cleanFiles/bookmap_Putting_AI_Ethics_into_Practice_at_SAP_en-US_learning-sap-com_lsc-prod.zip (MMCP-DROPZONE) - 2024-11-07T08:50:40.000Z

Integrating our Policy in the AI Lifecycle: AI Ethics Operationalization

Lesson Content

Putting AI Ethics into Practice at SAP

<div>Learn how SAP integrates AI ethics into the development lifecycle of AI systems, outlining key roles and responsibilities in ensuring ethical practices. Discover the business AI process and the importance of AI ethics policy compliance.<br><br># This description was auto-generated #</div>

SAP_MOOC_aie2_Lesson_8_LIFECYCLE_Video.mp4

Welcome to the lesson integrating our policy in the AI lifecycle, AI Ethics operationalization. My name is Bettina Laugwitz, I'm the global lead of AI Ethics here at SAP and I'm your host for this lesson. You have heard about the AI Ethics policy and the use case perspective of AI Ethics at SAP and I would like to share now a perspective on how AI ethics and addressing related requirements is integrated in the development of AI systems at SAP. Let's have a look at SAP's business AI process. This is a process that defines the development, deployment and operations of AI in the context of SAP systems. systems. And it consists of five stages and follows an iterative approach. And I would like to go the different phases now just to line out a little bit what is the focus. So the first phase is the ideation phase where the use case is being identified and a specification is done based on domain and AI expertise. In the second phase, the validation phase, the feasibility of the AI functions is being assessed. In the third phase, the realization phase, the development of AI function takes place. And in the fourth phase, the productization, these functions and capabilities are integrated into the business application. The fifth phase, the operations phase, focuses on managing and optimizing the deployed AI systems. Throughout this process, different experts and roles and stakeholders are involved and need to ensure that AI ethics requirements are being addressed. And they have certain tasks and responsibilities. And I would like to go through a number of those roles now in a bit more detail, starting with the AI use case owner, who is coordinating the end-to-end development process, starting with the specification, and continuing through delivery and also including the operations phase. And you can see here on the slide, There is this process activity, text, and a number of dots here that represent the different phases that I've been talking about on the last slide. And you can see here the AI use case owner is active in each of those five phases, which is indicated by these colored dots. The second role is the AI data scientist who needs to find for the respective business problem the right AI methods to develop them, implement them or just implying them to derive solutions that can be translated into AI functions. The AI engineer is then putting this AI functionality into the product together with the product developer who is coming in in the productization phase which you can also see here indicated by the dots. Another role is the designer role who is bringing a human centered perspective on the use case and on the functionality to the process, doing research in the beginning and throughout the process, ensuring the relevance and also the usability of the AI system for the end user. The designer is working closely together with the user assistance developer, who is in charge of ensuring that everything that has to do with text around the application is done in a way that is understandable and is useful. This includes user interface text, messages, conversations, and also content for product documentation. So the user assistant developer is playing a key role in the aspect of transparency of AI systems. The AI or LLM operations engineer is operating the AI enabled products, or also the individual AI functions and manages lifecycle aspects like version updates. And last but not least we have the customer who is representing the user of the AI enabled product specifically in the beginning to ensure that the planned AI system is relevant and of course that the operation of the AI system and the deployment works as desired and designed. So this was a lot of information already. We put this together, information about the development process, but also about the assessment process and the specific roles into a handbook, the SAP AI Ethics Handbook, which is available for all employees and also for everybody who is interested in how SAP is putting AI ethics into practice. So here are the key takeaways for this lesson. Throughout the business AI process, SAP employees must be aware of the AI ethics policy framework and follow its requirements. The business AI process assigns different responsibilities to different personas. And to support the understanding and putting the requirements into practice, SAP has created an AI ethics handbook. This concludes our lesson, and it also marks the end of this SAP learning MOOC. We hope you have enjoyed the learning content and gained a better understanding of AI ethics at SAP. I would like to thank all colleagues who contributed to this course. And of course, we thank you for taking the time to learn about this important topic of ethical AI.

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