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How do you keep human judgment and ethical considerations central in AI-supported decision-making?
How do you keep human judgment and ethical considerations central in AI-supported decision-making?
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6 answers
Sumitra Biswal
Independent researcher in the intersection of AI and Cybersecurity
40
Answers
India
Updated
Sumitra’s Answer
Hi!
Thank you for this very important question. It is essential to keep human judgment and ethics at the center of AI-supported decision-making. Fully handing over decisions to AI can be risky, especially in safety-critical areas. That’s why many global standards explicitly require a “Human in the loop” rather than allowing fully autonomous systems.
From my experience, the key is to treat AI as a support tool, not the final authority. Always question its outputs, do additional research, and validate suggestions with expert knowledge and trusted sources before acting on them. This way, AI becomes an assistant that strengthens decisions, while human judgment ensures they remain responsible and ethical.
Warm regards,
Sumitra
Thank you for this very important question. It is essential to keep human judgment and ethics at the center of AI-supported decision-making. Fully handing over decisions to AI can be risky, especially in safety-critical areas. That’s why many global standards explicitly require a “Human in the loop” rather than allowing fully autonomous systems.
From my experience, the key is to treat AI as a support tool, not the final authority. Always question its outputs, do additional research, and validate suggestions with expert knowledge and trusted sources before acting on them. This way, AI becomes an assistant that strengthens decisions, while human judgment ensures they remain responsible and ethical.
Warm regards,
Sumitra
Charlotte Fields
Sales Director
2
Answers
Dublin, County Dublin
Updated
Charlotte’s Answer
I think there are a few important steps to follow:
1. Ensure Human Oversight – AI should inform decisions, not replace them. People remain accountable for outcomes, especially in high-stakes scenarios.
2. Apply Ethical Frameworks – Define clear principles for fairness, transparency, and accountability. Make it clear when and how AI influences decisions.
3. Prioritise Explainability – Use AI systems that provide understandable outputs so decisions can be reviewed and challenged.
4. Mitigate Bias – Continuously test for bias in data and models, and involve diverse experts in the review process.
5. Foster Responsible Use – Train people and teams to validate AI outputs and apply contextual judgment rather than accepting recommendations blindly.
1. Ensure Human Oversight – AI should inform decisions, not replace them. People remain accountable for outcomes, especially in high-stakes scenarios.
2. Apply Ethical Frameworks – Define clear principles for fairness, transparency, and accountability. Make it clear when and how AI influences decisions.
3. Prioritise Explainability – Use AI systems that provide understandable outputs so decisions can be reviewed and challenged.
4. Mitigate Bias – Continuously test for bias in data and models, and involve diverse experts in the review process.
5. Foster Responsible Use – Train people and teams to validate AI outputs and apply contextual judgment rather than accepting recommendations blindly.
Goodera Careervillage
63
Answers
Bengaluru, Karnataka, India
Updated
Goodera’s Answer
This is the "one life question", I think all innovation (including AI) will be fundamental to human life, however each one of us need to choose is one path, believes and guide lines. I see myself as a moderated person, how tries to always get the best from the innovations (AI as a major concern now) but keep make myself judgments based in life experience and research with the most fact checking as possible.
Goodera Careervillage
63
Answers
Bengaluru, Karnataka, India
Updated
Goodera’s Answer
You've identified the main issue with AI. To improve results, use ethically sourced data from diverse backgrounds. It's important to keep checking the outcomes of AI to ensure it works properly. Since AI lacks human judgment, we must stay alert and keep an eye on its actions, even if we're not directly controlling it.
Allan Thompson
Volunteer
17
Answers
San Jose, California
Updated
Allan’s Answer
Good answers above. Also, many of our professions have organizations with ethical requirements and guidelines. I'm an electrical engineer so IEEE is the organization in my case. If you're working on AI, you and your AI should follow those ethical requirements and guidelines as well as your own company's ethical requirements and guidelines. As the old saying goes, AI should not say anything that you would not say to your own spouse or child or mother in person face to face.
Sankarraj Subramani
GenAI Opensource Contributor and GenAI Sr. SDET
13
Answers
Sugar Land, Texas
Updated
Sankarraj’s Answer
For me, keeping human judgment and ethics at the center of AI-supported decision-making starts with a clear principle: AI should assist, but humans remain accountable. AI can process data faster, detect patterns, and simulate scenarios at scale, but it cannot fully understand human context, fairness, or long-term consequences. That’s where human oversight is essential.
In my work at United Airlines, for example, AI simulations predicted potential failures in Starlink in-flight Wi-Fi. While the AI models flagged risks, I and my team made the final calls after considering FAA safety requirements, passenger experience, and regulatory compliance. At Freddie Mac, AI-driven bias detection tools highlighted risks in mortgage approvals, but it was human judgment that ensured policies aligned with fairness and equitable access to credit.
I reinforce this balance by leaning on ethical frameworks I’ve studied through certifications like AI Governance and Compliance 2.0, Generative AI Governance, and AI-Driven Cybersecurity. These programs emphasize transparency, explainability, and accountability, and I put them into practice by asking:
Is the AI output explainable to a non-technical stakeholder?
Could this decision unintentionally disadvantage someone?
Does it align with compliance and ethical standards?
Finally, I embed ethics into team culture. I mentor engineers to not just trust AI blindly, but to challenge outputs, validate against human experience, and always consider the broader human impact. By doing this, we ensure that AI enhances decision-making but never replaces human responsibility, empathy, or ethical judgment.
In my work at United Airlines, for example, AI simulations predicted potential failures in Starlink in-flight Wi-Fi. While the AI models flagged risks, I and my team made the final calls after considering FAA safety requirements, passenger experience, and regulatory compliance. At Freddie Mac, AI-driven bias detection tools highlighted risks in mortgage approvals, but it was human judgment that ensured policies aligned with fairness and equitable access to credit.
I reinforce this balance by leaning on ethical frameworks I’ve studied through certifications like AI Governance and Compliance 2.0, Generative AI Governance, and AI-Driven Cybersecurity. These programs emphasize transparency, explainability, and accountability, and I put them into practice by asking:
Is the AI output explainable to a non-technical stakeholder?
Could this decision unintentionally disadvantage someone?
Does it align with compliance and ethical standards?
Finally, I embed ethics into team culture. I mentor engineers to not just trust AI blindly, but to challenge outputs, validate against human experience, and always consider the broader human impact. By doing this, we ensure that AI enhances decision-making but never replaces human responsibility, empathy, or ethical judgment.