Humanizing AI Decision-Making: Why Ethics Must Come First

Artificial intelligence is increasingly shaping decisions that impact human lives—from healthcare and hiring to criminal justice and financial services. While AI promises efficiency and objectivity, it often falls short in a critical area: human understanding.

The question we must ask is not just whether AI can make better decisions, but whether it can make just, ethical, and human-centered ones.

The Limits of AI Rationality

Most AI systems optimize for predefined metrics—accuracy, efficiency, or cost reduction. However, human decision-making is rarely just about numbers. Ethical dilemmas, social contexts, and the nuances of individual circumstances often defy rigid algorithmic rules.

Case Study: AI in Healthcare—Who Deserves Care?

In 2019, researchers uncovered a racial bias in a U.S. healthcare algorithm used to allocate medical resources to over 200 million patients. The algorithm prioritized white patients over Black patients for extra care—not due to explicit racial parameters but because it relied on historical healthcare costs as a proxy for need (Obermeyer et al., 2019). Since Black patients historically spent less on healthcare (due to systemic disparities), the AI wrongly inferred they needed less care.

This illustrates a core flaw in AI rationality—if models optimize for cost rather than moral imperatives like fairness or justice, they inherit and amplify systemic inequities.

Related Theory: Rawls’ “Justice as Fairness”

John Rawls (1971) argued that fairness should be assessed from a “veil of ignorance”, where policies are made as if we don’t know our social position. AI models, in contrast, often reinforce pre-existing power dynamics, violating this principle.

Fairness vs. Context: The Challenge of Ethical AI

AI fairness is often reduced to mathematical formulas—such as equal error rates across demographics. But fairness in real life is more complex. Consider:

Example: AI in Criminal Justice—Can Algorithms Be Fair?

In 2016, a ProPublica investigation found that COMPAS, an AI used in U.S. courts to predict recidivism (the likelihood of reoffending), was twice as likely to falsely label Black defendants as “high risk” compared to white defendants (Angwin et al., 2016).

The model optimized for predictive accuracy, but ignored historical racial biases in arrest rates and policing. This created a feedback loop—since more Black individuals were flagged as “high risk,” they were more frequently incarcerated, reinforcing systemic injustice.

Related Theory: Foucault’s “Discipline and Punish”

Michel Foucault (1975) argued that institutions shape knowledge and power dynamics—in this case, AI systems don’t just reflect social biases but institutionalize them. AI, rather than being neutral, becomes a tool of systemic control.

Bridging the Gap: How to Humanize AI

So how can we ensure AI decision-making aligns with human values?

1. Embed Ethical Reasoning into AI Models

AI must be designed to weigh moral considerations, not just optimize metrics. This means incorporating ethical frameworks like virtue ethics, consequentialism, or deontology into model development.

Example: AI in Autonomous Vehicles

• If a self-driving car must choose between hitting one pedestrian vs. five, how should it decide?

• The MIT Moral Machine project (Awad et al., 2018) found that different cultures have different moral preferences—some prioritize young over old, while others emphasize lawfulness (e.g., avoiding jaywalkers).

• This highlights the need for ethical pluralism in AI design—no single ethical framework fits all societies.

2. Maintain Human Oversight in High-Stakes Decisions

AI should assist, not replace, human decision-making in areas like healthcare, law enforcement, and hiring.

Example: AI in Hiring—Who Gets the Job?

• Amazon’s AI-powered hiring tool (used between 2014–2017) automatically downgraded female applicants for technical roles because it was trained on past hiring data, which favored men (Dastin, 2018).

• When humans rely blindly on AI recommendations, biases become self-reinforcing. Human oversight is essential to prevent discriminatory automation.

3. Require Transparency and Explainability

If an AI denies someone a job, loan, or medical treatment, they should know why.

Example: AI in Finance—The Black Box Problem

• Many AI-driven credit scoring models reject applicants without explanation.

• ZestFinance developed an interpretable AI that explains why someone was denied credit—whether due to low savings, inconsistent income, or missed payments.

• Transparency builds trust and allows individuals to contest unjust decisions.

4. Prioritize Inclusive AI Development

AI teams must be diverse and interdisciplinary, including ethicists, social scientists, and affected communities.

Example: Google’s “Project Respect”

• Google modified its hate speech detection AI after realizing it disproportionately flagged Black and LGBTQ+ speech as offensive (Bender et al., 2021).

• Involving diverse perspectives helped refine the model, making it more culturally aware.

Conclusion: AI as a Tool, Not a Judge

The power of AI lies in its ability to process vast amounts of data, but wisdom requires more than computation. Decision-making—especially in healthcare, law, and hiring—demands context, ethics, and empathy.

AI should enhance human judgment, not replace it.

The future of AI isn’t just about making machines smarter—it’s about making them human-centered.

If AI is to serve humanity responsibly, we must ensure it aligns not just with efficiency, but with fairness, justice, and human dignity.

References

• Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks. ProPublica.

• Awad, E., Dsouza, S., Kim, R., et al. (2018). The Moral Machine experiment. Nature, 563(7729), 59-64.

• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of FAccT.

• Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.

• Foucault, M. (1975). Discipline and Punish: The Birth of the Prison.

• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

• Rawls, J. (1971). A Theory of Justice. Harvard University.