A Human-Centered Approach to Algorithmic Services: Considerations for Fair and Motivating Smart Community Service Management that Allocates Donations to Non-Profit Organizations

By Min Kyung Lee, Ji Tae Kim, Leah Lizarondo. 2017

91 citations recorded by the source corpus.

Algorithms are increasingly being incorporated into diverse services that orchestrate multiple stakeholders' needs and interests. How can we design these algorithmic services to make decisions that are not only efficient, but also fair and motivating? We take a human-centered approach to identify and address challenges in building human-centered algorithmic services. We are in the process of building an allocation algorithm for 412 Food Rescue, an organization that matches food donations with non-profit organizations. As part of this ongoing project, we conducted interviews with multiple stakeholders in the service-organization staff, donors, volunteers, recipient non-profits and their clients, and everyday citizens-in order to understand how the allocation algorithm, interfaces, and surrounding work practices should be designed. The findings suggest that we need to understand and account for varying fairness notions held by stakeholders; consider people, contexts, and interfaces for algorithms to work fairly in the real world; and preserve meaningfulness and social interaction in automation in order to build fair and motivating algorithmic services.

Subfields: Information Systems, Safety Research, Sociology and Political Science

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Citations in the corpus

How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness (2018, background): “Since the public is affected by these algorithmic systems, it is important to investigate public views of algorithmic fairness (Lee & Baykal, 2017; Lee et al., 2017; Lee, 2018; Binns et al., 2018; Woodruff et al., 2018).”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Fairness is also important when algorithms are used in social settings to support group decision making [109], or the allocation of distributed work [107].”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “While a growing body of HCI work argues strongly for encoding such human values into AI technology [46, 82], our findings suggest that those algorithmic design decisions should be made transparent to users, along with potential implications for those decisions.”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “fair division algorithms explored in [51,52]), and in other cases exploring broader patterns related to groups that have traditionally been marginalized in society.”
The ethics of algorithms: key problems and solutions (2020, unknown): “…for providing explanations has been a problem since the late 1990s (Tickle et al. 1998), but contemporary efforts can be categorised into two main approaches: subject-centric explanations and model-centric explanations (Doshi-Velez and Kim 2017; Lee et al. 2017; Baumer 2017; Buhmann et al. 2019).”
Algorithmic bias: review, synthesis, and future research directions (2021, background): “Accordingly, the technical components of algorithms including their internal processes (Grgić-Hlača et al., 2018) and outcomes (Lee et al., 2017; Saxena et al., 2019) as well as the explanation styles employed to make algorithmic processes and outcomes transparent and interpretable to users and…”