Research has demonstrated that algorithmic and AI (AAI) technologies produce significant social harms. This short intervention turns the focus from social harm production to social harm reduction and seeks to map how and what AAI technology features affect harm reduction efforts by AAI system designers.

The intervention argues that six cumulative features are relevant. The technological agency embedded in AAI systems makes conscious harm reduction by design possible but also potentially strips previous human harm safeguards. Complexity destabilises designers’ capability to predict and control system performance, justify outcomes, and analyse and ensure the legitimacy system ontologies. Uninterpretability further complicates harm-reduction efforts by making analytical tracing of system logics difficult and introducing alien ontologies. Non-linear performance further destabilises outcome prediction capabilities, while indeterminacy and dynamicity provide the ultimate challenges for harm reduction. If the AAI systems performance patterns are indeterminate in nature or prone to change during use due to lifelong learning, designers lose direct control over the systems. While the technology features challenge harm reduction, concerted efforts in managing and containing their effects allow designers to engage in harm reduction.