The secret to 'Responsible AI'?
"If you approach innovation like cooking, you’ll get what I mean."
Building ethical values into smart machines starts with defining their purpose and continues through deployment. (Like cooking), it’s about selecting the right components, adding them at the right time, and managing each step carefully. Skipping or overlapping stages can compromise the outcome, efforts, and, most importantly, 'your expectation.'.
Getting the ingredients right from the start.
The Value I Bring
I know how challenging it can be to start something new. Balancing your vision, originality, legal obligations, and compliance, especially when resources are tight, is no small feat. Here's why I focus on providing solutions tailored to your innovation, not just off-the-shelf options. I keep things simple and clear, free from heavy jargon, so you always know what is to be done, how it is done, and why it matters for your regulatory obligations.
So What I Offer:
Responsible AI Governance Consulting & Solutions.
To advise you on best practices for embedding human-centric values and a risk-based approach throughout your AI lifecycle and operations step by step. So that you don’t just comply with regulatory requirements but also demonstrate it to your users.
Ethical AI Consulting.
To assist you in complying with Trustworthy AI standards, build & prioritize fairness, transparency, accountability, and privacy in your innovation and technology.
Remote Freelance Support & Assistance.
To provide you with flexible, on-demand support in applying frameworks, tools, and standards tailored to your business objectives, obligations, and available resources. So that you have the right expertise on time, no matter where you’re located or what stage your business is in.
I Am Profound In:
OECD Principles & Guidelines.
To advise on building a system that works for people, not against them.
EU AI ACT.
To simplify what does the law expect?
GDPR, UK DPA of 2018, and PDPA.
To prioritize your users' integrity. confidentiality & rights.
NIST RISK MANAGEMENT FRAMEWORK.
To assist in applying real standards & practices.
We Can Work Together:
Understanding Compliance and Meeting Your Obligations.
Does AI regulation apply to you?
What does compliance expect?
Identifying your role & obligations.
Where to start?
What are the best practices for it?
How you demonstrate them?
I Can Help You With:
01
Implementing Responsible AI Governance and Risk Management.
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Performing AI Compliance & Conformity Assessments.
To check that your smart machine meets the necessary standards, ensuring it follows the rules and works as it should.
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Establishing Clear AI Accountability Structures for AI Systems.
To define who’s responsible for every part of your AI systems, making sure nothing is overlooked and everyone knows their role.
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Establishing Mechanisms for Human Oversight and Governance
To put safeguards in place so humans stay in control and in the loop, ensuring the technology works for people and not the other way around.
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And More...
Let's connect, and we'll discuss more...
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Conducting Comprehensive AI Risk and Impact Assessments (AIAs).
To evaluate how your invention affects people and society, reduce potential harms, and make sure your system stays responsible and fair.
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Identifing and Classifing Internal/External Risks and Contributing Factors.
To pinpoint risks, understand what causes them, and prioritize actions to address them before they turn into bigger problems.
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Embeding Privacy by Design and Security by Default in AI Systems.
To build a product or service that protects user data and ensures safety from the ground up, making trust and security an automatic feature.
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Developing Post-Market Monitoring Systems.
To keep an eye on your innovation after launch, spotting issues early and ensuring they continue to operate as intended.
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Designing & Establishing AI Governance Frameworks.
To establish a clear RASCI framework that defines roles, crafts policies, and sets standards to align privacy and governance practices for effective AI management.
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Developing Risk Strategy and Tolerance.
To create a clear plan for managing risks, deciding what’s worth taking, what’s not, and how to handle the unexpected along the way.
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Risk Categorization and Compliance.
To organize potential risks into clear categories and ensure your actions meet the rules, keeping both your innovation and trustworthiness intact.
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Preparing AI Project, Model and Datasets Inventories.
To document and organize all projects, models, and datasets so everything is transparent, trackable, and ready for effective management.
02
Bulding AI Ethics for Fairness, Transparency, & Accountability.
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Establishing Ethical Guidelines for AI Development and Use.
To set clear standards for creating and using AI responsibly, ensuring fairness, transparency, and accountability at every step.
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Aligning Ethics with Fundamental Rights.
To ensure your AI systems respect human rights, safeguard dignity, and promote equality in every decision they make.
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Embedding Ethical Principles in AI Lifecycle.
To integrate ethics into every phase of your innovation, from planning and development to deployment and monitoring.
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Promoting Human-Centric Values in AI Operations and Outputs.
To design a system that prioritizes people, ensuring their needs, values, and safety remain at the core of your operations.
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Creating Policies to Manage Third Party Risk, to ensure end-to-end Accountability.
To establish clear policies that hold third parties accountable, ensuring the security and integrity at every stage.
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Applying Privacy-Preserving and Privacy-Enhancing Techniques.
To protect user data and maintain trust by embedding methods that safeguard privacy without compromising functionality.
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Developing Algorithmic Transparency and Explainability (XAI) Practices.
To make an understandable and transparent technology, so users and stakeholders can trust how decisions are made and why.
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And More...
Let's connect, and we'll discuss more...
Ready to take the next step?
Let's put People First in your Data and Technology.
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FAQs About AI Governance & Ethics.
What are the key principles of AI governance and ethics?
AI governance is built on fairness, transparency, accountability, and privacy. Ethical AI should respect human rights, minimize bias, and ensure decisions are explainable. Strong governance prevents harm, builds trust, and keeps AI aligned with real-world values.
How can businesses ensure AI compliance with regulations like the EU AI Act?
Start by identifying your AI system’s risk category under the EU AI Act—some require stricter compliance. Implement transparency, bias detection, and human oversight to meet regulatory expectations. Regular audits and risk assessments will keep your AI both lawful and ethical.
What are the risks of unethical AI, and how can they be mitigated?
Unethical AI can lead to bias, discrimination, privacy violations, and even legal trouble. To prevent this, use diverse training data, test for biases, and ensure human oversight in decision-making. A strong AI governance framework helps keep your systems fair and responsible.
How can we start with AI governance?
The first thing you should do is define the model, project, and datasets. Start by identifying the key attributes associated with each component and document them thoroughly.
How to start building AI ethics?
The first step in building AI ethics is to dentify the key ethical principles that will guide your AI development and deployment, such as fairness, transparency, accountability, privacy, and inclusivity. Evaluate potential ethical risks associated with your AI projects, such as bias, discrimination, or unintended consequences.
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