In today’s rapidly evolving AI landscape, organizations face the dual challenge of harnessing the potential of artificial intelligence while ensuring responsible and ethical practices.
Karma Advisory’s Responsible AI Policy Framework is designed to help organizations navigate this complexity. By establishing a structured, three-pillar approach grounded in transparency, fairness, and governance,
we empower clients to mitigate risks, foster trust, and maintain a competitive edge in their industries.
The Problem: Challenges in AI Governance
Artificial Intelligence technologies, including machine learning, large language models, and predictive analytics, are powerful tools that offer significant opportunities. However, they also pose risks:
Bias and Discrimination: AI systems may unintentionally reinforce biases, leading to unfair outcomes.
Privacy and Security Risks: Sensitive data can be exposed or misused without proper safeguards.
Lack of Accountability: Without clear governance structures, organizations may struggle to ensure ethical oversight.
Regulatory Uncertainty: The fast-changing regulatory landscape demands policies that are adaptable and forward-looking.
Karma Advisory’s Solution: The Three-Pillar Framework
Karma Advisory’s Responsible AI Framework is built around three core pillars that provide a robust foundation for AI governance:
1. Data Governance
Data Quality Assurance: Ensuring the accuracy, relevance, and integrity of data used in AI systems.
Data Collection Practices: Adopting responsible practices for data collection, including user consent and compliance with GDPR, HIPAA, and other regulations.
Data Lifecycle Management: Implementing protocols for retention, archiving, and deletion to minimize risks and ensure compliance.
Data Lineage and Traceability: Tracking data origins, transformations, and usage for greater accountability.
2. Algorithmic Transparency and Fairness
Transparency: Designing systems that make AI processes understandable and accessible to all stakeholders.
Fairness: Mitigating biases through rigorous testing.
Ethical AI Design: Incorporating fairness checks and ethical reviews at every stage of the AI lifecycle.
3. Governance and Oversight
AI Governance Policies: Establishing structured roles and responsibilities for AI oversight.
Cross-Functional Oversight Committee: Engaging diverse teams to ensure holistic governance.
Ethical Review Governance: Providing independent assessments of AI projects to address ethical considerations.
Continuous Improvement: Regularly reviewing and adapting policies to align with technological advancements and evolving regulations.
Guiding Principles: A Foundation for Responsible AI
Our framework is underpinned by six guiding principles:
Transparency: Ensuring decisions and processes are understandable.
Accountability: Assigning clear roles and responsibilities for ethical oversight.
Fairness: Preventing discrimination by designing inclusive AI systems.
Privacy and Security: Protecting data through robust safeguards and compliance.
Sustainability: Minimizing environmental impact with sustainable AI practices.
Continuous Learning: Evolving systems and policies to keep pace with innovation.
How the Framework Works
Discovery Phase: Assess the organization’s current AI use and identify risks.
Framework Design: Develop customized governance structures tailored to the organization’s needs.
Implementation: Deploy policies, train teams, and establish oversight committees.
Monitoring and Improvement: Continuously track AI performance, review policies, and refine systems based on feedback and advancements.
The Karma Advisory Advantage
Our approach goes beyond policy creation:
Tailored Solutions: Policies customized to your unique operational needs and strategic goals.
Expertise Across Domains: Deep understanding of AI, ethics, and regulatory landscapes.
Ongoing Support: Long-term guidance to ensure compliance and ethical alignment.
Proven Results: See our Success Stories to understand our real-world impact of our work.
Take The Next Step: Let’s have a conversation.
In an era where responsible AI adoption is critical, Karma Advisory offers a proven framework to help organizations balance innovation with governance.
Take the first step towards building trust and mitigating risks—Contact us today to learn how our Responsible AI Policy Framework can transform your organization’s approach to AI.
Is your company’s website running on an outdated Content Management System (CMS)? Do you need to upgrade your instance of Sitecore, WordPress or Drupal; or, move from one CMS to another? Are you considering upgrading to a more modern, feature-rich platform? In this blog post, we’ll walk you through the key steps involved in a successful CMS upgrade project, from pre-work and kick-off to final readout and planning.
1. Pre-Work and Kick-Off
The first step in any CMS upgrade project is to hold a kick-off meeting with all stakeholders. During this meeting, you’ll review the project background, objectives, plan, and proposed deliverables. This is also an opportunity to set expectations and establish communication channels for the duration of the project.
Key Deliverables:
Kick-Off Deck
Detailed Discovery and Planning Plan
Communication/Status Updates
Review with the GH Team
It’s also important to review any existing website-related documentation, such as sitemaps and technical specifications, to ensure everyone is on the same page.
2. Current-State Review
Before you can plan for the future, you need to understand your current CMS setup. This involves reviewing current-state website features and user flows, as well as taking an inventory of existing features and prioritizing them based on importance (e.g., must-have vs. nice-to-have).
