AI brings many opportunities to businesses and we can see the AI boom across different industry verticals. However, it also questions who would be responsible for AI. People are asking important questions about ethics, data governance, trust, and legality when it comes to using AI. In fact, a recent study found that only 35% of global consumers trust how organizations are using AI, and 77% think organizations must be held accountable for their misuse of AI.
To address this, organizations need to be mindful of new and pending regulations and take steps to make sure they're using AI responsibly. That's where Responsible AI comes in.
What is responsible AI?
Responsible AI means designing, developing, and deploying AI in a way that benefits employees, businesses, customers, and society as a whole. It's about being fair and ethical, and building trust so companies can confidently use AI to scale their operations.
Responsible AI encompasses a range of principles and practices, including fairness, accountability, transparency, privacy, and security. It requires that AI systems are designed and trained on diverse and representative data and that their outputs are not biased or discriminatory against any individual or group.
Why is governance critical for responsible AI?
Governance is really important for responsible AI because without it, deploying AI is like asking your models to keep silent about why, what, and how they're making decisions that affect your business. That's not a good idea, because the whole point of AI is to create models that achieve desired outcomes.
When you have proper governance practices in place, you can monitor and regulate the results that AI models yield, as well as the impact they have on your business and the people it serves. This allows you to ensure that your AI is being implemented responsibly and ethically.
In other words, governance helps you to establish guidelines and best practices that ensure your AI is transparent, accountable, and trustworthy. By doing so, you can make sure that the AI you're using is achieving your business goals while also keeping in mind the potential impact it could have on your business and society as a whole.
Why is AI governance even more relevant now?
AI governance for responsible AI is important now because AI is rapidly expanding and being used in various industries. According to a recent study by Gartner, the global business value derived from AI is projected to reach $3.9 trillion in 2022. As AI continues to become more prevalent in business operations, it's important to ensure that its use is responsible and ethical.
The implications of AI can be significant - from perpetuating biases and discrimination to causing job displacement and other unintended consequences. And as organizations increasingly rely on AI to make important decisions, there is a growing need for transparency and accountability in how these decisions are made. Without proper governance, it can be difficult to understand how AI models arrive at their conclusions, which can erode trust and lead to negative outcomes.
For example, in the healthcare industry, AI is being used to diagnose diseases and develop treatment plans. However, if these AI models are not developed and deployed responsibly, they could perpetuate bias and discrimination, resulting in inadequate care for certain populations.
Therefore, it's important for organizations to establish governance practices that ensure AI is used responsibly and ethically. This includes establishing clear guidelines for data privacy, ensuring diverse and representative data sets are used, and implementing transparency measures that allow for meaningful human oversight of AI decision-making. By doing so, organizations can help ensure that their use of AI aligns with societal values, legal requirements, and human well-being.
Are organizations implementing responsible AI?
According to a joint study by MIT Sloan and Boston Consulting Group, while nearly all companies recognized the importance of Responsible AI (RAI), only one in four had fully developed RAI programs. However, organizations with better RAI programs were more successful in introducing models with assurance.
The demand for Responsible AI and its associated business function has increased due to market demand, technological advancements, and urgent global needs for reliable solutions.
Investing in responsible AI is likely to yield an improved return on investment, given growing expectations from consumers, regulators, investors, and society for businesses to conduct themselves responsibly. When given the chance, business leaders tend to make the right decision.
What is AI governance?
AI governance is the set of processes and policies that organizations use to ensure that their use of artificial intelligence (AI) is responsible, ethical, and aligned with their values and goals. It encompasses a range of activities, including developing ethical principles and guidelines for AI use, establishing procedures for monitoring and auditing AI systems, ensuring transparency and accountability in decision-making, and addressing issues related to data privacy, security, and bias.
Effective AI governance requires collaboration between various stakeholders, including business leaders, data scientists, policymakers, and the wider public. By establishing clear guidelines and best practices for AI use, organizations can help ensure that their use of AI aligns with societal values, legal requirements, and human well-being. This can help to promote trust in AI and ensure that it is used to achieve positive outcomes for all stakeholders involved.
Implementing AI governance
Rather than being daunted by the idea of AI governance, it simply involves setting and maintaining high standards for your ML and AI models. Governance should be a priority for all businesses, with no excuses for failing to adhere to expected corporate behavior standards.
