Lovet Ndialle

• standardized AI-related terminology and risk management concepts across the organization by creating a comprehensive glossary. This initiative improved cross-departmental collaboration by 30%, ensuring clear and consistent communication among stakeholders and significantly reducing miscommunication incidents.
• Compiled and managed a detailed inventory of all organizational models, categorizing them by complexity and risk levels.
• I successfully developed and enforced a comprehensive AI governance framework that focused on the ethical, transparent, and responsible use of AI technologies. By integrating key components from various AI principles and frameworks—including the NIST AI Framework, Singapore's AI Governance Framework, the EU AI Act, UNESCO's AI Ethical Principles, Trustworthy AI guidelines, and the White House AI Bill of Rights—I achieved a 25% improvement in compliance with industry regulations and standards.
• I spearheaded the development and implementation of comprehensive internal AI review structures. By collaborating with cross-functional teams, incorporating industry best practices, and staying ahead of regulatory changes, we proactively addressed the challenges posed by rapidly evolving AI technologies, ensuring rigorous compliance with emerging standards.
• I successfully defined clear roles and responsibilities within our organization by utilizing a RACI matrix. Through strategic collaboration with key stakeholders, I ensured accountability at every level, driving our AI initiatives with clarity and precision.
• I established the Responsible AI Governance Community of Practice (RAIGCP), a biweekly forum that educated our organization on AI advancements, new regulations, and ethical standards such as the EU AI Act and the MIT Risk Catalogue. This initiative significantly heightened organizational awareness and compliance, fostering a culture of responsible AI use.
• I conducted regular, in-depth risk assessments to align organizational policies with evolving AI regulations and industry standards, worked closely with legal professionals to interpret new laws and AI liability derivatives, ensuring the company remained compliant and avoided fines and penalties.
• Actively engaged key stakeholders from the outset to identify common objectives, fostering strong relationships and a sense of ownership and commitment to AI governance. I developed PowerPoint presentations to educate the organization on AI-related risks and simplified complex topics for easier understanding. Additionally, I distributed monthly newsletters highlighting trends in the AI ecosystem. These initiatives enhanced stakeholder alignment, increased awareness and understanding of AI risks, and kept the organization informed on the latest AI developments.
• collaborated with cybersecurity, security, and other cross-functional teams to identify and classify risks associated with our AI systems. Utilizing a probability/severity harms matrix, we created a repository and assigned risk owners, categorizing risks as prohibitive, high, minimal, or limited. Implementing a risk mitigation hierarchy, along with the EU AI Act and MIT risk classification models, we successfully reduced critical risks by 30% through targeted mitigation strategies.
• Successfully led my team in comprehensive education on biases, the importance of conducting Data Impact Assessments, and adherence to GDPR regulations, ensuring high-quality data for training models. I also advised on developing a robust incident response strategy and implemented a shutdown protocol for our AI systems to manage potential issues effectively.
• spearheaded initiatives to ensure our AI systems were used ethically and as intended. Collaborating with the red teaming group, we rigorously tested and fortified our systems to ensure robustness and mitigate bias. Additionally, I implemented stringent human oversight protocols to maintain ethical standards and accountability. This was done to safeguard trust and uphold our commitment to responsible AI deployment.
• Established an effective communication channel to gather feedback on AI-related incidents and issues. This initiative ensured proactive engagement with stakeholders and experts, facilitating timely and informed resolutions. This strategy was implemented to enhance transparency, mitigate risks, and foster trust in AI deployment.

• Managed a project, to create trustworthy, reliable, and human-in-the-loop AI systems using machine learning to create real-time, intelligent, automated customer experiences such as informing customers about unusual charges and answering their questions in real-time. I worked with Generative AI/ML Engineers, and AI Researchers, with skills in machine learning platforms, public cloud infrastructure, LLMs, FMs, and programming languages like Python, Go, Scala, and Java.
• Key Transferable Skills for a Responsible AI Governance Role
• Oversaw the development and implementation of monitoring protocols to track deviations, inaccuracies, irregular model decisions, and data drift. By ensuring all these monitoring facets were in place, mitigated potential performance degradation over time, maintaining high model reliability and accuracy.
• Spearheaded rigorous development and documentation processes for AI models in compliance with SR 11-7 and OCC guidance.
• Work with the engineers to develop and implement communication protocols within the AI model design phase to notify consumers of AI involvement in transactions, resulting in enhanced consumer awareness and trust, as evidenced by positive feedback on customer satisfaction
• Ensured thorough evaluations were conducted to validate if AI models met desired performance criteria, data quality standards, and functional requirements before deployment. This involved rigorous testing and addressing any identified issues, leading to a seamless production deployment. Enforced Agile methodology to boost team productivity and ensure effective collaboration between researchers and Ml/Data engineers to establish and enforce AI safety protocols.
• Implemented labeling protocols and conducted training sessions to ensure 100% compliance with FTC transparency guidelines for AI models and projects, enhancing consumer trust and regulatory approval.
• Proactively identify and remove obstacles to ensure smooth team progression and project continuity to create and implement AI systems, maintaining a focus on trust and reliability standards.
• Facilitated cross-team dependency meetings maintaining clear and consistent communication with stakeholders, and the team in defining the business problems, requirement gathering, ensuring alignment, and managing expectations effectively.
• Advocated for the adoption of best practices in coding, monitoring, and security to ensure robust and reliable AI solutions.
• Establish a comprehensive stakeholder engagement and communication plan to keep all relevant parties informed about project milestones, progress, achievements, expected impacts, challenges, and changes associated with the AI implementation. Ensure regular updates and open channels for feedback to address concerns, foster collaboration and ensure strategic alignment
• Facilitated daily standup, sprint planning, sprint retrospectives, and sprint review sessions to meet project objectives and timelines.
• Educate my team on the need to build trustworthy AI Systems by ensuring the development of AI systems with built-in human-in-the-loop oversight mechanisms to ensure responsible use and continuous human vetting, aligning with best practices in AI governance and accountability.
• Successfully led the integration of the Software Development Lifecycle (SDLC), Scaled Agile principles, and AI Development Lifecycle in the development of an AI system, ensuring timely delivery and continuous improvement through iterative feedback and cross-functional team collaboration.
• Improved Model Exploitability by Ensuring and advocating for the implementation of feature engineering techniques that prioritized fairness, privacy, and reliability in AI models. This initiative made models more transparent and easier to interpret, thereby increasing stakeholder trust and allowing for more informed decision-making while complying with regulatory standards.
• Led the deployment of AI models from development environments to operational settings by organizing the necessary resources and technical expertise.
• Ensured the team Developed and enforced technical standards for AI usage within the organization by collaborating with technical teams to create comprehensive guidelines for AI model development and deployment, regularly monitoring adherence, and ensuring high performance and ethical use while minimizing risks of bias and inefficiency.
• Developed and implemented rigorous data governance policies aligned with Fair Information Practices (FIP) principles, and ensured regular audits and training sessions were conducted, to consistently maintain high standards of data protection and user privacy, earning industry recognition for responsible data management.
• Ensured the team integrated contestability features within our Model designs by implementing an automated SMS system that informed customers immediately when transactions were flagged and declined, offering them the choice to confirm or decline the transaction, block their card, and request a new one, which increased customer trust and improved the bank's fraud detection accuracy.