Key Activities:
Review current-state content and information architecture
Analyze page templates, site design, and site navigation/organization
Create an inventory of high-level changes and critical decisions
Conduct an infrastructure and integrations review, including data structure, development environments, security considerations, and third-party integrations
Deliverables:
Summary of Current-State and Future-State Considerations
Key Required Decisions
Infrastructure and Integrations Review
3. Future-State Considerations
With a clear understanding of your current CMS, you can start planning for the future. This involves documenting high-level future-state user flows for prospective customers, existing customers, and admin users (e.g., publishing workflow, user management).
Key Activities:
Document high-level requirements for reports, subscriptions, data services, payments, access and permissions, and compliance considerations
Detail requirements for specific features
Review brand guidelines and gather future-state design considerations (e.g., desktop/mobile, accessibility, language support)
Document future-state structure and design requirements/needs
Deliverables:
User Flows and User Journeys
Requirements Compilation Report with Prioritization
Design Audit Report
Brand Guidelines Review
Future State User Experience and Design Requirements
4. Migration Review
Migrating from one CMS to another can be a complex process. It’s important to determine high-level technical and data considerations, such as export capabilities, file types, data integrity, and data clean-up. You’ll also need to consider whether a custom migration tool is needed for importing into the new CMS.
Key Activities:
Identify essential features that need to be retained and find equivalent plugins or custom solutions in the new CMS
Determine business and security considerations, such as PEN testing
Plan for testing and cutover
Deliverables:
Feature Retention Analysis
Summary of Migration and Cutover Considerations
Business Requirements Documents (where applicable)
5. Final Readout and Plan
The final step is to develop a comprehensive readout of findings and a future-state plan. This will serve as a roadmap for the actual CMS upgrade project, ensuring all stakeholders are aligned on the goals, timeline, and deliverables.
Call to Action
If your company is considering upgrading its CMS, we’d love to help. Our team of experts has extensive experience in CMS migrations and can guide you through the entire process, from pre-work and kick-off to final readout and planning. Contact us today to schedule a consultation and take the first step towards a more modern, feature-rich website.
One of the challenges we face are the need to innovate and transform our institutions to be more dynamic and regenerative. Whether it be the WHO, World Bank, UN, or a government agency, etc. — these institutions were made for a world that was far more stable; they were designed in a world where rapid pace of technological was not a part of the discussion.
This podcast discusses the concepts of the commons of the future, the idea of welfare state in the future, and ideas around new industrialist. Hilary Cottam and Azheem Azhar have an intense and dynamic conversation.
One of the key ideas from Hilary, we must move from Homo econonomicus to Sapiens integra:
– “Homo econonomicus — guided by ration to maximize economic gains”
– “Sapiens integra — a new theoretical human with stronger connections to nature and other humans”
The concept of data portability is all too often simplified to the question “Can we have our data in a machine readable format?”
At best, this ensures the data can be loaded to a new system and at worst the data can be added to a data warehouse.
Palantir, Peter Thiels company, provides a lens into the importance of “thinking from the end” with systems — i.e. what happens what the system potentially reaches obsolescence or you simply want to leave it for whatever reason.
Can you get the data out?
And, can you output the analysis in machine readable format to continue the work in another system?
I assume most companies will not hand over queries as they could claim they are proprietary.
However, this complexity, clear provides the importance of creating in-house capabilities for analytics, and building in the end of contract clauses and what they actually mean into contracts. 
The department has created a new system to replace Palantir, and it wants to transfer the analysis generated by Palantir’s software to the new system. But Palantir, the NYPD claims, has not produced the full analysis in a standardized format — one that would work with the new software — despite multiple requests from the police department in recent months.
The primer is great, and a quick read. Here is my quick summary below:
The basics of deep learning is to think about how the brain breaks up a specific task. For example, let’s say you are hiking the Appalachian Trail, and you see something in the distance running towards you. First, you might notice it is moving. Then, you might notice what shape it is. Then, you might notice how fast it is going. Then, you might notice a big snout. Then, your brain will determine that this is an animal.
The process would continue until your brain evaluated, classified and predicts what object it is seeing. The joy of the mental exercise (for me) is to understand how the human mind works to break down ideas.
Inputs > Algorithm > Prediction > Training:
The following are the key concepts for thinking about deep learning concepts. Yes, this is overly simplified, but it is still a helpful start.
Inputs: Labels/Images
Algorithm:
Levels of Abstraction 1: Is this a shape?
Level of Abstraction 2: Is this shape an ear?
Level of Abstraction 3: Is this a cat?
Prediction = Yes or No. Is this prediction correct?
Current-State of Deep Learning:
Supervised Deep Learning: In effect, this is attempting to clone human behavior via labeled images, video, text or speech.
Reinforcement Learning: This is where the model attempts to “learn” behaviors, codify those behaviors (i.e. what does that mean), and then implement strategies to optimize based on those strategies. As the article suggests, the following are some examples:
E-Commerce: model learns customer behaviors and tailors service to suit customer interests.
Finance: model learns market behavior and generates trading strategies.
Robots: model learns how physical world behaves (through video) and then navigates that world.
Network Architecture to Detect Objects in Images:
Input: Image
Extract Feature: Extract the specific features
Classification: Classify based on the probability of those features