Implementing enterprise-level AI governance requires applying traditional business management principles to this newer technology. This includes strategizing around the risks and opportunities of AI and understanding how it can impact your business, customers, and society.
Similar to other business practices, effective AI governance requires a combination of people, structures, and processes. Good governance includes creating and managing policies effectively, promoting cross-functional integration and collaboration, and providing oversight and reporting. The important thing to note is that AI entrusts business decisions to an automated system, so the focus becomes how to effectively and responsibly regulate it.
How is AI governance different from GRC?
AI governance and GRC (Governance, Risk, and Compliance) are related concepts, but they have some key differences.
AI governance focuses specifically on the management of artificial intelligence models and technologies within an organization. It involves developing policies, procedures, and controls to ensure that AI is used responsibly, ethically, and effectively.
On the other hand, GRC is a broader framework that encompasses all aspects of corporate governance, risk management, and compliance. It involves managing risks related to financial reporting, regulatory compliance, and other operational areas. While AI governance is a subset of GRC, it focuses specifically on the governance of AI technologies.
Another difference between AI governance and GRC is that AI governance is a relatively new concept, while GRC has been around for some time. As AI continues to gain importance in organizations, AI governance is becoming an increasingly important aspect of GRC.
What are the prerequisites of AI governance?
There are several prerequisites for effective AI governance:
Clear understanding of AI: Before implementing AI governance, it is essential to have a clear understanding of what AI is, how it works, and its potential applications and limitations. This will help organizations identify potential risks and develop appropriate policies and controls.
Data quality and management: High-quality data is essential for accurate and reliable AI models. Therefore, organizations must have effective data management processes in place, including data acquisition, cleaning, and storage.
Risk assessment: Organizations must conduct a comprehensive risk assessment to identify potential risks associated with AI and develop appropriate controls to mitigate those risks.
Policies and procedures: Developing clear policies and procedures for AI development, deployment, and monitoring is essential. These policies should address issues such as transparency, accountability, bias, and ethical considerations.
Governance structure: An effective governance structure is crucial for overseeing AI governance. This structure should include a cross-functional team of experts, including data scientists, IT professionals, legal experts, and business leaders.
Monitoring and reporting: Regular monitoring and reporting are essential to ensure that AI models are operating as intended and to identify any issues or risks that may arise. Organizations must establish effective monitoring and reporting processes to enable continuous improvement of AI models.
By addressing these prerequisites, organizations can establish effective AI governance that promotes responsible and ethical AI use while minimizing potential risks.
What are the benefits of AI governance?
AI governance has several potential benefits, including:
Mitigating risks: AI governance frameworks can help identify and mitigate potential risks associated with the use of AI, including risks related to privacy, security, and bias.
Ensuring accountability and transparency: AI governance frameworks promote accountability and transparency in AI development and use, which can help build trust with stakeholders and reduce the potential for negative impacts on society.
Promoting ethical considerations: AI governance frameworks can help ensure that AI is developed and used in an ethical manner, taking into account considerations such as fairness, accountability, and privacy.
Improving decision-making: AI governance frameworks can help ensure that AI systems are developed and used in a way that improves decision-making and delivers value to organizations and society.
Enhancing innovation: By promoting responsible and ethical AI development and use, AI governance frameworks can help enhance innovation in AI technology and applications.
Meeting regulatory requirements: AI governance frameworks can help organizations meet regulatory requirements related to AI use, ensuring compliance and avoiding potential legal and financial risks.
Towards a future of responsible AI
Towards a future of responsible AI, organizations must prioritize the development of AI governance frameworks that address issues such as bias, privacy, and algorithmic transparency. They must also involve a diverse range of stakeholders in the development of these frameworks, including data scientists, IT professionals, legal experts, business leaders, and civil society organizations.
Responsible AI also requires ongoing monitoring and evaluation to ensure that AI systems are operating as intended and that any issues or risks are identified and addressed promptly. This requires a culture of continuous learning and improvement, as well as a willingness to adapt and evolve as new ethical considerations emerge.
Ultimately, the future of responsible AI depends on the actions of individuals, organizations, and governments around the world. By prioritizing transparency, accountability, fairness, and ethical considerations in the development and deployment of AI systems, we can harness the potential of this powerful technology while minimizing potential risks and promoting a more just and equitable society.
At 6clicks, our platform constantly evolves to keep up with the changing needs of organizations to manage governance, risk, and compliance. Take a tour of 6clicks to see how we bring all governance activities to a single platform.