AI 1.pdf

IBM auditing Ms. Prakriti SJCC July 2024 1 IBM Auditing: IBM audits refer to the process by which IBM (International Business Machines Corporation), a major multinational technology and consulting compa...

IBM auditing Ms. Prakriti SJCC July 2024 1 IBM Auditing: IBM audits refer to the process by which IBM (International Business Machines Corporation), a major multinational technology and consulting company, as- sesses and verifies the compliance of its software licenses and contracts with its customers. These audits are also known as IBM Software License Reviews or IBM License Compliance Verification (LCV). 2 What is the Purpose of IBM Audits? The primary purpose of IBM audits is to ensure that customers are using IBM software products in compliance with the terms and conditions outlined in their software licenses and contracts. This includes verifying that the customer has not exceeded the number of licenses purchased, is using the software for the intended purposes, and is not engaged in any unauthorized or unlicensed usage. It’s essential for organizations using IBM software to be aware of their con- tractual obligations and maintain compliance to avoid unexpected costs and penalties associated with non-compliance during an IBM audit. Engaging with IBM in a cooperative and transparent manner during the audit process can help facilitate a smoother resolution if compliance issues are identified. 3 Types of Auditing: 3.1 Compliance Auditing: Organizations may audit their AI systems to ensure they are in compliance with relevant regulations, industry standards, and internal policies. This is especially important in sectors like healthcare, finance, and legal services where strict compliance is necessary. 3.2 Performance Auditing: Auditing can involve assessing the performance of AI systems to ensure they meet expected accuracy, reliability, and efficiency standards. This may include 1 tracking metrics, evaluating model performance, and optimizing algorithms. 3.3 Bias and Fairness Auditing: To address concerns about bias and fairness in AI systems, organizations may conduct audits to identify and mitigate biases in data and algorithms. This helps ensure that AI systems provide equitable results for all user groups. 3.4 Security Auditing: Auditing AI systems for security vulnerabilities and threats is essential to pro- tect against data breaches and cyberattacks. This involves assessing the security measures in place to protect AI models and data. 3.5 Data Privacy Auditing: AI systems often process sensitive data. Auditing for data privacy compliance ensures that AI applications handle data in a manner consistent with privacy regulations (e.g., GDPR(General Data Protection Regulation), CCPA(Central Consumer Protection Authority). 3.6 Usage and Cost Auditing: For AI deployed in cloud environments or as part of subscription services, orga- nizations may audit usage and costs to ensure cost-effective utilization and to prevent unexpected expenses. 3.7 Ethical Auditing: Some organizations are increasingly focusing on ethical auditing of AI systems to ensure that the technology aligns with ethical principles and societal values. The specific tools and processes used for auditing AI systems, including those built with IBM Watson, can vary depending on the organization’s needs and objectives. These audits are often performed by a combination of data scientists, compliance officers, security experts, and domain specialists. 4 Top 5 Speech Recognition Softwares: Speech recognition, also known as Automatic Speech Recognition (ASR) or voice recognition, is a technology that allows a computer system or software to convert spoken language into written text. It enables computers to understand and interpret human speech, making it a valuable tool for various applications. ASR technology is used to convert spoken language or audio speech into written text. It is commonly used in various applications, including transcription services, voice assistants, and more, where spoken words are transcribed into text for further processing or analysis. 2 4.1 Dragon NaturallySpeaking (Dragon Professional): Developed by Nuance Communications, Dragon NaturallySpeaking has a long history and is recognized for its accuracy and robust features in speech recognition. It is popular among professionals and individuals for tasks like transcription and voice-controlled computer operations. 4.2 Microsoft Speech Recognition: Microsoft’s built-in speech recognition software is widely known because it comes pre-installed with Windows operating systems. Users can use it for dictation, controlling their computer, and interacting with applications. 4.3 Google Speech Recognition: Google’s speech recognition technology is integrated into various products and services, making it widely recognized. It’s commonly used in Google Docs for voice typing and for voice commands through Google Assistant. 4.4 Amazon Transcribe : Part of Amazon Web Services (AWS), Amazon Transcribe is a recognized and reliable solution for converting audio into text. It’s used in various applications, including transcription services, content indexing, and analytics. 4.5 Apple Dictation: Apple’s built-in dictation and speech recognition features on macOS and iOS devices are well-known among Apple users. They are used for voice-to-text input and controlling Apple devices through voice commands. Conclusion: These software options are known for their accessibility, func- tionality, and integration with popular operating systems and services. Keep in mind that the landscape of speech recognition technology can change over time, so it’s a good idea to check for updates and reviews to ensure you’re using the most suitable and up-to-date software for your needs. 3