Was the project manager of the Personalized Cancer Treatment Predictive Modeling project, to enhance personalized treatment plans for cancer patients, improve clinical decision-making, and optimize resource allocation in healthcare settings. I worked with data scientists, machine learning engineers, software engineers, and data engineers, utilizing technologies such as Pandas, SQL, Amazon Redshift for data processing and feature engineering, Jupyter Notebooks for model development, Git for version control, and Docker and AWS for deployment

Transferable Skills for Responsible AI Governance Role

• Managed EMRs with strict HIPAA adherence, ensuring data integrity and privacy, resulting in 100% compliance during audits and reinforcing stakeholder trust.
• Implemented GDPR protocols to anticipate and mitigate risks associated with data handling. My strategic approach ensured that data impact access assessments were routinely conducted, leading to a transparent, auditable process that safeguarded user privacy and upheld regulatory standards.
• Ensured the team employed advanced privacy-enhancing techniques (PETs) such as anonymization, encryption, and pseudonymization to safeguard sensitive data and maintain user trust.
• Emphasized the importance of routine maintenance to address the complex environments AI systems operate within. Implemented monitoring systems to detect and prevent model drift, ensuring the model’s performance remained optimal and relevant throughout its lifecycle.

Managed a team that developed a lending tool that leveraged machine learning to assess the creditworthiness of loan applicants. The primary technologies used included Python for data analysis and model building, TensorFlow for machine learning, and AWS for deployment. The team comprised data engineers who handled data preprocessing and infrastructure, machine learning engineers who developed and trained the predictive models, and software engineers who integrated the models into a user-friendly application. The goal was to create an automated, accurate, and scalable system to streamline loan approval processes and reduce default rates:

Key Transferable Skills for Responsible AI Governance Role

• Facilitated collaboration of a diverse team of AI researchers, data scientists, software engineers, ethical AI specialists, and senior stakeholders by running requirement gathering sessions, Daily stand, sprint planning sprint reviews, and sprint retrospectives.
• Led and managed multiple AI oversight initiatives from inception to deployment, ensuring alignment with ethical standards and organizational goals.
• Ensured qualitative research study on AI red teaming to identify vulnerabilities, command ensured safer AI development practices.
• Collaborated in developing and deploying a novel algorithm audit tool, empowering users to assess the fairness and transparency of AI models.
• Led organizational responses to national inquiries on AI governance, influencing policy-making and advocating for ethical standards in AI development.

• Scrum Master/Project Manager for the Digital Engagement project, which integrated client data from external sources into the company's IDH system and then to downstream systems like CRM and SIMs. I also led the Client Data Analytics (CDA) Data Mastering project, centralizing sales data to reduce costs, speed up data availability, simplify management, and ensure consistency across applications like GSAM. These projects enhanced insights and client interactions, provided faster data access, and delivered significant cost savings. Our team of Data Engineers and Analysts utilized AWS, Snowflake, SQL, Python, Power BI, Business Objects, and Azure.

Key Transferable Skills for Responsible AI Governance Role

• Established rigorous data quality assurance protocols, reducing data-related errors by 40% and ensuring compliance with AI governance frameworks, thereby safeguarding ethical and responsible data usage.
• Spearheaded the integration of multi-source data into a unified platform, improving data governance processes and reducing inconsistencies by 30%, while leading a team of 15 data professionals to enhance data insights using tools such as AWS, Snowflake, SQL, Python, and Power BI.
• Directed a multidisciplinary team of 10+ Data Engineers and Analysts, fostering cross-functional collaboration that accelerated project delivery times by 20%, and consistently met project milestones within budget and scope through strong leadership and clear communication.