Online Reporter
Dr Evans Sagomba
Everything AI
WE are witnessing a frantic reimagining of what it means to think, to know, and to be.
Public figures and lead engineers proclaim that a digital brain is only a matter of scale and computation away from being indistinguishable from us.
That rhetoric demands rebuttal, not because it underestimates technical possibility, but because it mistakes equivalence for continuity.
Zimbabwe needs to hear a clear, academically grounded case that humans and machines are categorically different in ways that matter for policy, for social institutions, and for how we live a life worth living.
The categorical divide between biological minds and digital systems
At the level of functional output, advanced algorithms may soon outperform humans across many tasks. They will solve complex equations more quickly, translate between languages flawlessly, generate convincing creative artefacts, and optimise systems with superhuman efficiency. These achievements matter. They will multiply productivity and reshape entire industries. Yet performance parity or superiority on tasks does not dissolve the ontological differences that separate a biological, embodied mind from an engineered information processor.
Humanness is not merely a bundle of information-processing routines. It is an emergent constellation of embodiment, temporality, affect, vulnerability, and narrative self hood. Embodiment means having a body that ages, aches, heals, and dies. Temporality means living with a finite horizon that shapes priorities, commitments, and moral concerns. Affect means feeling attraction, shame, grief, joy, and a host of other states that colour perception and reasoning.
Narrative selfhood means telling a story about the self, situated within family, community, and history. Digital systems do not share this lived architecture. They may simulate expressions of grief or craft plausible first-person narratives; simulation is not the same as the lived interiority of mortality, dependency, and care.
This matters for how Zimbabwe designs institutions and regulations.
If policy treats AI as a substitute for human relations of care, stewardship, or governance, it will be disastrously mistaken.
AI can assist, complement, and amplify human capacities. It cannot be the repository of trust where trust must arise from reciprocity, shared sacrifice, and mutual vulnerability.
Why meaning and social bonds resist automation
Meaning is socially constructed and emotionally anchored. Human meaning-making relies on shared histories, rituals, and the bodily cues that scaffold trust. A digital assistant can compute a funeral liturgy, suggest consolatory words, and moderate ritual timing with clinical precision. None of this replicates the moral weight of being present as one human holds another’s hand while a life is mourned. The gestures that matter are not performative outputs; they are relational acts bound to human fragility.
Belonging is similarly rooted. Membership in families, neighbourhoods, and political communities relies on obligations, reciprocal labour, and recognition that persists through failure and forgiveness. AI-mediated interactions risk producing brittle forms of recognition: personalised content that mirrors preferences, not moral obligation. When a system “knows” me because it predicts my clicks, it does not thereby owe me anything. In contrast, a neighbour who tends a sick child incurs obligations that outlast algorithmic optimisation.
For Zimbabwe, a country where social capital and communal networks are often the scaffolding of resilience, policy must protect spaces where human relations cannot be offshored to automated systems. Public services that require empathy, discretion, and moral judgement must remain human-led. Where AI is introduced, it should be explicitly framed as a tool to strengthen human relationships rather than a surrogate for them.
Moral agency and responsibility
If we accept that machines can simulate decisions without sharing the moral ecology that grounds them, then responsibility becomes a central axis of policy. Moral agency presupposes more than action; it presupposes an ability to understand and be held answerable in human terms. Machines cannot bear moral blame, repentance, or the burdens of social sanction in the way persons do. They lack the lived continuity and social embeddedness that make moral education, rehabilitation, and accountability meaningful.
Consequently, the delegation of morally significant decisions to inscrutable models is not merely a technical risk; it is an ethical abdication. When an algorithm decides welfare eligibility, parole release, or medical triage without transparent human oversight, the state abdicates its duty to treat citizens as moral persons, not statistical objects. Zimbabwean regulators must insist on institutional arrangements that allocate final responsibility to identifiable human agents who are publicly accountable. This avoids the convenient fiction that “the algorithm decided” and preserves the possibility of remedial justice when errors occur.
