Closing the Socio–Technical Gap in AI: The Need for Measuring Practitioners’ Attitudes and Perceptions

This article discusses the need for artificial intelligence (AI) practitioners to shift their focus from a purely technical mindset toward a more human-centered approach. Technical experts are trained to consider the technical aspects of their work, which can cause them to overlook important socio–technical considerations and implications, resulting in a socio–technical gap in AI-based systems <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref>. Unhelpful practitioner cultures can lead to them “rejecting practices or downplaying the importance of values or the possible threats of ignoring them” <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref>. While efforts are being made to create ethical and more human-centered AI systems, there is a need for corresponding changes in the attitudes and perceptions of AI practitioners. Practitioners need to move away from a sole focus on compliance with responsible AI guidelines and regulations toward active reflection and empathy based on a true understanding of the profound effects their decisions can have on different stakeholders. However, one problematic barrier to beginning work on interventions that target practitioners’ mindsets and attitudes is the lack of a standardized method for evaluating or measuring the effectiveness of design interventions on their attitudes and perceptions. This article suggests the need for clearer metrics within the human–computer interaction (HCI) community for looking at practitioners’ attitudes toward socio–technical factors in AI design.