We argue that an imperfect criminal law procedure cannot be group-fair, if 'group fairness' involves ensuring the same chances of acquittal or convictions to all innocent defendants independently of their morally arbitrary features. We show mathematically that only a perfect procedure (involving no mistake), a non-deterministic one, or a degenerate one (everyone or no one is convicted) can guarantee group fairness, in the general case. Following a recent proposal, we adopt a definition of group fairness, requiring that individuals who are equal in merit ought to have the same statistical chances of obtaining advantages and disadvantages, in a way that is statistically independent of any of their feature that does not count as merit. We explain by mathematical argument that the only imperfect procedures offering an a-priori guarantee of fairness in relation to all non-merit trait are lotteries or degenerate ones (i.e., everyone or no one is convicted). To provide a more intuitive point of view, we exploit an adjustment of the well-known ROC space, in order to represent all possible procedures in our model by a schematic diagram. The argument seems to be equally valid for all human procedures, provided they are imperfect. This clearly includes algorithmic decision-making, including decisions based on statistical predictions, since in practice all statistical models are error prone.
翻译:我们认为,不完善的刑法程序不可能是集团公平,如果“集团公平”意味着确保对所有无辜被告的无罪开释或定罪机会的相同机会,而不论其道德上的任意性特征如何。我们从数学角度表明,只有完美程序(不涉及错误)、非决定性程序或堕落程序(人人或无人被定罪)才能在一般情况下保障集团公平。根据最近的一项提案,我们采纳了群体公平的定义,要求条件平等的个人在统计上应该拥有获得优缺点的相同机会,这种机会在统计上独立于不计为优点的任何特征。我们用数学来解释,所有非荣誉特征方面提供优先公平保障的唯一不完美的程序是彩色或堕落程序(即每个人或任何人均被定罪)。为了提供更直观的观点,我们利用众所周知的RC空间的调整,以便用示意图来代表我们模型中的所有可能程序。我们的论点似乎同样有效,因为所有非荣誉特性都以其价值为准。我们用数学来解释,所有非荣誉特性的特征都提供一种最优的保证,即彩色或堕落(即每个人或无人被定罪 ) 。为了更直观的观点,我们利用众所周知的统计模型,我们所知道的ROC空间的调整,从而在模型中代表所有可能的程序。这个模型中代表了我们模型中的所有程序。 包括了所有统计上的精确性决定。