AI brings unprecedented opportunities to businesses, but also incredible responsibilities. The results of AI systems have a real impact on people's lives, raising serious questions related to AI ethics, data governance, trust and legitimacy. The more decisions a business puts in the hands of AI, the more it takes on significant risks such as reputation, employment/HR, data privacy, health and safety issues. However, according to a global study, 88% of respondents do not trust AI-based solutions. So how do you learn to trust AI? Responsible AI is the practice of designing, developing, and deploying AI with good intentions to empower employees and businesses, and impact customers and society fairly, enabling companies to build trust and confidently scale AI. With the help of Responsible AI, you can formulate key goals and develop a management strategy, creating systems that allow AI and your business to thrive. Minimize Unintended Bias Make sure your AI is responsible for ensuring that the algorithms and underlying data are as objective and representative as possible.
Make AI transparent To build trust between employees and customers, develop explanatory AI that is transparent to processes and functions. Create opportunities for employees Empower your business to voice doubts or concerns about AI systems and effectively manage technology without stifling innovation. Data privacy and security protection Use a privacy and security oriented approach to ensure that personal and/or sensitive data is never used in an unethical manner. Benefit customers and markets By building an ethical foundation for AI, you can mitigate risk and create systems that benefit your shareholders, employees, and society as a whole. Enabling robust AI Principles and management Define and articulate the mission and principles for responsible AI, and create a transparent organizational governance structure that builds confidence and trust in AI technologies. Risk, policy and control Strengthening compliance with existing laws and regulations while monitoring future ones, developing policies to mitigate risks and implementing these policies through a risk management system with regular reporting and monitoring. Technologies and tools Develop tools and methods to support principles such as fairness, explainability, trustworthiness, traceability, and privacy, and build them into your AI systems and platforms.
Culture and learning Empower management to make responsible AI a mission-critical business requirement and require training so that all employees are clear about the principles and success criteria for responsible AI. Determine AI bias before scaling Algorithmic assessment is a technical assessment that helps you identify and mitigate potential risks and unintended consequences of AI systems in your business to build trust and build support systems for AI decision making. Use cases are prioritized first to ensure that you evaluate and fix those that have the greatest risk and impact. Once priorities have been identified, they are evaluated using our algorithmic assessment, which includes a series of qualitative and quantitative checks to support the various stages of AI development. The assessment consists of four main steps: Set goals around your fairness goals for the system, taking into account different end users. Measure and discover differences in potential outcomes and sources of bias across different users or groups. Mitigate any unintended consequences by using the suggested recovery strategies.
Monitor and control systems with processes that flag and resolve future inconsistencies as the AI system evolves. .