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Non Maleficence

The principle of non-maleficence is an adaptation of the principle of "do no harm" in the Hippocratic oath. This adaptation is necessary because harm is unavoidable in many circumstances; the surgeon must sometimes harm in order to heal, for example. Harm may occur in other professions as well; a teacher might punish, a researcher might violate privacy, a defence contractor might develop weapons.

So the principle of non-maleficience, as developed for example by Beauchamp & Childress (1992) means "avoiding anything which is unnecessarily or unjustifiably harmful" (and) whether the level of harm is proportionate to the good it might achieve and whether there are other procedures that might achieve the same result without causing as much harm" (Ethics Centre, 2017). The principle arguably also requires consideration of what the subject considers to be harm because as Englehardt (1993) says, we engage one another as moral strangers who need to negotiate moral arrangements (Erlanger, 2002).

The definition of maleficence to be avoided can be variably broad. For example, the AMA (2001) addresses not only the nature and priority of patient care, but also "respect for law, respect of a patient's rights, including confidences and privacy." The AMA's Declaration of Professional Responsibility also advocates "a commitment to respect human life" which includes a provision to "refrain from crimes against humanity" (Riddick, 2003).

The principle of non-maleficence is found in numerous ethical codes, and not only medical ethics. For example, the Association for Computing Machinery (2018) states "an essential aim of computing professionals is to minimize negative consequences of computing, including threats to health, safety, personal security, and privacy,\x94 including \x93examples of harm include unjustified physical or mental injury, unjustified destruction or disclosure of information, and unjustified damage to property, reputation, and the environment" (ACM, 2018).

Non-maleficence in research and data science includes being minimally intrusive (Drew, 2016), to keep data secure (ibid; also Raden, 2019: 9), to promote "resilience to attack and security, fall back plan and general safety, accuracy, reliability and reproducibility" including respect for privacy, quality and integrity of data, and access to data (AI HLEG, 2019). AI systems, says Fjeld (2020) should perform as intended and be secure from compromise (also Drachsler & Greller, 2016).

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