In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

https://journals.sagepub.com/doi/pdf/10.1177/2053951716679679

The ethics of algorithms: Mapping the debate:

This article reflects the kinds of situations and spaces where people and algorithms meet. In what situations do people become aware of algorithms? How do they experience and make sense of these algorithms, given their often hidden and invisible nature? To what extent does an awareness of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself. Examining how algorithms make people feel, then, seems crucial if we want to understand their social power.

The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms

The widespread implementation of algorithmic technologies in organizations prompts questions about how algorithms may reshape organizational control. We use Edwards’ (1979) perspective of “conteste...

Algorithms at Work: The New Contested Terrain of Control

Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users' experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms' existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm's existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends' decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm's presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

"I always assumed that I wasn't really that close to [her]": Reasoning about Invisible Algorithms in News Feeds

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many recognize the necessity to incorporate explainability features in AI systems, how to address real-world user needs for understanding AI remains an open question. By interviewing 20 UX and design practitioners working on various AI products, we seek to identify gaps between the current XAI algorithmic work and practices to create explainable AI products. To do so, we develop an algorithm-informed XAI question bank in which user needs for explainability are represented as prototypical questions users might ask about the AI, and use it as a study probe. Our work contributes insights into the design space of XAI, informs efforts to support design practices in this space, and identifies opportunities for future XAI work. We also provide an extended XAI question bank and discuss how it can be used for creating user-centered XAI.

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Every day automated algorithms make decisions that can amplify the power of businesses and governments. Yet as algorithms come to regulate more aspects of our lives, the contours of their power can remain difficult to grasp. This paper studies the notion of algorithmic accountability reporting as a mechanism for elucidating and articulating the power structures, biases, and influences that computational artifacts exercise in society. A framework for algorithmic power based on autonomous decision-making is proffered and motivates specific questions about algorithmic influence. Five cases of algorithmic accountability reporting involving the use of reverse engineering methods in journalism are then studied and analyzed to provide insight into the method and its application in a journalism context. The applicability of transparency policies for algorithms is discussed alongside challenges to implementing algorithmic accountability as a broadly viable investigative method.

Nicholas Diakopoulos

Papers

Algorithmic Accountability: Journalistic investigation of computational power structures