Epistemic trust, truth, and the fragility of human judgment
Another dimension that resists automation is epistemic trust. Human societies organise knowledge through procedures of testimony, adjudication, and criticism. Experts gain authority not merely by output accuracy but by situated credibility grounded in training, peer correction, and institutional checks. AI outputs that appear authoritative can distort these epistemic practices by creating the illusion of infallibility. Citizens may defer to machine-generated “truths” because they are confident, fast, or rhetorically persuasive, not because they have been vetted through social epistemic norms.
Zimbabwe faces the specific risk of epistemic capture where algorithmic pronouncements become de facto policy without deliberative validation. To prevent this, public institutions must institutionalise processes that treat algorithmic outputs as provisional evidence subject to public reasoning. Parliamentary oversight, independent audit mechanisms, and participatory deliberations can reclaim epistemic authority from black-box systems. The goal is not to slow useful innovation but to anchor new tools in practices that sustain critical scrutiny and public confidence.
Cultural narratives, identity,
and the limits of simulation
Culture is a web of practices, symbols, and shared memories. It is transmitted intergenerationally through embodied practices, songs sung at dusk, hands passing on craft, and elders telling stories. Machines can reproduce cultural forms at scale; they cannot inherit them. The difference matters because authenticity and legitimacy in cultural life depend on continuity of human authorship and custodianship.
For Zimbabwean policy, this implies caution in outsourcing cultural curation to platforms whose incentives are commercial and external. Preservation of indigenous knowledge, local languages, and plural narratives requires human stewardship. Where AI is used for archiving or linguistic revitalisation, it should operate under community governance, with explicit protections against appropriation and commodification. Culture must be a matter of civic formation, not merely data to be monetised.
Designing policy for human flourishing, not just efficiency
Adoption strategies for AI should be evaluated by how they contribute to human flourishing, not only by productivity metrics. A schooling system that uses AI to standardise curricula may raise test scores but degrade the relational apprenticeship central to civic formation. A healthcare system that substitutes diagnostic chatbots for nurses might reduce costs yet hollow out the trust that underpins therapeutic relationships.
Therefore, policy appraisal frameworks should include qualitative indicators: preservation of relational labour, maintenance of civic trust, and enhancement of communal resilience. Regulatory cost–benefit analyses must broaden to include social capital measures and dignity-related outcomes. Incentivising technologies that augment human capacities in caring, teaching, and political deliberation will align innovation with Zimbabwe’s social strengths.
Institutional capacities and capability building
To implement these principles, Zimbabwe must build institutional capability. That entails training regulators who understand AI’s technical affordances and social consequences, funding interdisciplinary research that combines computer science with anthropology and ethics, and cultivating civic literacy so citizens can engage meaningfully in policy debates.
Practical capacity building means embedding ethicists and social scientists in procurement and deployment teams, creating civic observatories that monitor societal impacts, and supporting grassroots organisations that insist on community consent in data practices. These measures decentralise power and make governance resilient to both technological hype and corporate capture.
The promise and peril of advanced AI demand a posture of sober imagination. Zimbabwe can benefit from the efficiencies and insights that digital systems offer while steadfastly preserving the human domains that machines cannot occupy. By insisting on institutional accountability, data sovereignty, cultural custodianship, and policy metrics oriented to flourishing, Zimbabwe will not reject the digital age; it will shape it.
We must resist a metaphysics that equates simulation with being. Machines may think in useful ways and compute with breathtaking power. They will not live, love, bear children, die, or tell stories in ways that make sense to human lives. Keeping those distinctions clear is not a sentimental clinging to the past; it is a political and ethical necessity for a society that seeks to use tools in service of human dignity rather than to let tools remake the meaning of dignity itself.
The next four weeks, and we count down to the launch of my book, AI Ethics: Weaving Global Narratives with African Perspectives. The articles will focus on AI ethics.
About the author: Dr Evans Sagomba is a Doctor of Philosophy and Chartered Marketer (CMktr, FCIM) with an MPhil and PhD in Philosophy. He specialises in AI, Ethics, and Policy Research, and is an AI Governance and Policy Consultant. His expertise extends to Ethics of War and Peace, Philosophy of Development, and Political Philosophy. [email protected]. ORCID: 0009-0007-0681-0329; social media handles: LinkedIn; @ Dr. Evans Sagomba (MSc Marketing)(FCIM )(MPhil) (PhD)
