Counterfactual Fairness in Machine Learning
Fairness in machine learning has been predominantly studied through global metrics like Demographic Parity and Equalized Odds. These approaches aim to ensure equity between groups
Innovation thrives at the intersection of technological advancement and ethical responsibility. At Esdha, we help organisations bridge this gap by embedding fairness, transparency, and accountability into their AI systems. The result? Sustainable AI adoption that builds trust with users and stakeholders while ensuring compliance with global regulations.
Gain expert guidance to seamlessly align with the EU AI Act, UK regulations, and NIST AI RMF, ensuring your AI systems meet ethical and technical compliance requirements without delays or penalties.
Build confidence among users, stakeholders, and regulators by integrating trust, ethics, and transparency into your AI systems, fostering stronger relationships and a positive brand reputation.
Adopt responsible AI practices that not only drive innovation but also mitigate risks, ensuring sustainable, ethical, and scalable AI adoption tailored to your business needs.
With our customised AI strategy service you set the foundation for leveraging AI’s potential and driving positive outcomes for your organisation.
Getting started with AI strategy depends on your organisation’s readiness and specific goals. However, having an AI strategy before implementation will provide you and your team with a clear vision and roadmap.
Even during the experimentation phase, an AI strategy provides clarity, focus, risk mitigation, scalability, stakeholder alignment, and a framework for learning and improvement. It helps you make the most of your AI experiments and sets the stage for successful AI implementation in the future.
While an AI strategy provides a high-level vision and roadmap for AI adoption across the organization, a business case is a more detailed analysis focused on a specific AI project or initiative, aiming to justify its investment and evaluate its feasibility and potential impact. Both are essential components for successful AI implementation, with the AI strategy guiding the overall direction and the business case supporting decision-making for individual projects.
An AI strategy typically includes the organisation’s vision for AI, goals and objectives, a roadmap for implementation, resource allocation plans, AI ethics and governance for addressing, and a plan for talent acquisition and upskilling.
We provide AI strategy both online and offline, tailoring the approach based on the specific needs and preferences of our clients. This ensures flexibility and accessibility, allowing organisations to engage in the AI strategy creation process in a way that best suits their requirements and circumstances.
An AI strategy should be a living document that is regularly reviewed and updated to adapt to evolving technologies, market trends, and organizational goals. It is recommended to review the AI strategy at least annually or when significant changes occur within the organization or the AI landscape.
Developing an AI strategy involves input from various stakeholders, including senior leadership, technology and data experts, business leaders, and relevant departments within the organisation.
An AI strategy can drive innovation, enhance operational efficiency, improve decision-making, and create new business opportunities. It ensures that AI initiatives are aligned with the organisation’s values, mitigate risks, and maximise the benefits of AI adoption.
Fairness in machine learning has been predominantly studied through global metrics like Demographic Parity and Equalized Odds. These approaches aim to ensure equity between groups
Fairness in healthcare machine learning is highly context-dependent. Different use cases, populations, and risks require tailored approaches to fairness, making it essential to align fairness
One concept that has gained significant attention in recent years is “Equalized Odds.” As AI and ML models continue to influence crucial decisions, it is
The growing need for fairness in machine learning models stems from the increasing reliance on AI for decisions that impact people’s lives, from hiring and