By Prof. Naomi Klineberg
When a human being commits a crime, societies debate whether rehabilitation is possible. Can the wrongdoer change? Should they be forgiven? Now consider a flawed algorithm: a bail recommendation system that unfairly penalizes minorities, or a hiring tool that weeds out women’s résumés. If the code is patched, if its “bias” corrected, do we grant it amnesty? Or does the stain of its past errors linger, shaping how we judge its future use?
The Weight of Digital Guilt
Humans are moral agents; algorithms, we are told, are not. Yet both leave consequences in their wake. A discriminatory model can ruin careers or cost lives, even if no intent is present. Do we treat these failures as accidents, akin to a bridge collapsing, or as culpable errors requiring accountability? The metaphor matters. One view encourages us to focus on repair; the other compels us to ask whether the system itself deserves to be decommissioned, no matter how “improved.”
Can Code Change?
When humans change, we demand evidence: therapy completed, years served, restitution made. What is the analog for code? A re-trained dataset? A more transparent auditing process? But here the analogy frays. Humans evolve in ways that are unpredictable; algorithms evolve only at the discretion of those who control them. Does that make their redemption easier—or more suspect?
The Problem of Collective Responsibility
Unlike a person, an algorithm is rarely the product of a single hand. It is written by engineers, trained on public data, deployed by institutions, and monitored (or not) by regulators. Who, then, holds the guilt? If we forgive “the algorithm,” do we risk absolving the humans behind it? Or if we condemn the system indefinitely, do we foreclose the possibility of useful reform?
Toward a Philosophy of Digital Forgiveness
Perhaps the point is not to forgive or forget, but to clarify the terms under which technologies earn trust again. This might mean fixed “sentences”—mandatory sunset clauses forcing periodic re-evaluation. It might mean permanent records of past harms, so that no algorithm returns unexamined. Or it might mean rejecting the human metaphor altogether, treating code as infrastructure to be rebuilt rather than as a soul to be redeemed.
Should algorithms be forgiven like humans? Or should we resist the temptation to humanize them at all? The answer is less important than the question itself. For in asking it, we confront the deeper reality: that our ethical frameworks for technology are still borrowed, still incomplete, and still searching for justice in a world increasingly governed by code.
