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Cite as:Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao and Nigel Shadbolt (2018) 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions. ACM Conference on Human Factors in Computing Systems (CHI'18), April 21–26, Montreal, Canada. doi: 10.1145/3173574.3173951Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

Mainstream PIM tools capture only a portion of the information that people need to manage. Many information scraps seem to exist that don't make their way into these tools, instead being relegated to sticky notes, text files, and other makeshift storage, or simply being lost. In an effort to understand the role of these information scraps, the underlying needs they reflect, and the way PIM tools must be modified to support those needs, we created List-it, a micronote tool for quick and simple capture of information scraps. In this article, we analyze the notes and interaction logs of 420 volunteer users of List-it over a two-year period of study (August 2008-August 2010). We contextualize our analysis with results of two surveys and an e-mail interview we conducted in October 2009. We find that people are drawn to List-it by the ease and speed of note capture and by the ability to record scraps with arbitrary content that blends or completely escapes the types and roles imposed by our rigid PIM tools. Notes are taken to serve a variety of needs -- reminding, reference, journaling/activity logging, brainstorming, and to indefinitely archive information of sentimental or personal value. Finally, while people differ considerably in the ways they keep information, our findings suggest such differences can be described as a combination of four distinct strategies, enriching the Filer/Piler distinction identified for classic document management.

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this paper, we describe a next-generation personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their time-varying states and activities. Combined with other information sources on the web, including the user’s online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate’s open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model.