AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers' experiences with these systems in practice. In this paper, we present findings from a series of interviews and contextual inquiries at a child welfare agency, to understand how they currently make AI-assisted child maltreatment screening decisions. Overall, we observe how workers' reliance upon the ADS is guided by (1) their knowledge of rich, contextual information beyond what the AI model captures, (2) their beliefs about the ADS's capabilities and limitations relative to their own, (3) organizational pressures and incentives around the use of the ADS, and (4) awareness of misalignments between algorithmic predictions and their own decision-making objectives. Drawing upon these findings, we discuss design implications towards supporting more effective human-AI decision-making.

Data-driven AI systems are increasingly used to augment human decision-making in complex, social contexts, such as social work or legal practice. Yet, most existing design knowledge regarding how to best support AI-augmented decision-making comes from studies in comparatively well-defned settings. In this paper, we present fndings from design interviews with 12 social workers who use an algorithmic decision support tool (ADS) to assist their day-to-day child maltreatment screening decisions. We generated a range of design concepts, each envisioning diferent ways of redesigning or augmenting the ADS interface. Overall, workers desired ways to understand the risk score and incorporate contextual knowledge, which move beyond existing notions of AI interpretability. Conversations around our design concepts also surfaced more fundamental concerns around the assumptions underlying statistical prediction, such as inference based on similar historical cases and statistical notions of uncertainty. Based on our fndings, we discuss how ADS may be better designed to support the roles of human decision-makers in social decision-making contexts. CCS CONCEPTS• Human-centered computing → Human Computer Interaction (HCI); Interactive system and tools.

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The human proteome is replete with short linear motifs (SLiMs) of four to six residues that are critical for protein-protein interactions, yet the importance of the sequence surrounding such motifs is underexplored. We devised a proteomic screen to examine the influence of SLiM sequence context on protein-protein interactions. Focusing on the EVH1 domain of human ENAH, an actin regulator that is highly expressed in invasive cancers, we screened 36-residue proteome-derived peptides and discovered new interaction partners of ENAH and diverse mechanisms by which context influences binding. A pocket on the ENAH EVH1 domain that has diverged from other Ena/VASP paralogs recognizes extended SLiMs and favors motif-flanking proline residues. Many high-affinity ENAH binders that contain two proline-rich SLiMs use a noncanonical site on the EVH1 domain for binding and display a thermodynamic signature consistent with the two-motif chain engaging a single domain. We also found that photoreceptor cilium actin regulator (PCARE) uses an extended 23-residue region to obtain a higher affinity than any known ENAH EVH1-binding motif. Our screen provides a way to uncover the effects of proteomic context on motif-mediated binding, revealing diverse mechanisms of control over EVH1 interactions and establishing that SLiMs can’t be fully understood outside of their native context.

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