"Getting the ingredients right from the start. 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'.
The secret to 'Responsible AI'?
Hi, I'm Ankit Bhargava.
Responsible AI Ethics Specialist for Trustworthy AI Compliance.
I Am Proficient In
EU AI Act
NIST AI RMF
OECD AI Principles
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, 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.
We Can Work Together On:
Understanding Compliance and Meeting Your Obligations.
What does compliance expect?
Identifying your role & obligations.
Where to start?
What are the best practices for it?
How you demonstrate them?
03
Embedding Ethical Principles in AI Lifecycle.
To integrate ethics into every phase of your innovation, from planning and development to deployment and monitoring.
I Can Help You With:
Service 01
Building AI Ethics for Fairness, Transparency & Accountability
Moving beyond compliance into true ethical operations. I help you implement human-centric values into your technical workflows to prevent bias and ensure your AI systems serve everyone fairly and transparently.
01.
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.
02
Aligning Ethics with Fundamental Rights.
To ensure your AI systems respect human rights, safeguard dignity, and promote equality in every decision they make.
05
Creating Policies to Manage Third Party Risk, & end-to-end Accountability.
To establish clear policies that hold third parties accountable, ensuring security and integrity at every stage.
04
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.
06
Applying Privacy-Preserving and Privacy-Enhancing Techniques.
To protect your users' personal data and maintain trust by embedding methods that safeguard privacy without compromising functionality.
07
Developing Algorithmic Transparency and Explainability (XAI) Practices.
To make an understandable and transparent technology so your users and stakeholders can trust how decisions are made and why.
08
And More...
Let's connect, and we'll discuss this...
03
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.
Service 02
Implementing Responsible AI Governance and Risk Management.
A comprehensive approach to ensuring your AI initiatives are built on a foundation of safety and accountability. We focus on identifying potential risks early and building systems to manage them perpetually through the entire lifecycle.
01.
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.
02
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.
05
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.
04
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.
06
Identifying and Classifying 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.
07
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.
08
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.
09
Embedding 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.
11
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.
10
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.
12
And More...
Let's connect, and we'll discuss this...
FAQs: Data Privacy Compliance.
Step By Step.
Answer:
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Here is exactly how we work together. Step by step.
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Step 1 — Identify what applies to your AI system.
We start by looking at your specific AI. What does it do? Who uses it? Where are your users located? That tells us which laws and ethical standards matter — EU AI Act, NIST AI RMF, OECD principles, or others. We also look at your risk level. High-risk? Limited risk? Minimal? At the end of this step, you know exactly what rules you are playing with.
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Step 2 — Design a realistic roadmap together.
Once we know what applies, we build a plan that fits your budget and your available resources. Not a 50-page dream document. A one-page, step-by-step roadmap. Week one you do this. Month one you aim for that. We skip what can wait. We focus only on what keeps your AI safe, fair, and legal. You tell me your budget. I tell you what is possible. We adjust until it makes sense for you.
Step 3 — Pick one obligation at a time, starting with what needs first.
You do not need to fix everything at once. We pick the most urgent obligation — the one that keeps you up at night or the one regulators care about most. We focus only on that one. Nothing else. And I will need your proactive support simultaneously. That means you stay engaged. You answer my questions quickly. You review my drafts promptly. Together, we move faster than you ever could alone.
Step 4 — Create and maintain your AI compliance records together.
We document every AI ethics initiative we build. Risk assessments. Conformity declarations. Technical files. Governance policies. All of it organized in one place. This becomes your AI compliance record — exactly what regulators expect to see. You keep it updated. I help you maintain it. So when a user asks "how does your AI make decisions?" or a regulator says "show us your conformity assessment," you are ready. No panic. No last-minute scrambling.
That is our process. Complete documentation. You stay compliant. You stay in control. And you can prove it to anyone who asks.
Q1:
How do I start my responsible AI ethics journey with you?
Answer:
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Here is exactly what I work with and how I use them.
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1 — EU AI Act.
This is Europe's AI law. It sorts AI systems into risk levels — unacceptable, high, limited, or minimal. If you sell AI into Europe or build AI that affects Europeans, this matters to you. I help you figure out where your AI fits and what you need to do about it.
2 — NIST AI RMF.
This is the US approach. NIST stands for National Institute of Standards and Technology. It is not a law — it is a practical guide. It helps you manage AI risks without losing your mind. Very flexible. Very hands-on. I use it to build your governance framework and risk assessments.
3 — OECD AI Principles.
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These are five core values that most countries agree on — inclusive growth, human-centred values, transparency, robustness, and accountability.
The EU AI Act and many other laws are built on top of these principles. I use them as your ethical foundation.
I translate everything into plain action.
I do not hand you academic documents or legal walls of text. I take these three frameworks, pull out only what applies to your specific AI, and give you clear steps. One obligation at a time. No confusion. No wasted work.
That is how I work with frameworks. As tools to keep your AI safe, fair, and legal.
Q2:
What AI compliance frameworks do you work with?
Answer:
Yes. Here is how I help you classify your AI under the EU AI Act risk levels.
1 — We look at what your AI does.
I ask you four questions:
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What is the purpose of your AI system?
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Who or what does it affect? (Users, employees, applicants, patients?)
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What happens if your AI makes a mistake?
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Is your AI listed in the EU AI Act's high-risk use cases?
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2 — I map your AI to one of four risk levels.
Risk Level: Unacceptable risk
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What It Means: Banned. Cannot be used in the EU.
Examples: Social scoring by governments, real-time biometric surveillance in public spaces.
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Risk Level:: High-risk
What It Means: Strict requirements. Must comply or face fines.
Examples: CV screening, credit scoring, medical diagnosis, critical infrastructure, access to education or benefits
Risk Level: Limited risk
What It Means: Transparency obligations only. Tell users they are interacting with AI.
Examples: Chatbots, deepfakes (disclosed), emotion recognition (non-workplace)
Risk Level: Minimal risk
What It Means: No specific obligations. Voluntary codes of conduct.
Examples: AI-powered spam filters, video game NPCs, recommendation engines
3 — If you are high-risk, I tell you what that means.
You will need:
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A risk management system
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Training data governance (bias checks, representativeness)
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Compliance/Technical documentation (conformity assessment)
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Transparency (user information)
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Human oversight
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Accuracy, robustness, and cybersecurity measures
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Post-market monitoring
4 — I give you a clear classification statement.
You get a one-page document that says: "Your AI system is [risk level] under the EU AI Act. Here is why. Here is what you need to do next."