Beyond Surface-Level Ethics: Advocating for Responsible and Human-Centered AI

Beyond Surface-Level Ethics: Advocating for Responsible and Human-Centered AI

While AI ethics initiatives often emphasize transparency and fairness, they can overlook how systems impact those historically excluded from development processes. This article calls for stronger commitments to responsible AI practices that protect well-being and encourage collaborative futures.

By Lucky Star, Responsible AI | Blockchain Educator & Consultant

In the fast-changing world of artificial intelligence, many well-meaning initiatives describe themselves as “ethical.” But closer examination reveals an uncomfortable truth: much of this work lacks a meaningful path to responsibility. Without concrete steps to reduce harm and extend benefit over time, these efforts risk reinforcing the very disparities they claim to address.

Too often, ethical frameworks focus on general values like transparency or fairness. While those values are important, they must be applied through design choices that reflect lived experiences—not just ideals. Voices historically sidelined from high-level decision-making often feel the consequences of these systems most acutely. A facial recognition tool that works poorly for people with deeper skin tones, for instance, is not merely a technical flaw—it is a failure of responsibility (Fosch-Villaronga & Poulsen, 2023).

Responsible AI means making deliberate, human-first choices at every stage of development. This includes:

  • Collaborative Development: Inviting input from people with a range of life experiences, especially those most affected by digital infrastructure.
  • Transparency in Review: Implementing formal checks and systems of redress when AI tools produce harmful outcomes (Radanliev et al., 2024).
  • Designing for the Long Term: Building platforms and tools that prioritize enduring usefulness, rather than just short-term efficiency or return on investment.

Responsible AI is not only about avoiding harm—it is about actively creating benefit.

Often, groups who have been left out of the digital AI renaissance face outsized impacts when AI systems fail. These same groups are rarely part of the training, testing, or oversight of those systems. When this happens, the result is not just a technical gap—it is a social fracture. Research continues to show that datasets and benchmarks, when built without care, reproduce limitations that map directly onto real-world harms (Kuhlman et al., 2020).

To address this:

  • Improve Dataset Quality: Use training data that more accurately reflects the real-world populations AI systems interact with.
  • Engage Beyond Traditional Centers: Make room for rural areas, overlooked regions, and low-access communities to take part in AI development and validation.
  • Allow for Cultural Adaptability: Design tools that honor different ethical priorities, respecting variations in how communities define safety, trust, or fairness.

Many AI efforts begin with sincere intentions. But intention without structure leads to drift. To move from idealism to accountability, we must shift our focus from ethics as theory to responsibility as a method.

By doing so, we build systems that reflect not just what is possible—but what is wise. As a practitioner working at the intersection of human well-being and digital infrastructure, I believe in tools that enable, not just perform. Responsible AI is how we make that future real—one design decision, one safeguard, and one act of listening at a time.

 

Sources & References:

Birhane, A., Ruane, E., Laurent, T., Brown, M. S., Flowers, J., Ventresque, A., & Dancy, C. L. (2022). The forgotten margins of AI ethics. arXiv. Retrieved from  https://arxiv.org/abs/2205.04221

Fosch-Villaronga, E., & Poulsen, A. (2023). AI and the quest for diversity and inclusion: A systematic literature review. AI and Ethics. Retrieved from https://link.springer.com/article/10.1007/s43681-023-00362-w

Huang, Y., Arora, C., Houng, W. C., Kanij, T., Madulgalla, A., & Grundy, J. (2025). Ethical concerns of generative AI and mitigation strategies: A systematic mapping study. arXiv. Retrieved from https://arxiv.org/abs/2502.00015

Kuhlman, C., Jackson, L., & Chunara, R. (2020). No computation without representation: Avoiding data and algorithm biases through diversity. arXiv. Retrieved from https://arxiv.org/abs/2002.11836

Radanliev, P., Santos, O., Brandon-Jones, A., & Joinson, A. (2024). Ethics and responsible AI deployment. Frontiers in Artificial Intelligence, 7. Retrieved from https://www.frontiersin.org/articles/10.3389/frai.2024.1377011/full

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