To guarantee the privacy and security of AI, it is essential to take the necessary steps to ensure reliability. A large US corporation partnered with IBM to automate its hiring processes while keeping its AI unbiased. The company wanted to ensure fairness by accelerating the identification of any biases in hiring, as well as understanding how AI models make decisions. To do this, they used IBM Watson Studio, an AI monitoring and management tool in Cloud Pak for Data.Generative AI has the potential to revolutionize the way companies interact with customers and drive business growth.
Ethical and social frameworks, such as AI ethical principles, AI HLEG guidelines, and FATML principles, can help ensure system reliability. A mature ethical AI practice puts its principles or values into practice through the responsible development and deployment of products, uniting disciplines such as product management, data science, engineering, privacy, legislation, user research, design and accessibility to mitigate potential harms and maximize the social benefits of AI. Organizations must establish a governance system that assigns responsibility to AI and allows faster and more informed decisions to be made. H&M Group, one of the largest fashion retailers in the world, uses artificial intelligence capabilities for its objectives of sustainability and supply chain optimization, as well as to offer a personalized customer experience. Companies must train generative AI tools using third-party data that customers proactively share and their own data, which they collect directly. To ensure reliability when using custom AI solutions, organizations should adhere to ethical and social frameworks such as AI ethical principles, AI HLEG guidelines, and FATML principles.
Additionally, they should create a governance system that assigns responsibility to AI and allows faster and more informed decisions to be made. Finally, companies must train generative AI tools using third-party data that customers proactively share and their own data. Organizations must also consider the implications of using custom AI solutions. They should be aware of potential risks such as data privacy issues or bias in decision-making. Companies should also consider how their custom AI solutions will interact with existing systems or processes.
Finally, organizations should ensure that their custom AI solutions are regularly tested for accuracy and reliability. By following these best practices for ensuring reliability with custom AI solutions, organizations can ensure that their systems are secure and reliable. This will help them maximize the potential benefits of using custom AI solutions while minimizing any potential risks.
