quantifiable metrics and KPIs. It ensures that the impact of innovative AI is underlined by the goal of creating beneficial and equitable outcomes.
On the other hand, ethical AI centers on the foundational and moral principles guiding AI by drawing on human-centric approaches such as fairness, welfare, bias prevention, and long-term ethical implications. While the two concepts overlap and may be often used interchangeably, responsible AI takes a far more proactive approach to enact ethical AI philosophies.
Why Responsible AI frameworks matter: 5 key principles for effective AI deployment
To demonstrate how RAI can be applied effectively for business organizations and enterprises, Microsoft introduced the Responsible AI Standard framework, emphasizing the following fundamental principles that augment and support the AI creation process:
- Fairness and inclusiveness: Be mindful of equitable treatment amongst all individuals and avoid biases among similar groups. An example of this is to provide a consistent recommendation for services such as medical treatment, loan applications, or employment to individuals with comparable symptoms, financial situations, or qualifications.
- Reliability and safety: AI systems must function reliably, safely and consistently to instill user confidence. They should perform as intended, handle unexpected conditions safely, and be resistant to compromising manipulative tactics. Its behavior and ability to mediate a variety of conditions is contingent on the thoroughness of the developers’ design and testing processes.
- Privacy and security: Safeguarding privacy for personal and business data is increasingly complex with AI advancements. Consequently, prioritizing security and access to ensure accurate and informed decision-making is paramount. AI systems must maintain transparency to adhere to privacy laws in data collection, usage, and storage. Additionally, they must provide consumers with appropriate controls over how this data is used.
- Transparency: The influence of AI systems on decision-making is just as important as how users perceive the process leading to the result. For example, banks use AI to determine customer credit approval, and companies rely on AI to identify the best candidates for a specific role. Therefore, interpretability is vital as it bridges stakeholders' understanding of AI functions, allowing them to resolve performance and fairness issues and unintended outcomes.
- Accountability: Developers of AI systems need to take responsibility for the AI system’s functionality. Organizations should leverage industry standards to establish accountability practices. These practices will make sure AI systems do not become the ultimate decision-makers in matters affecting people’s lives. Additionally, they help humans retain significant oversight and control over highly autonomous AI systems.
Leveraging the Azure Machine Learning platform, and its Responsible AI dashboard, users are provided analytical and assessment tools, including debugging features, to enhance data-driven decision-making. For instance, the dashboard can help data scientists and developers understand critical model failures and identify data subsets with higher error rates for specified improvement opportunities and thoroughly addressing reliability and safety concerns.
Although Microsoft's RAI Standard offers a robust framework for guiding AI development, it's recommended to explore and employ additional RAI frameworks from tech-industry leaders for a well-rounded perspective.
How have early adopters and leaders benefited from RAI?
After a detailed exploration of both the "what" and the "how" of responsible AI, what key insights can be gleaned from seasoned RAI practitioners? First and foremost, leaders in responsible AI (which account for 16% of respondents from the MIT study) recognize that RAI presents organizational challenges rather than purely technical ones, requiring significant time investments to build comprehensive RAI programs. In turn, these leaders enjoy better products and services, along with long-term profitability. As a fact, 41% of RAI leaders report measurable business benefits, and more than half feel well-prepared to navigate the evolving AI regulatory landscape.
Mature RAI programs are also a top priority for senior management, with over 77% of leaders allocating substantial resources to training, talent acquisition, and budgeting. It's apparent that decisions around RAI and the implementation of AI in a responsible manner are driven directly from the top as evidenced by nearly half of RAI leaders saying that CEOs are actively involved in these efforts.
Top management has recognized that mature RAI programs must extend beyond the organization itself, involving a broader range of roles—an average of 5.8 key stakeholders—in their expansive and inclusive AI journey. Many leading companies are integrating RAI into their corporate social responsibility initiatives, even viewing society itself as a prime stakeholder. For these organizations, the values and principles guiding responsible behavior are applied across their entire portfolio of technologies and systems.
How can you prioritize Responsible AI to secure future ROI?
It typically takes around three years for responsible AI investments to yield favorable business ROI. The time to act is now, as organizations must begin preparing the next steps to secure top-tier expertise and implement training for this undertaking. AI experts are urging leaders and key stakeholders to stress advancing responsible AI maturity ahead of AI development itself. Doing so can help mitigate the significant risks associated with scaling AI efforts without proper oversight.
At Alithya, we know the precautions needed to approach AI with proper insight. Our comprehensive support helps clients achieve their AI goals through a realistic, value-driven approach, aligning responsible frameworks with the specific markets in which you operate. Our aim is to strengthen your AI investments by equipping your organization with the right talent, teams, and expertise.
As a global provider of learning services and solutions in the digital space with over 30 years of experience, we are committed to transforming how you operate AI technologies through the lens of ethical and trust, so that innovation isn’t stifled and the products created are human-friendly first and foremost.
To support this initiative, Alithya has developed a comprehensive Copilot Implementation eBook designed to guide stakeholders through assessing technical readiness. It delves into crucial data security, sensitivity, and information protection strategies that align with the responsible practices highlighted in this blog. Whether you're a C-level executive, business leader, or IT manager, this eBook provides the critical insights needed to stay ahead of the AI curve while upholding integrity. Download this eBook now to stay informed and make the most of your responsible AI and reap its business benefits for years and decades to come!