A Conditional Defense of the Use of Algorithms in Criminal Sentencing.

The presence of predictive AI has steadily expanded into ever-increasing aspects of civil society. I aim to show that despite reasons for believing the use of such systems is currently problematic, these worries give no indication of their future potential. I argue that the absence of moral limits o...

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Publicado en:Techné: Research in Philosophy & Technology Vol. 27; no. 1; pp. 1 - 21
Autor principal: Daley, Ken
Formato: Artículo
Publicado: Philosophy Documentation Center 2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        affil: Southern Methodist University, Philosophy Department, Hyer Hall Rm 207, Dallas, TX 75205
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        Criminal sentencing
        Algorithms
        Maxima & minima
        Civil society
        Artificial intelligence
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          Criminal sentencing
          Algorithms
          Maxima & minima
          Civil society
          Artificial intelligence
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        artificial intelligence
        bias
        criminal sentencing
        opacity
        Predictive algorithms
        race
      ab: The presence of predictive AI has steadily expanded into ever-increasing aspects of civil society. I aim to show that despite reasons for believing the use of such systems is currently problematic, these worries give no indication of their future potential. I argue that the absence of moral limits on how we might manipulate automated systems, together with the likelihood that they are more easily manipulated in the relevant ways than humans, suggests that such systems will eventually outstrip the human ability to make accurate judgments and unbiased predictions. I begin with some reasonable justifications for the use of predictive AI. I then discuss two of the most significant reasons for believing the use of such systems is currently problematic before arguing that neither provides sufficient reason against such systems being superior in the future. In fact, there's reason to believe they can, in principle, be preferable to human decision-makers.
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