Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include an AI Risk Repository from MIT, an article on the essential analytics frameworks, and a case study on Scotiabank's AI initiatives. Follow us on Twitter and LinkedIn to receive daily updates on IIA content and curated content as it becomes available.
Featured Articles on Leadership and Culture
Do You Really Need a Chief AI Officer? (MIT Sloan Review)
Every organization is different. This article breaks down when you should, and more importantly when you should not, appoint a Chief AI Officer.
Analytics Frameworks Every Data Scientist Should Know (Towards Data Science)
This article reflects on the author's consulting experience at McKinsey and introduces essential analytics frameworks she utilized as a consultant like MECE, issue trees, hypothesis trees, and 2x2 matrices.
Research and Advisory Network (RAN)
How could you benefit from hundreds of C-suite level D&A executives at your fingertips? Get access to the leading network of independent analytics expertise, allowing you to apply real practitioner insights against initiatives, projects, and problems through RAN today.
Featured Articles on Analytics and AI Strategy
How Scotiabank Built an Ethical, Engaged AI Culture (MIT Sloan Management Review)
Scotiabank won two awards for its AI initiatives, recognized for its innovative chatbot and responsible AI practices. The chatbot's success is attributed to a collaborative approach, improving accuracy from 35% to 90%. The bank also focuses on managing unstructured data, essential for generative AI, and has embedded ethics in AI development with policies and education programs. This comprehensive strategy has positioned Scotiabank as a leader in AI, reflecting a significant cultural shift since 2021.
How to Improve LLM Responses With Better Sampling Parameters (Towards Data Science)
When calling the OpenAI API with the Python, have you ever wondered what exactly the temperature and top_p parameters do? This article discuses how you can tweak LLM parameters to your specific use case and garner better LLM results for your organization.
AI Risk Repository (MIT)
This resource from MIT has captured 700+ documented risks from existing frameworks and classifies how, when, and why the risks occur. Keep this repository bookmarked for:
- An accessible overview of the AI risk landscape
- A regularly updated source of information about new risks and research
- A common frame of reference for researchers, developers, businesses, evaluators, auditors, policymakers, and regulators
- A resource to help develop research, curricula, audits, and policy
- An easy way to find relevant risks and research
Featured Articles on Data
Ensure High-Quality Data Powers Your AI (Harvard Business Review)
AI does not need to fail on a global scale to cause enormous damage — to individuals, companies, and societies. Models frequently get things wrong, hallucinate, drift, and can collapse. Good AI comes from good data, but data quality is an enormous organization-wide issue (and opportunity), yet most companies have neglected it. Companies need to understand the nuances of the problem they’re trying to solve, get the data right (both by having the right data for that problem and by ensuring that the data is error-free), assign responsibility for data quality in the short term, and then push quality efforts upstream in the longer-term.
Unified Data: The Missing Piece to the AI Puzzle (Information Week)
This article tackles the difficulties of unifying data across an organization, namely how to handle data silos.