Feminist AI: Can We Expect Our AI Systems to Become Feminist?

Abstract

The rise of AI-based systems has been accompanied by the belief that these systems are impartial and do not suffer from the biases that humans and older technologies express. It becomes evident, however, that gender and racial biases exist in some AI algorithms. The question is where the bias is rooted—in the training dataset or in the algorithm? Is it a linguistic issue or a broader sociological current? Works in feminist philosophy of technology and behavioral economics reveal the gender bias in AI technologies as a multi-faceted phenomenon, and the linguistic explanation as too narrow. The next step moves from the linguistic aspects to the relational ones, with postphenomenology. One of the analytical tools of this theory is the “I-technology-world” formula that models our relations with technologies, and through them—with the world. Realizing that AI technologies give rise to new types of relations in which the technology has an “enhanced technological intentionality”, a new formula is suggested: “I-algorithm-dataset.” In the third part of the article, four types of solutions to the gender bias in AI are reviewed: ignoring any reference to gender, revealing the considerations that led the algorithm to decide, designing algorithms that are not biased, or lastly, involving humans in the process. In order to avoid gender bias, we can recall a feminist basic understanding—visibility matters. Users and developers should be aware of the possibility of gender and racial biases, and try to avoid them, bypass them, or exterminates them altogether.

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Notes

  1. 1.

    Previous generations of AI that were based on pre-programmed models were criticized for encoded gender bias into the software – see for example (Suchman 1994); (Adam 1998).

  2. 2.

    The effect of gender-biased translation algorithms continues and intensifies as such algorithms keep producing more biased texts, which are in turn fed into the algorithm as new training data (Zou and Schiebinger 2018).

  3. 3.

    This is the logic of Generative Adversarial Networks (GAN) structures, where one algorithm provides feedback to the other.

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Correspondence to Galit Wellner.

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Wellner, G., Rothman, T. Feminist AI: Can We Expect Our AI Systems to Become Feminist?. Philos. Technol. 33, 191–205 (2020). https://doi.org/10.1007/s13347-019-00352-z

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Keywords

  • Feminist philosophy of technology
  • Postphenomenology
  • Behavioral economics
  • Bias
  • Dataset