I approach my work with integrity, accountability, and consistency. My professional conduct is guided by honesty, sound judgment, and adherence to principles that build long-term trust.
I value clear, timely, and professional communication. I aim to keep clients informed through structured updates, reliable coordination, and honest discussion of progress, risks, and priorities.
I am committed to conducting my work with integrity, professional discipline, and adherence to applicable laws, regulations, and internationally recognized standards. My approach is grounded in respect for human rights, the rule of law, and responsible conduct across client engagements, business practices, and professional decision-making.
I maintain a zero-tolerance approach to corruption, discrimination, exploitation, slavery, servitude, forced or compulsory labour, and human trafficking. I do not knowingly support work, relationships, or practices that are inconsistent with human dignity, fundamental freedoms, or the principles of lawful and ethical conduct.
I support the responsible, lawful, and ethical use of artificial intelligence in business and consulting services. Where AI is used to assist research, analysis, drafting, automation, or operational efficiency, its use must remain consistent with professional judgment, confidentiality, data protection, human rights, fairness, and applicable legal and regulatory standards.
AI is a support tool, not a substitute for accountability. I maintain human oversight over material outputs, exercise professional review before relying on AI-assisted work, and reject uses of AI that enable discrimination, exploitation, deception, unsafe decision-making, or unlawful or unethical conduct.
As part of my commitment to responsible and sustainable practices, I strive to design and recommend solutions with environmental impact in mind, including efficiency, resource usage, and long-term sustainability.
The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand is projected to account for 4.2-6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4-6 Denmark or half of the United Kingdom. This is concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI. (source: Cornell University)