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

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.

[1]  Eli Pariser,et al.  The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think , 2012 .

[2]  Lynn Dombrowski,et al.  The labor practices of service mediation: a study of the work practices of food assistance outreach , 2012, CHI.

[3]  J. Hisnanick In the age of the smart machine: The future of work and power , 1989 .

[4]  Rebecca E. Grinter,et al.  Designing for Transient Use: A Human-in-the-loop Translation Platform for Refugees , 2016, CHI.

[5]  Ban Al-Ani,et al.  Bridging between organizations and the public: volunteer coordinators' uneasy relationship with social computing , 2012, CHI.

[6]  Janet Davis,et al.  Laying the foundations for public participation and value advocacy: interaction design for a large scale urban simulation , 2008, DG.O.

[7]  Arne Willy Dahl,et al.  Implementation in practice , 2012 .

[8]  Loren G. Terveen,et al.  Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets , 2015, CSCW.

[9]  Tony Hope,et al.  An Inquiry into the Principles of Needs‐Based Allocation of Health Care , 2009, Bioethics.

[10]  Kara M. Kockelman,et al.  THE ELECTRIC VEHICLE CHARGING STATION LOCATION PROBLEM: A PARKING-BASED ASSIGNMENT METHOD FOR SEATTLE , 2013 .

[11]  Helen Nissenbaum,et al.  Bias in computer systems , 1996, TOIS.

[12]  Latanya Sweeney,et al.  Discrimination in online ad delivery , 2013, CACM.

[13]  Lynn Dombrowski,et al.  It takes a network to get dinner: designing location-based systems to address local food needs , 2013, UbiComp.

[14]  Patrick R. McMullen,et al.  Ant colony optimization techniques for the vehicle routing problem , 2004, Adv. Eng. Informatics.

[15]  Matthias Scheutz,et al.  Sacrifice One For the Good of Many? People Apply Different Moral Norms to Human and Robot Agents , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[16]  R. Bales A set of categories for the analysis of small group interaction. , 1950 .

[17]  Lynn Dombrowski Designing SocioTechnical Food Justice , 2015 .

[18]  Wendy Ju,et al.  From Trolley to Autonomous Vehicle: Perceptions of Responsibility and Moral Norms in Traffic Accidents with Self-Driving Cars , 2016 .

[19]  Batya Friedman,et al.  Human values and the design of computer technology , 1997 .

[20]  W. Keith Edwards,et al.  Publics in practice: ubiquitous computing at a shelter for homeless mothers , 2011, CHI.

[21]  James Konow,et al.  Which Is the Fairest One of All? A Positive Analysis of Justice Theories , 2003 .

[22]  M. Patton,et al.  Qualitative evaluation and research methods , 1992 .

[23]  G. Leventhal,et al.  The Distribution of Rewards and Resources in Groups and Organizations , 1976 .

[24]  Peter Stone,et al.  Why Lotteries Are Just , 2007 .

[25]  V. Murphy-Berman,et al.  Factors affecting allocation to needy and meritorious recipients: A cross-cultural comparison. , 1984 .

[26]  Jessica K. Miller,et al.  Value tensions in design: the value sensitive design, development, and appropriation of a corporation's groupware system , 2007, GROUP.

[27]  David Sweeney,et al.  Data-in-Place: Thinking through the Relations Between Data and Community , 2015, CHI.

[28]  Ta-Hui Yang,et al.  Strategic design of public bicycle sharing systems with service level constraints , 2011 .

[29]  Karrie Karahalios,et al.  Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms , 2014 .

[30]  Shoshana Zuboff,et al.  In the Age of the Smart Machine: The Future of Work and Power , 1989 .

[31]  Ryan Calo,et al.  There is a blind spot in AI research , 2016, Nature.

[32]  Jack Whalen,et al.  Expert systems versus systems for experts: computer-aided dispatch as a support system in real-world environments , 1995 .

[33]  Tawanna Dillahunt,et al.  The Promise of the Sharing Economy among Disadvantaged Communities , 2015, CHI.

[34]  Helen Nissenbaum,et al.  Shaping the Web: Why the Politics of Search Engines Matters , 2000, Inf. Soc..

[35]  Min Kyung Lee Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division , 2017, CSCW.

[36]  E. Walster,et al.  New Directions in Equity Research12 , 1976 .

[37]  N. Hoffart Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory , 2000 .

[38]  Laura A. Dabbish,et al.  Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers , 2015, CHI.

[39]  David Lazer,et al.  Measuring Price Discrimination and Steering on E-commerce Web Sites , 2014, Internet Measurement Conference.