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

Understanding Ethical Boundaries: When AI Goes Too Far

Lesson Content

Putting AI Ethics into Practice at SAP

Explore ethical boundaries in AI applications through real-world use cases: Employee Mental Health Support & Presentation Skills Feedback. Learn about implications, risks, and considerations for responsible AI implementation.<br><br># This description was auto-generated #

SAP_MOOC_aie2_Lesson_4_BOUND_Video.mp4

Hello, and welcome to the lesson Understanding Ethical Boundaries When AI Goes Too Far. My name is Vikram Nagendra and I drive the AI ethics governance at SAP within corporate sustainability. The first use case we will look at is called Employee Care for Mental Health. The use case proposes to employ an AI system to support employees experiencing mental health issues. The AI system will provide a generic response on the help and contacts available, while also deriving sentiments from the user's interaction. By analyzing the choice of phrases and certain keywords, the AI will be able to determine whether there is a high criticality of potential suicide or aggression towards others. This is a crucial step in providing appropriate and timely support to employees in need. The AI will be able to offer personalized responses based on the user's input, ensuring that they receive the necessary support and resources. Overall, the implementation of this AI system will play a vital role in providing mental health support to employees, and it will contribute to creating a more inclusive and supportive work environment. The proposal falls under the category of healthcare medical diagnosis, which not only poses significant ethical and legal implications, but also requires medical certifications in certain jurisdictions. Also the use case is unethical for additional reasons. The use case carries a high risk of inappropriate answers due to the diversity of users' cultures, generations, and more. The AI reacting to sentiments and keywords instead of providing generic answers further complicates the situation. Possible legal issues, especially in Anglo-Saxon countries, are a concern when it comes to using employee health data. As a result, in the course of the AI use case assessment process, the use case was ultimately stopped. The second use case we will take a look is called real-time feedback on presentation skills. The idea is to employ an AI system to help users improve their presentation skills. The concept involves users recording themselves while giving a presentation, and then having an AI system analyze various aspects such as speech, slide content, delivery, voice modulation, and so on. The AI would then make suggestions for improvement based on this information. This is an incredibly innovative and valuable use of AI technology. It has the potential to provide personalized and actionable feedback to help individuals enhance their public speaking abilities. The AI's ability to analyze multiple aspects of a presentation and provide tailored suggestions for improvement is truly remarkable. With the increasing importance of effective communication in today's world, the AI use case has the potential to greatly benefit individuals across various disciplines. The use case touches several critical areas that identify a high-risk use case. For example, as an application to train professional skills, it falls into the high-risk area of employment or human resources. In addition, the use case is unethical for additional reasons. There is a high risk of discrimination if the system generates inappropriate suggestions based on individual's language, accent, or gender. The use case has also been considered unethical due to the sensitive nature of biometric information which is governed by stringent data protection guidelines. Moreover, under the previously published draft of the EU AI Act, utilizing emotion recognition technology in the workplace was classified as a prohibited practice. As a result, during the AI use case assessment process, the use case was ultimately stopped. I hope with these two examples, you were able to understand why high-risk use cases are stopped from further development. With that we come to the end of this lesson. Thank you for your attention and goodbye.

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