Short notes on analytics, ML, and governance. RSS →
A data governance framework tailored for humanitarian assistance organizations provides a structured approach to managing data assets effectively. We will explore how to build a comprehensive data governance framework step by step, ensuring it aligns with organizational goals and industry best practices.
Data quality can be categorized as either high, low, or poor. When speaking of ethical AI, we need to ensure that data quality is high. An obviously overlooked factor in creating ethical AI is the quality of data available in product development. The use of inaccurate, non-representative, and non-reliable data causes bias. The only way to have such data is when data quality management is not highly placed in the development process.
ML algorithms can adapt to new data as it becomes available, continuously improving pricing strategies to reflect the most recent market conditions. This results in pricing strategies that are more flexible, data-driven, and adaptable, providing a substantial improvement over static pricing models.
Intelligence Augmentation aims to enhance rather than replace human cognition. When applied thoughtfully, it could mitigate the cognitive and creative erosions identified by both studies.
It is possible to mitigate some of the risks associated with general-purpose AI- frameworks, policies, and approaches exist. However, we still have a long way to go before all the conceivable dangers are mitigated or managed effectively. As a result, there is a gap in research to come up with Beter and more effective methods to address risks in general-purpose AI, especially when the growth rate is as fast as it is.
Increased use of energy, water, and raw materials are AI advances, and a growth in computing use in general-purpose AI development is witnessed. For the necessary infrastructure to be set up, these environmental resources are being used on a large scale, which poses a risk in the already dilapidated environment today.
With the generative power of general-purpose AI, manipulative content can be generated to sway public opinion today. A simple prompt on a general-purpose AI can generate content at scale that can be used to manipulate political views. One mitigative approach is content watermarking, but this can be circumvented with a simple tool like cropping and image manipulation.
The principles of AI governance, which range from ethics to explainability, privacy, inclusivity, and safety, help propel the development of AI systems in ways that ensure they are fair, beneficial, and do not harm users. Let’s explore these principles today. Since we already mentioned coffee, why not make a cup while at it?
Data security, governance, management, and cloud technologies are critical components of managing large amounts of data in today’s digital world. However, these areas also come with their own set of challenges. This post will explore some of the common challenges in these areas and discuss potential solutions.