fairsthough

Fairsthough is a term used in ethics and governance to describe a procedural approach to fairness in decision-making processes. It focuses on whether the reasoning, documentation, and deliberative steps that lead to a decision are fair, in addition to evaluating the final outcome.

The word fairsthough is a portmanteau of fair and thought (or through), signaling fairness through deliberation. It emerged in late 2010s scholarly discussions of procedural justice in technology-assisted decision-making, particularly in debates about algorithmic governance and public administration.

In practice, fairsthough is defined by several criteria: transparency of the decision process; the availability of auditable reasoning and data; opportunities for affected parties to participate or challenge conclusions; and safeguards to prevent manipulation, bias, or opaque influence. Proponents advocate routine process audits and openly accessible documentation to support accountability.

Fairsthough is distinct from outcome-centric notions of fairness, placing importance on how decisions are produced rather than solely on their results. It is often used in conjunction with other fairness frameworks, such as distributive justice, to strengthen procedural legitimacy in AI policy, procurement, and adjudication.

Reception is mixed. Supporters praise its emphasis on legitimacy and accountability, while critics note definitional vagueness and measurement challenges. Practical implementation requires governance infrastructure, data stewardship, and independent oversight to avoid superficial transparency.

See also: Procedural justice, Algorithmic fairness, Transparency, Accountability.