In this engaging conversation, Mohamed Abusaid, an advocate for AI governance, and Mara Pometti, a design director at McKinsey, dive into the ethical intricacies of AI technology. They discuss the crucial need for governance programs to navigate safety challenges and risk management. The duo emphasizes transforming organizations from passive AI users to proactive creators, highlighting the balance between open-source models and managed services. Their insights reveal how responsible AI can enhance customer trust while navigating regulatory landscapes.
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Quick takeaways
AI governance programs are essential for ensuring safe and ethical technology use while managing potential risks and compliance.
Organizations interact with AI as takers, shapers, and makers, highlighting different levels of engagement and resource requirements in AI adoption.
Balancing privacy concerns with convenience in AI usage necessitates robust governance frameworks to maintain user trust and comply with evolving regulations.
Deep dives
Understanding AI Maturity Levels
Organizations interact with AI in three distinct ways: takers, shapers, and makers. Takers are users who subscribe to services like OpenAI, using them straightforwardly with prompts without needing extensive customization or their own data. Shapers, on the other hand, enhance existing services by adding their context or data, often employing strategies like knowledge embeddings to create more sophisticated solutions. Finally, makers represent the most complex level, where organizations build their custom AI models from scratch, which requires considerable resources and expertise.
The Shift Towards Treating AI as Software
There is an ongoing discussion about treating AI more like traditional software development, particularly in how organizations manage and deploy large language models (LLMs). Incorporating MLOps principles is essential, requiring practices like prompt versioning and rigorous testing of model outputs to ensure reliability and quality over time. By establishing a development framework similar to software engineering, organizations can smoothly transition from basic usage to more complex implementations that leverage their own data. This shift emphasizes the importance of treating LLM projects with the same rigorous standards as software development to optimize outcomes.
The Role of Open Source in AI Development
Open source is emerging as a critical component in AI, providing transparency and safety in model usage. Open source models allow for peer review and community contributions, which can help identify and mitigate potential biases or unintended consequences. However, the definition of 'open source' itself is evolving, with many companies imposing restrictions on commercial uses of their models. The emergence of high-quality open source options gives organizations more autonomy and flexibility to develop tailored solutions without being solely dependent on proprietary services.
Navigating Privacy and Regulation Challenges
Organizations are increasingly faced with balancing privacy concerns against the desire for convenience when it comes to using AI technologies. While many companies recognize the importance of safeguarding user data, there are still gaps in understanding how to implement effective governance frameworks that protect against misuse. Regulations related to AI are maturing and becoming more stringent, pushing companies to adopt best practices for data handling and compliance. As AI adoption grows, the need for comprehensive privacy strategies will become critical to maintain user trust and comply with legal expectations.
The Importance of Education in AI Adoption
Education is a vital factor in the successful adoption of AI within organizations, impacting how employees understand and utilize new technologies. Comprehensive training programs help staff navigate safe and effective AI use while fostering innovation in workflow processes. Leaders must prioritize building a culture of learning around AI capabilities to maximize the potential benefits while minimizing risks. As companies embrace AI, the emphasis on education will ensure that employees are informed about both opportunities and limitations associated with the technology.
MLOps Coffee Sessions #177 with Mohamed Abusaid and Mara Pometti, Building in Production Human-centred GenAI Solutions sponsored by QuantumBlack, AI by McKinsey.
// Abstract
Trust is paramount in the adoption of new technologies, especially in the realm of education. Mohamed and Mara shed light on the importance of AI governance programs and establishing AI governance boards to ensure safe and ethical use of technology while managing associated risks. They discuss the impact on customers, potential risks, and mitigation strategies that organizations must consider to protect their brand reputation and comply with regulations.
// Bio
Mara Pometti
Mara is an Associate Design Director at McKinsey & Company, where she helps organisations drive AI adoption through human-centered methods. She defines herself as a data-savvy humanist. Her practice spans across AI, data journalism, and design with the overarching objective of finding the strategic intersection between AI models and human intents to implement responsible AI systems that move organisations forward. Previously, she led the AI Strategy practice at IBM, where she also developed the company’s first-ever data storytelling program. Yet, by background, she is a data journalist. She worked as a data journalist for agencies and newsrooms like Aljazeera. Mara lectured at many universities about how to humanize AI, including the London School of Economics. Her books and writing explore how to weave a humanistic approach to AI development.
Mohamed Abusaid
Am Mohamed, a tech enthusiast, hacker, avid traveler, and foodie all rolled into one individual. Built his first website when he was 9 and fell in love with computers and the internet ever since. Graduated with computer science from university although dabbled in electrical, electronic, and network engineering before that. When he's not reading up on the latest tech conversations and products on Hacker News, Mohamed spends his time traveling to new destinations and exploring their cuisine and culture.
Mohamed works with different companies helping them tackle challenges in developing, deploying, and scaling their analytics to reach its potential. Some topics he's enthusiastic about include MLOps, DataOps, GenerativeAI, Product thinking, and building cross-functional teams to deliver user-first products.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
QuantumBlack, AI by McKinsey: https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients
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Connect with Mohamed on LinkedIn: https://www.linkedin.com/in/mabusaid/
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